Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

Organizational Justice and Physiological Coronary Heart Disease Risk Factors in Japanese Employees: a Cross-Sectional Study.

PubMed

Inoue, Akiomi; Kawakami, Norito; Eguchi, Hisashi; Miyaki, Koichi; Tsutsumi, Akizumi

2015-12-01

Growing evidence has shown that lack of organizational justice (i.e., procedural justice and interactional justice) is associated with coronary heart disease (CHD) while biological mechanisms underlying this association have not yet been fully clarified. The purpose of the present study was to investigate the cross-sectional association of organizational justice with physiological CHD risk factors (i.e., blood pressure, high-density lipoprotein [HDL] cholesterol, low-density lipoprotein [LDL] cholesterol, and triglyceride) in Japanese employees. Overall, 3598 male and 901 female employees from two manufacturing companies in Japan completed self-administered questionnaires measuring organizational justice, demographic characteristics, and lifestyle factors. They completed health checkup, which included blood pressure and serum lipid measurements. Multiple logistic regression analyses and trend tests were conducted. Among male employees, multiple logistic regression analyses and trend tests showed significant associations of low procedural justice and low interactional justice with high triglyceride (defined as 150 mg/dL or greater) after adjusting for demographic characteristics and lifestyle factors. Among female employees, trend tests showed significant dose-response relationship between low interactional justice and high LDL cholesterol (defined as 140 mg/dL or greater) while multiple logistic regression analysis showed only marginally significant or insignificant odds ratio of high LDL cholesterol among the low interactional justice group. Neither procedural justice nor interactional justice was associated with blood pressure or HDL cholesterol. Organizational justice may be an important psychosocial factor associated with increased triglyceride at least among Japanese male employees.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Rate of revisions or conversion after bariatric surgery over 10 years in the state of New York.

PubMed

Altieri, Maria S; Yang, Jie; Nie, Lizhou; Blackstone, Robin; Spaniolas, Konstantinos; Pryor, Aurora

2018-04-01

A primary measure of the success of a procedure is the whether or not additional surgery may be necessary. Multi-institutional studies regarding the need for reoperation after bariatric surgery are scarce. The purpose of this study is to evaluate the rate of revisions/conversions (RC) after 3 common bariatric procedures over 10 years in the state of New York. University Hospital, involving a large database in New York State. The Statewide Planning and Research Cooperative System database was used to identify all patients undergoing laparoscopic adjustable gastric banding (LAGB), sleeve gastrectomy (SG), and Roux-en-Y gastric bypass (RYGB) between 2004 and 2010. Patients were followed for RC to other bariatric procedures for at least 4 years (up to 2014). Multivariable cox proportional hazard regression analysis was performed to identify risk factors for additional surgery after each common bariatric procedure. Multivariable logistic regression was used to check the factors associated with having ≥2 follow-up procedures. There were 40,994 bariatric procedures with 16,444 LAGB, 22,769 RYGB, and 1781 SG. Rate of RC was 26.0% for LAGB, 9.8% for SG, and 4.9% for RYGB. Multiple RC ( = />2) were more common for LAGB (5.7% for LAGB, .5% for RYGB, and .2% for LSG). Band revision/replacements required further procedures compared with patients who underwent conversion to RYGB/SG (939 compared with 48 procedures). Majority of RC were not performed at initial institution (68.2% of LAGB patients, 75.9% for RYGB, 63.7% of SG). Risk factors for multiple procedures included surgery type, as LAGB was more likely to have multiple RC. Reoperation was common for LAGB, but less common for RYGB (4.9%) and SG (9.8%). RC rate are almost twice after SG than after RYGB. LAGB had the highest rate (5.7%) of multiple reoperations. Conversion was the procedure of choice after a failed LAGB. Copyright © 2018 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Interaction Models for Functional Regression.

PubMed

Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab

2016-02-01

A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data.

The multiple imputation method: a case study involving secondary data analysis.

PubMed

Walani, Salimah R; Cleland, Charles M

2015-05-01

To illustrate with the example of a secondary data analysis study the use of the multiple imputation method to replace missing data. Most large public datasets have missing data, which need to be handled by researchers conducting secondary data analysis studies. Multiple imputation is a technique widely used to replace missing values while preserving the sample size and sampling variability of the data. The 2004 National Sample Survey of Registered Nurses. The authors created a model to impute missing values using the chained equation method. They used imputation diagnostics procedures and conducted regression analysis of imputed data to determine the differences between the log hourly wages of internationally educated and US-educated registered nurses. The authors used multiple imputation procedures to replace missing values in a large dataset with 29,059 observations. Five multiple imputed datasets were created. Imputation diagnostics using time series and density plots showed that imputation was successful. The authors also present an example of the use of multiple imputed datasets to conduct regression analysis to answer a substantive research question. Multiple imputation is a powerful technique for imputing missing values in large datasets while preserving the sample size and variance of the data. Even though the chained equation method involves complex statistical computations, recent innovations in software and computation have made it possible for researchers to conduct this technique on large datasets. The authors recommend nurse researchers use multiple imputation methods for handling missing data to improve the statistical power and external validity of their studies.

Occupational exposure to potentially infectious biological material in a dental teaching environment.

PubMed

Machado-Carvalhais, Helenaura P; Ramos-Jorge, Maria L; Auad, Sheyla M; Martins, Laura H P M; Paiva, Saul M; Pordeus, Isabela A

2008-10-01

The aims of this cross-sectional study were to determine the prevalence of occupational accidents with exposure to biological material among undergraduate students of dentistry and to estimate potential risk factors associated with exposure to blood. Data were collected through a self-administered questionnaire (86.4 percent return rate), which was completed by a sample of 286 undergraduate dental students (mean age 22.4 +/-2.4 years). The students were enrolled in the clinical component of the curriculum, which corresponds to the final six semesters of study. Descriptive, bivariate, simple logistic regression and multiple logistic regression (Forward Stepwise Procedure) analyses were performed. The level of statistical significance was set at 5 percent. Percutaneous and mucous exposures to potentially infectious biological material were reported by 102 individuals (35.6 percent); 26.8 percent reported the occurrence of multiple episodes of exposure. The logistic regression analyses revealed that the incomplete use of individual protection equipment (OR=3.7; 95 percent CI 1.5-9.3), disciplines where surgical procedures are carried out (OR=16.3; 95 percent CI 7.1-37.2), and handling sharp instruments (OR=4.4; 95 percent CI 2.1-9.1), more specifically, hollow-bore needles (OR=6.8; 95 percent CI 2.1-19.0), were independently associated with exposure to blood. Policies of reviewing the procedures during clinical practice are recommended in order to reduce occupational exposure.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Examination of Factors that Influence the Operation Income and Expenditure Balance Difference Rate of 20 Educational Foundation Universities.

PubMed

Nakajima, Hisato; Yano, Kouya; Nagasawa, Kaoko; Katou, Satoka; Yokota, Kuninobu

2017-01-01

The objective of this study is to examine the factors that influence the operation income and expenditure balance ratio of school corporations running university hospitals by multiple regression analysis. 1. We conducted cluster analysis of the financial ratio and classified the school corporations into those running colleges and universities.2. We conducted multiple regression analysis using the operation income and expenditure balance ratio of the colleges as the variables and the Diagnosis Procedure Combination data as the explaining variables.3. The predictive expression was used for multiple regression analysis. 1. The school corporations were divided into those running universities (7), colleges (20) and others. The medical income ratio and the debt ratio were high and the student payment ratio was low in the colleges.2. The numbers of emergency care hospitalizations, operations, radiation therapies, and ambulance conveyances, and the complexity index had a positive influence on the operation income and expenditure balance ratio. On the other hand, the number of general anesthesia procedures, the cover rate index, and the emergency care index had a negative influence.3. The predictive expression was as follows.Operation income and expenditure balance ratio = 0.027 × number of emergency care hospitalizations + 0.005 × number of operations + 0.019 × number of radiation therapies + 0.007 × number of ambulance conveyances - 0.003 × number of general anesthesia procedures + 648.344 × complexity index - 5877.210 × cover rate index - 2746.415 × emergency care index - 38.647Conclusion: In colleges, the number of emergency care hospitalizations, the number of operations, the number of radiation therapies, and the number of ambulance conveyances and the complexity index were factors for gaining ordinary profit.

The Impact of Religious Orientation in Conjugal Bereavement among Older Adults.

ERIC Educational Resources Information Center

Rosik, Christopher H.

1989-01-01

Explored relationship between religious commitment and adaptation to widowhood among 159 widowed elderly involved in southern Californian support groups. Grief, depression, and intrinsic-extrinsic religiousness were assessed and then analyzed via hierarchical multiple regression procedures. Higher extrinsicness (religion as a means to…

Overcoming multicollinearity in multiple regression using correlation coefficient

NASA Astrophysics Data System (ADS)

Zainodin, H. J.; Yap, S. J.

2013-09-01

Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

Applications of statistics to medical science, II overview of statistical procedures for general use.

PubMed

Watanabe, Hiroshi

2012-01-01

Procedures of statistical analysis are reviewed to provide an overview of applications of statistics for general use. Topics that are dealt with are inference on a population, comparison of two populations with respect to means and probabilities, and multiple comparisons. This study is the second part of series in which we survey medical statistics. Arguments related to statistical associations and regressions will be made in subsequent papers.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Estimating Interaction Effects With Incomplete Predictor Variables

PubMed Central

Enders, Craig K.; Baraldi, Amanda N.; Cham, Heining

2014-01-01

The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of probing interaction effects with maximum likelihood and multiple imputation. For both missing data handling techniques, we outline centering and transformation strategies that researchers can implement in popular software packages, and we use a series of real data analyses to illustrate these methods. Finally, we use computer simulations to evaluate the performance of the proposed techniques. PMID:24707955

Bayesian Scalar-on-Image Regression with Application to Association Between Intracranial DTI and Cognitive Outcomes

PubMed Central

Huang, Lei; Goldsmith, Jeff; Reiss, Philip T.; Reich, Daniel S.; Crainiceanu, Ciprian M.

2013-01-01

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian “scalar-on-image” regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients. PMID:23792220

Computer Simulation of Human Behavior: Assessment of Creativity.

ERIC Educational Resources Information Center

Greene, John F.

The major purpose of this study is to further the development of procedures which minimize current limitations of creativity instruments, thus yielding a reliable and functional means for assessing creativity. Computerized content analysis and multiple regression are employed to simulate the creativity ratings of trained judges. The computerized…

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1975-01-01

A model was developed for predicting the day 50 percent of the wheat crop is planted in North Dakota. This model incorporates location as an independent variable. The Julian date when 50 percent of the crop was planted for the nine divisions of North Dakota for seven years was regressed on the 49 variables through the step-down multiple regression procedure. This procedure begins with all of the independent variables and sequentially removes variables that are below a predetermined level of significance after each step. The prediction equation was tested on daily data. The accuracy of the model is considered satisfactory for finding the historic dates on which to initiate yield prediction model. Growth prediction models were also developed for spring wheat.

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

PubMed

Brown, Angus M

2006-04-01

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

Coping Strategies for Managing Acculturative Stress among Asian International Students

ERIC Educational Resources Information Center

Ra, Young-An; Trusty, Jerry

2015-01-01

This article examines the effects of specific coping strategies on managing acculturative stress and acculturation of Asian international students, based on a sample of 220 Asian international students in the U.S. The data were analyzed with hierarchical multiple regression using Baron and Kenny's (1986) mediation procedure. The results supported…

A SAS Interface for Bayesian Analysis with WinBUGS

ERIC Educational Resources Information Center

Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki

2008-01-01

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…

Modelling Student Satisfaction and Motivation in the Integrated Educational Environment: An Empirical study

ERIC Educational Resources Information Center

Stukalina, Yulia

2016-01-01

Purpose: The purpose of this paper is to explore some issues related to enhancing the quality of educational services provided by a university in the agenda of integrating quality assurance activities and strategic management procedures. Design/methodology/approach: Employing multiple regression analysis the author has examined some factors that…

Statistical procedures for determination and verification of minimum reporting levels for drinking water methods.

PubMed

Winslow, Stephen D; Pepich, Barry V; Martin, John J; Hallberg, George R; Munch, David J; Frebis, Christopher P; Hedrick, Elizabeth J; Krop, Richard A

2006-01-01

The United States Environmental Protection Agency's Office of Ground Water and Drinking Water has developed a single-laboratory quantitation procedure: the lowest concentration minimum reporting level (LCMRL). The LCMRL is the lowest true concentration for which future recovery is predicted to fall, with high confidence (99%), between 50% and 150%. The procedure takes into account precision and accuracy. Multiple concentration replicates are processed through the entire analytical method and the data are plotted as measured sample concentration (y-axis) versus true concentration (x-axis). If the data support an assumption of constant variance over the concentration range, an ordinary least-squares regression line is drawn; otherwise, a variance-weighted least-squares regression is used. Prediction interval lines of 99% confidence are drawn about the regression. At the points where the prediction interval lines intersect with data quality objective lines of 50% and 150% recovery, lines are dropped to the x-axis. The higher of the two values is the LCMRL. The LCMRL procedure is flexible because the data quality objectives (50-150%) and the prediction interval confidence (99%) can be varied to suit program needs. The LCMRL determination is performed during method development only. A simpler procedure for verification of data quality objectives at a given minimum reporting level (MRL) is also presented. The verification procedure requires a single set of seven samples taken through the entire method procedure. If the calculated prediction interval is contained within data quality recovery limits (50-150%), the laboratory performance at the MRL is verified.

Professional charges not reimbursed to dentists in the U.S.: evidence from Medical Expenditure Panel Survey, 1996.

PubMed

Chattopadhyay, A; Slade, G D; Caplan, D J

2009-12-01

This cross-sectional study examined professional charges not paid to dentists. This analysis used logistic regression in SUDAAN examining the 1996 MEPS data from 12,931 adults. Among people incurring dental care charges, 13.6% had more than $50 of unpaid charge (UC). The percapita UC was $53.30. Total UC was higher for highest income group [45.4% of total] compared to lowest income group [26.0%]. The percapita UC of $76.70 for low income group was significantly greater than for high income group ($47.80, P < 0.01). More Medicaid recipients (52% vs. non-recipients: 12%) incurred at least $50 in UC (P < 0.01). Adjusted odds of incurring UC were greater for those employed (OR = 1.3, 95% CI: 1.0-1.7), and for those with private insurance (OR: 1.5, CI: 1.3-1.9). Number of dental procedure types modified the association between Medicaid recipient and UC (OR = 13.6 for Medicaid recipients undergoing multiple procedure types; OR: 2.3 for Medicaid non-recipients with multiple procedure types; OR: 1.9 for Medicaid recipients receiving single dental procedure. Having private insurance, being unemployed and being Medicaid insured undergoing multiple procedure were strongest predictors of UC.

Predictive Factors of Atelectasis Following Endoscopic Resection.

PubMed

Choe, Jung Wan; Jung, Sung Woo; Song, Jong Kyu; Shim, Euddeum; Choo, Ji Yung; Kim, Seung Young; Hyun, Jong Jin; Koo, Ja Seol; Yim, Hyung Joon; Lee, Sang Woo

2016-01-01

Atelectasis is one of the pulmonary complications associated with anesthesia. Little is known about atelectasis following endoscopic procedures under deep sedation. This study evaluated the frequency, risk factors, and clinical course of atelectasis after endoscopic resection. A total of 349 patients who underwent endoscopic resection of the upper gastrointestinal tract at a single academic tertiary referral center from March 2010 to October 2013 were enrolled. Baseline characteristics and clinical data were retrospectively reviewed from medical records. To identify atelectasis, we compared the chest radiography taken before and after the endoscopic procedure. Among the 349 patients, 68 (19.5 %) had newly developed atelectasis following endoscopic resection. In univariate logistic regression analysis, atelectasis correlated significantly with high body mass index, smoking, diabetes mellitus, procedure duration, size of lesion, and total amount of propofol. In multiple logistic regression analysis, body mass index, procedure duration, and total propofol amount were risk factors for atelectasis following endoscopic procedures. Of the 68 patients with atelectasis, nine patients developed fever, and six patients displayed pneumonic infiltration. The others had no symptoms related to atelectasis. The incidence of radiographic atelectasis following endoscopic resection was nearly 20 %. Obesity, procedural time, and amount of propofol were the significant risk factors for atelectasis following endoscopic procedure. Most cases of the atelectasis resolved spontaneously with no sequelae.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Missing data treatments matter: an analysis of multiple imputation for anterior cervical discectomy and fusion procedures.

PubMed

Ondeck, Nathaniel T; Fu, Michael C; Skrip, Laura A; McLynn, Ryan P; Cui, Jonathan J; Basques, Bryce A; Albert, Todd J; Grauer, Jonathan N

2018-04-09

The presence of missing data is a limitation of large datasets, including the National Surgical Quality Improvement Program (NSQIP). In addressing this issue, most studies use complete case analysis, which excludes cases with missing data, thus potentially introducing selection bias. Multiple imputation, a statistically rigorous approach that approximates missing data and preserves sample size, may be an improvement over complete case analysis. The present study aims to evaluate the impact of using multiple imputation in comparison with complete case analysis for assessing the associations between preoperative laboratory values and adverse outcomes following anterior cervical discectomy and fusion (ACDF) procedures. This is a retrospective review of prospectively collected data. Patients undergoing one-level ACDF were identified in NSQIP 2012-2015. Perioperative adverse outcome variables assessed included the occurrence of any adverse event, severe adverse events, and hospital readmission. Missing preoperative albumin and hematocrit values were handled using complete case analysis and multiple imputation. These preoperative laboratory levels were then tested for associations with 30-day postoperative outcomes using logistic regression. A total of 11,999 patients were included. Of this cohort, 63.5% of patients had missing preoperative albumin and 9.9% had missing preoperative hematocrit. When using complete case analysis, only 4,311 patients were studied. The removed patients were significantly younger, healthier, of a common body mass index, and male. Logistic regression analysis failed to identify either preoperative hypoalbuminemia or preoperative anemia as significantly associated with adverse outcomes. When employing multiple imputation, all 11,999 patients were included. Preoperative hypoalbuminemia was significantly associated with the occurrence of any adverse event and severe adverse events. Preoperative anemia was significantly associated with the occurrence of any adverse event, severe adverse events, and hospital readmission. Multiple imputation is a rigorous statistical procedure that is being increasingly used to address missing values in large datasets. Using this technique for ACDF avoided the loss of cases that may have affected the representativeness and power of the study and led to different results than complete case analysis. Multiple imputation should be considered for future spine studies. Copyright © 2018 Elsevier Inc. All rights reserved.

Does the Type of Metal Instrumentation Affect the Risk of Surgical Site Infection in Pediatric Scoliosis Surgery?

PubMed

Wright, Margaret L; Skaggs, David L; Matsumoto, Hiroko; Woon, Regina P; Trocle, Ashley; Flynn, John M; Vitale, Michael G

2016-05-01

Retrospective cohort study. To determine the association of implant metal composition with the risk of surgical site infection (SSI) following pediatric spine surgery. SSI is a well-described complication following pediatric spine surgery. Many risk factors have been identified in the literature, but controversy remains regarding metal composition as a risk factor. This was a retrospective study of patients who underwent posterior spinal instrumentation procedures between January 1, 2006, and December 31, 2008, at three large children's hospitals for any etiology of scoliosis and had at least 1 year of postoperative follow-up. Procedures included posterior spinal fusion, growth-friendly instrumentation, and revision of spinal instrumentation. The Centers for Disease Control and Prevention definition of SSI was used. A chi-squared test was performed to determine the relationship between type of metal instrumentation and development of an SSI. The study included 874 patients who underwent 1,156 total procedures. Overall, 752 (65%) procedures used stainless steel instrumentation, 238 (21%) procedures used titanium instrumentation, and the remaining 166 (14%) procedures used cobalt chrome and titanium hybrid instrumentation. The overall risk of infection was 6.1% (70/1,156) per procedure, with 5.9% (44/752) for stainless steel, 6.7% (12/238) for titanium, and 6.0% (10/166) for cobalt chrome. The multiple regression analysis found no significant differences in the metal type used between patients with and without infection (p = .886) adjusting for etiology, instrumentation to pelvis, and type of procedures. When stratified based on etiology, the multiple regression analyses also found no significant difference in SSI between two metal type groups. This study found no difference in risk of infection with stainless steel, titanium, or cobalt chrome/titanium instrumentation and is adequately powered to detect a true difference in risk of SSI. Level II, prognostic. Copyright © 2016 Scoliosis Research Society. Published by Elsevier Inc. All rights reserved.

Using Regression Analysis To Determine If Faculty Salaries Are Overly Compressed. AIR 1997 Annual Forum Paper.

ERIC Educational Resources Information Center

Toutkoushian, Robert K.

This paper proposes a five-step process by which to analyze whether the salary ratio between junior and senior college faculty exhibits salary compression, a term used to describe an unusually small differential between faculty with different levels of experience. The procedure utilizes commonly used statistical techniques (multiple regression…

Two Readiness Measures As Predictors Of First- And Third-Grade Reading Achievement

ERIC Educational Resources Information Center

Randel, Mildred A.; And Others

1977-01-01

Multiple-regression procedures were used to assess effectiveness of the ABC Inventory and the Metropolitan Readiness Test (MRT) in predicting first- and third-grade reading achievement. MRT performance accounted for 11 percent of the variance in first-grade SRA reading scores. In predicting third-grade reading, the MRT accounted for 26 percent of…

Site conditions related to erosion on logging roads

Treesearch

R. M. Rice; J. D. McCashion

1985-01-01

Synopsis - Data collected from 299 road segments in northwestern California were used to develop and test a procedure for estimating and managing road-related erosion. Site conditions and the design of each segment were described by 30 variables. Equations developed using 149 of the road segments were tested on the other 150. The best multiple regression equation...

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

Relationship between affective determinants and achievement in science for seventeen-year-olds

NASA Astrophysics Data System (ADS)

Napier, John D.; Riley, Joseph P.

Data collected in the 1976-1977 NAEP survey of seventeen-year-olds was used to reanalyze the hypothesis that there are affective determinates of science achievement. Factor and item analysis procedures were used to examine affective and cognitive items from Booklet 4. Eight affective scales and one cognitive achievement scale were identified. Using stepwise multiple regression procedures, the four affective scales of Motivation, Anxiety, Student Choice, and Teacher Support were found to account for the majority of the correlation between the affective determinants and achievement.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

A Powerful Test for Comparing Multiple Regression Functions.

PubMed

Maity, Arnab

2012-09-01

In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Multiple calibrator measurements improve accuracy and stability estimates of automated assays.

PubMed

Akbas, Neval; Budd, Jeffrey R; Klee, George G

2016-01-01

The effects of combining multiple calibrations on assay accuracy (bias) and measurement of calibration stability were investigated for total triiodothyronine (TT3), vitamin B12 and luteinizing hormone (LH) using Beckman Coulter's Access 2 analyzer. Three calibration procedures (CC1, CC2 and CC3) combined 12, 34 and 56 calibrator measurements over 1, 2, and 3 days. Bias was calculated between target values and average measured value over 3 consecutive days after calibration. Using regression analysis of calibrator measurements versus measurement date, calibration stability was determined as the maximum number of days before a calibrator measurement exceeded 5% tolerance limits. Competitive assays (TT3, vitamin B12) had positive time regression slopes, while sandwich assay (LH) had a negative slope. Bias values for TT3 were -2.49%, 1.49%, and -0.50% using CC1, CC2 and CC3 respectively, with calibrator stability of 32, 20, and 30 days. Bias values for vitamin B12 were 2.44%, 0.91%, and -0.50%, with calibrator stability of 4, 9, and 12 days. Bias values for LH were 2.26%, 1.44% and -0.29% with calibrator stability of >43, 39 and 36 days. Measured stability was more consistent across calibration procedures using percent change rather than difference from target: 26 days for TT3, 12 days for B12 and 31 days for LH. Averaging over multiple calibrations produced smaller bias, consistent with improved accuracy. Time regression slopes in percent change were unaffected by number of calibration measurements but calibrator stability measured from the target value was highly affected by the calibrator value at time zero.

Multiple Regression as a Flexible Alternative to ANOVA in L2 Research

ERIC Educational Resources Information Center

Plonsky, Luke; Oswald, Frederick L.

2017-01-01

Second language (L2) research relies heavily and increasingly on ANOVA (analysis of variance)-based results as a means to advance theory and practice. This fact alone should merit some reflection on the utility and value of ANOVA. It is possible that we could use this procedure more appropriately and, as argued here, other analyses such as…

Medical Tourism for CCSVI Procedures in People with Multiple Sclerosis: An Observational Study.

PubMed

Metz, Luanne M; Greenfield, Jamie; Marrie, Ruth Ann; Jette, Nathalie; Blevins, Gregg; Svenson, Lawrence W; Alikhani, Katayoun; Wall, Winona; Dhaliwal, Raveena; Suchowersky, Oksana

2016-05-01

Many Canadians with multiple sclerosis (MS) have recently travelled internationally to have procedures for a putative condition called chronic cerebrospinal venous insufficiency (CCSVI). Here, we describe where and when they went and describe the baseline characteristics of persons with MS who participated in this non-evidence-based medical tourism for CCSVI procedures. We conducted a longitudinal observational study that used online questionnaires to collect patient-reported information about the safety, experiences, and outcomes following procedures for CCSVI. A convenience sample of all Albertans with MS was recruited between July 2011 and March 2013. In total, 868 individuals enrolled; 704 were included in this cross-sectional, baseline analysis. Of these, 128 (18.2%) participants retrospectively reported having procedures for CCSVI between April 2010 and September 2012. The proportion of participants reporting CCSVI procedures declined from 80 (62.5%) in 2010, to 40 (31.1%) in 2011, and 8 (6.3%) in 2012. In multivariable logistic regression analysis, CCSVI procedures were independently associated with longer disease duration, secondary progressive clinical course, and greater disability status. Although all types of people with MS pursued procedures for CCSVI, a major driver of participation was greater disability. This highlights that those with the greatest disability are the most vulnerable to unproven experimental procedures. Participation in CCSVI procedures waned over time possibly reflecting unmet expectations of treated patients, decreased media attention, or that individuals who wanted procedures had them soon after the CCSVI hypothesis was widely publicized.

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

PubMed

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

Building a Computer Program to Support Children, Parents, and Distraction during Healthcare Procedures

PubMed Central

McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W. Nick; Zimmerman, M. Bridget; Ersig, Anne L.

2012-01-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children’s responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, the Children, Parents and Distraction (CPaD), is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure. PMID:22805121

Bayesian function-on-function regression for multilevel functional data.

PubMed

Meyer, Mark J; Coull, Brent A; Versace, Francesco; Cinciripini, Paul; Morris, Jeffrey S

2015-09-01

Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images. © 2015, The International Biometric Society.

Modeling the energy content of combustible ship-scrapping waste at Alang-Sosiya, India, using multiple regression analysis.

PubMed

Reddy, M Srinivasa; Basha, Shaik; Joshi, H V; Sravan Kumar, V G; Jha, B; Ghosh, P K

2005-01-01

Alang-Sosiya is the largest ship-scrapping yard in the world, established in 1982. Every year an average of 171 ships having a mean weight of 2.10 x 10(6)(+/-7.82 x 10(5)) of light dead weight tonnage (LDT) being scrapped. Apart from scrapped metals, this yard generates a massive amount of combustible solid waste in the form of waste wood, plastic, insulation material, paper, glass wool, thermocol pieces (polyurethane foam material), sponge, oiled rope, cotton waste, rubber, etc. In this study multiple regression analysis was used to develop predictive models for energy content of combustible ship-scrapping solid wastes. The scope of work comprised qualitative and quantitative estimation of solid waste samples and performing a sequential selection procedure for isolating variables. Three regression models were developed to correlate the energy content (net calorific values (LHV)) with variables derived from material composition, proximate and ultimate analyses. The performance of these models for this particular waste complies well with the equations developed by other researchers (Dulong, Steuer, Scheurer-Kestner and Bento's) for estimating energy content of municipal solid waste.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Smoking and morphology of calcific deposits affect the outcome of needle aspiration of calcific deposits (NACD) for calcific tendinitis of the rotator cuff.

PubMed

Oudelaar, Bart W; Ooms, Edwin M; Huis In 't Veld, Rianne M H A; Schepers-Bok, Relinde; Vochteloo, Anne J

2015-11-01

Although NACD has proven to be an effective minimal invasive treatment for calcific tendinitis of the rotator cuff, little is known about the factors associated with treatment failure or the need for multiple procedures. Patients with symptomatic calcific tendinitis who were treated by NACD were evaluated in a retrospective cohort study. Demographic details, medical history, sonographic and radiographic findings were collected from patient files. Failure of NACD was defined as the persistence of symptoms after a follow-up of at least six months. NACD procedures performed within six months after a previous NACD procedure were considered repeated procedures. Multivariate logistic regression analysis was used to determine factors associated with treatment failure and multiple procedures. 431 patients (277 female; mean age 51.4±9.9 years) were included. Smoking (adjusted odds ratio (AOR): 1.7, 95% CI 1.0-2.7, p=0.04) was significantly associated with failure of NACD. Patients with Gärtner and Heyer (GH) type I calcific deposits were more likely to need multiple NACD procedures (AOR: 3.4, 95% CI 1.6-7.5, p<0.01) compared to patients with type III calcific deposits. Partial thickness rotator cuff tears were of no influence on the outcome of NACD or the number of treatments necessary. Smoking almost doubled the chance of failure of NACD and the presence of GH type I calcific deposits significantly increased the chance of multiple procedures. Partial thickness rotator cuff tears did not seem to affect the outcome of NACD. Based on the findings in this study, the importance of quitting smoking should be emphasized prior to NACD and partial thickness rotator cuff tears should not be a reason to withhold patients NACD. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Maximum margin multiple instance clustering with applications to image and text clustering.

PubMed

Zhang, Dan; Wang, Fei; Si, Luo; Li, Tao

2011-05-01

In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M(3)IC), for MIC. However, it is impractical to directly solve the optimization problem of M(3)IC. Therefore, M(3)IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency.

High school science enrollment of black students

NASA Astrophysics Data System (ADS)

Goggins, Ellen O.; Lindbeck, Joy S.

How can the high school science enrollment of black students be increased? School and home counseling and classroom procedures could benefit from variables identified as predictors of science enrollment. The problem in this study was to identify a set of variables which characterize science course enrollment by black secondary students. The population consisted of a subsample of 3963 black high school seniors from The High School and Beyond 1980 Base-Year Survey. Using multiple linear regression, backward regression, and correlation analyses, the US Census regions and grades mostly As and Bs in English were found to be significant predictors of the number of science courses scheduled by black seniors.

Combining abdominal and cosmetic breast surgery does not increase short-term complication rates: a comparison of each individual procedure and pretreatment risk stratification tool.

PubMed

Khavanin, Nima; Jordan, Sumanas W; Vieira, Brittany L; Hume, Keith M; Mlodinow, Alexei S; Simmons, Christopher J; Murphy, Robert X; Gutowski, Karol A; Kim, John Y S

2015-11-01

Combined abdominal and breast surgery presents a convenient and relatively cost-effective approach for accomplishing both procedures. This study is the largest to date assessing the safety of combined procedures, and it aims to develop a simple pretreatment risk stratification method for patients who desire a combined procedure. All women undergoing abdominoplasty, panniculectomy, augmentation mammaplasty, and/or mastopexy in the TOPS database were identified. Demographics and outcomes for combined procedures were compared to individual procedures using χ(2) and Student's t-tests. Multiple logistic regression provided adjusted odds ratios for the effect of a combined procedure on 30-day complications. Among combined procedures, a logistic regression model determined point values for pretreatment risk factors including diabetes (1 point), age over 53 (1), obesity (2), and 3+ ASA status (3), creating a 7-point pretreatment risk stratification tool. A total of 58,756 cases met inclusion criteria. Complication rates among combined procedures (9.40%) were greater than those of aesthetic breast surgery (2.66%; P < .001) but did not significantly differ from abdominal procedures (9.75%; P = .530). Nearly 77% of combined cases were classified as low-risk (0 points total) with a 9.78% complication rates. Medium-risk patients (1 to 3 points) had a 16.63% complication rate, and high-risk (4 to 7 points) 38.46%. Combining abdominal and breast procedures is safe in the majority of patients and does not increase 30-day complications rates. The risk stratification tool can continue to ensure favorable outcomes for patients who may desire a combined surgery. 4 Risk. © 2015 The American Society for Aesthetic Plastic Surgery, Inc. Reprints and permission: journals.permissions@oup.com.

Functional capacity following univentricular repair--midterm outcome.

PubMed

Sen, Supratim; Bandyopadhyay, Biswajit; Eriksson, Peter; Chattopadhyay, Amitabha

2012-01-01

Previous studies have seldom compared functional capacity in children following Fontan procedure alongside those with Glenn operation as destination therapy. We hypothesized that Fontan circulation enables better midterm submaximal exercise capacity as compared to Glenn physiology and evaluated this using the 6-minute walk test. Fifty-seven children aged 5-18 years with Glenn (44) or Fontan (13) operations were evaluated with standard 6-minute walk protocols. Baseline SpO(2) was significantly lower in Glenn patients younger than 10 years compared to Fontan counterparts and similar in the two groups in older children. Postexercise SpO(2) fell significantly in Glenn patients compared to the Fontan group. There was no statistically significant difference in baseline, postexercise, or postrecovery heart rates (HRs), or 6-minute walk distances in the two groups. Multiple regression analysis revealed lower resting HR, higher resting SpO(2) , and younger age at latest operation to be significant determinants of longer 6-minute walk distance. Multiple regression analysis also established that younger age at operation, higher resting SpO(2) , Fontan operation, lower resting HR, and lower postexercise HR were significant determinants of higher postexercise SpO(2) . Younger age at operation and exercise, lower resting HR and postexercise HR, higher resting SpO(2) and postexercise SpO(2) , and dominant ventricular morphology being left ventricular or indeterminate/mixed had significant association with better 6-minute work on multiple regression analysis. Lower resting HR had linear association with longer 6-minute walk distances in the Glenn patients. Compared to Glenn physiology, Fontan operation did not have better submaximal exercise capacity assessed by walk distance or work on multiple regression analysis. Lower resting HR, higher resting SpO(2) , and younger age at operation were factors uniformly associated with better submaximal exercise capacity. © 2012 Wiley Periodicals, Inc.

Model selection with multiple regression on distance matrices leads to incorrect inferences.

PubMed

Franckowiak, Ryan P; Panasci, Michael; Jarvis, Karl J; Acuña-Rodriguez, Ian S; Landguth, Erin L; Fortin, Marie-Josée; Wagner, Helene H

2017-01-01

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

PubMed

Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

2016-10-01

We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Endovascular treatment of chronic cerebro spinal venous insufficiency in patients with multiple sclerosis modifies circulating markers of endothelial dysfunction and coagulation activation: a prospective study.

PubMed

Napolitano, Mariasanta; Bruno, Aldo; Mastrangelo, Diego; De Vizia, Marcella; Bernardo, Benedetto; Rosa, Buonagura; De Lucia, Domenico

2014-10-01

We performed a monocentric observational prospective study to evaluate coagulation activation and endothelial dysfunction parameters in patients with multiple sclerosis undergoing endovascular treatment for cerebro-spinal-venous insufficiency. Between February 2011 and July 2012, 144 endovascular procedures in 110 patients with multiple sclerosis and chronical cerebro-spinal venous insufficiency were performed and they were prospectively analyzed. Each patient was included in the study according to previously published criteria, assessed by the investigators before enrollment. Endothelial dysfunction and coagulation activation parameters were determined before the procedure and during follow-up at 1, 3, 6, 9, 12, 15 and 18 months after treatment, respectively. After the endovascular procedure, patients were treated with standard therapies, with the addition of mesoglycan. Fifty-five percent of patients experienced a favorable outcome of multiple sclerosis within 1 month after treatment, 25% regressed in the following 3 months, 24.9% did not experience any benefit. In only 0.1% patients, acute recurrence was observed and it was treated with high-dose immunosuppressive therapy. No major complications were observed. Coagulation activation and endothelial dysfunction parameters were shown to be reduced at 1 month and stable up to 12-month follow-up, and they were furthermore associated with a good clinical outcome. Endovascular procedures performed by a qualified staff are well tolerated; they can be associated with other currently adopted treatments. Correlations between inflammation, coagulation activation and neurodegenerative disorders are here supported by the observed variations in plasma levels of markers of coagulation activation and endothelial dysfunction.

Abnormal dynamics of language in schizophrenia.

PubMed

Stephane, Massoud; Kuskowski, Michael; Gundel, Jeanette

2014-05-30

Language could be conceptualized as a dynamic system that includes multiple interactive levels (sub-lexical, lexical, sentence, and discourse) and components (phonology, semantics, and syntax). In schizophrenia, abnormalities are observed at all language elements (levels and components) but the dynamic between these elements remains unclear. We hypothesize that the dynamics between language elements in schizophrenia is abnormal and explore how this dynamic is altered. We, first, investigated language elements with comparable procedures in patients and healthy controls. Second, using measures of reaction time, we performed multiple linear regression analyses to evaluate the inter-relationships among language elements and the effect of group on these relationships. Patients significantly differed from controls with respect to sub-lexical/lexical, lexical/sentence, and sentence/discourse regression coefficients. The intercepts of the regression slopes increased in the same order above (from lower to higher levels) in patients but not in controls. Regression coefficients between syntax and both sentence level and discourse level semantics did not differentiate patients from controls. This study indicates that the dynamics between language elements is abnormal in schizophrenia. In patients, top-down flow of linguistic information might be reduced, and the relationship between phonology and semantics but not between syntax and semantics appears to be altered. Published by Elsevier Ireland Ltd.

Adjustment of geochemical background by robust multivariate statistics

USGS Publications Warehouse

Zhou, D.

1985-01-01

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

A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories

PubMed Central

Wang, Chia-Ning; Little, Roderick; Nan, Bin; Harlow, Siobán D.

2012-01-01

Summary We propose a regression-based hot deck multiple imputation method for gaps of missing data in longitudinal studies, where subjects experience a recurrent event process and a terminal event. Examples are repeated asthma episodes and death, or menstrual periods and the menopause, as in our motivating application. Research interest concerns the onset time of a marker event, defined by the recurrent-event process, or the duration from this marker event to the final event. Gaps in the recorded event history make it difficult to determine the onset time of the marker event, and hence, the duration from onset to the final event. Simple approaches such as jumping gap times or dropping cases with gaps have obvious limitations. We propose a procedure for imputing information in the gaps by substituting information in the gap from a matched individual with a completely recorded history in the corresponding interval. Predictive Mean Matching is used to incorporate information on longitudinal characteristics of the repeated process and the final event time. Multiple imputation is used to propagate imputation uncertainty. The procedure is applied to an important data set for assessing the timing and duration of the menopausal transition. The performance of the proposed method is assessed by a simulation study. PMID:21361886

Determining which phenotypes underlie a pleiotropic signal

PubMed Central

Majumdar, Arunabha; Haldar, Tanushree; Witte, John S.

2016-01-01

Discovering pleiotropic loci is important to understand the biological basis of seemingly distinct phenotypes. Most methods for assessing pleiotropy only test for the overall association between genetic variants and multiple phenotypes. To determine which specific traits are pleiotropic, we evaluate via simulation and application three different strategies. The first is model selection techniques based on the inverse regression of genotype on phenotypes. The second is a subset-based meta-analysis ASSET [Bhattacharjee et al., 2012], which provides an optimal subset of non-null traits. And the third is a modified Benjamini-Hochberg (B-H) procedure of controlling the expected false discovery rate [Benjamini and Hochberg, 1995] in the framework of phenome-wide association study. From our simulations we see that an inverse regression based approach MultiPhen [O’Reilly et al., 2012] is more powerful than ASSET for detecting overall pleiotropic association, except for when all the phenotypes are associated and have genetic effects in the same direction. For determining which specific traits are pleiotropic, the modified B-H procedure performs consistently better than the other two methods. The inverse regression based selection methods perform competitively with the modified B-H procedure only when the phenotypes are weakly correlated. The efficiency of ASSET is observed to lie below and in between the efficiency of the other two methods when the traits are weakly and strongly correlated, respectively. In our application to a large GWAS, we find that the modified B-H procedure also performs well, indicating that this may be an optimal approach for determining the traits underlying a pleiotropic signal. PMID:27238845

Crime prediction modeling

NASA Technical Reports Server (NTRS)

1971-01-01

A study of techniques for the prediction of crime in the City of Los Angeles was conducted. Alternative approaches to crime prediction (causal, quasicausal, associative, extrapolative, and pattern-recognition models) are discussed, as is the environment within which predictions were desired for the immediate application. The decision was made to use time series (extrapolative) models to produce the desired predictions. The characteristics of the data and the procedure used to choose equations for the extrapolations are discussed. The usefulness of different functional forms (constant, quadratic, and exponential forms) and of different parameter estimation techniques (multiple regression and multiple exponential smoothing) are compared, and the quality of the resultant predictions is assessed.

Comparative effectiveness analysis of anticoagulant strategies in a large observational database of percutaneous coronary interventions.

PubMed

Wise, Gregory R; Schwartz, Brian P; Dittoe, Nathaniel; Safar, Ammar; Sherman, Steven; Bowdy, Bruce; Hahn, Harvey S

2012-06-01

Percutaneous coronary intervention (PCI) is the most commonly used procedure for coronary revascularization. There are multiple adjuvant anticoagulation strategies available. In this era of cost containment, we performed a comparative effectiveness analysis of clinical outcomes and cost of the major anticoagulant strategies across all types of PCI procedures in a large observational database. A retrospective, comparative effectiveness analysis of the Premier observational database was conducted to determine the impact of anticoagulant treatment on outcomes. Multiple linear regression and logistic regression models were used to assess the association of initial antithrombotic treatment with outcomes while controlling for other factors. A total of 458,448 inpatient PCI procedures with known antithrombotic regimen from 299 hospitals between January 1, 2004 and March 31, 2008 were identified. Compared to patients treated with heparin plus glycoprotein IIb/IIIa inhibitor (GPI), bivalirudin was associated with a 41% relative risk reduction (RRR) for inpatient mortality, a 44% RRR for clinically apparent bleeding, and a 37% RRR for any transfusion. Furthermore, treatment with bivalirudin alone resulted in a cost savings of $976 per case. Similar results were seen between bivalirudin and heparin in all end-points. Combined use of both bivalirudin and GPI substantially attenuated the cost benefits demonstrated with bivalirudin alone. Bivalirudin use was associated with both improved clinical outcomes and decreased hospital costs in this large "real-world" database. To our knowledge, this study is the first to demonstrate the ideal comparative effectiveness end-point of both improved clinical outcomes with decreased costs in PCI. ©2012, Wiley Periodicals, Inc.

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Statistical summary of selected physical, chemical, and toxicity characteristics and estimates of annual constituent loads in urban stormwater, Maricopa County, Arizona

USGS Publications Warehouse

Fossum, Kenneth D.; O'Day, Christie M.; Wilson, Barbara J.; Monical, Jim E.

2001-01-01

Stormwater and streamflow in Maricopa County were monitored to (1) describe the physical, chemical, and toxicity characteristics of stormwater from areas having different land uses, (2) describe the physical, chemical, and toxicity characteristics of streamflow from areas that receive urban stormwater, and (3) estimate constituent loads in stormwater. Urban stormwater and streamflow had similar ranges in most constituent concentrations. The mean concentration of dissolved solids in urban stormwater was lower than in streamflow from the Salt River and Indian Bend Wash. Urban stormwater, however, had a greater chemical oxygen demand and higher concentrations of most nutrients. Mean seasonal loads and mean annual loads of 11 constituents and volumes of runoff were estimated for municipalities in the metropolitan Phoenix area, Arizona, by adjusting regional regression equations of loads. This adjustment procedure uses the original regional regression equation and additional explanatory variables that were not included in the original equation. The adjusted equations had standard errors that ranged from 161 to 196 percent. The large standard errors of the prediction result from the large variability of the constituent concentration data used in the regression analysis. Adjustment procedures produced unsatisfactory results for nine of the regressions?suspended solids, dissolved solids, total phosphorus, dissolved phosphorus, total recoverable cadmium, total recoverable copper, total recoverable lead, total recoverable zinc, and storm runoff. These equations had no consistent direction of bias and no other additional explanatory variables correlated with the observed loads. A stepwise-multiple regression or a three-variable regression (total storm rainfall, drainage area, and impervious area) and local data were used to develop local regression equations for these nine constituents. These equations had standard errors from 15 to 183 percent.

Exchange nailing for nonunion of diaphyseal fractures of the tibia: our results and an analysis of the risk factors for failure.

PubMed

Tsang, S T J; Mills, L A; Frantzias, J; Baren, J P; Keating, J F; Simpson, A H R W

2016-04-01

The aim of this study was to identify risk factors for the failure of exchange nailing in nonunion of tibial diaphyseal fractures. A cohort of 102 tibial diaphyseal nonunions in 101 patients with a mean age of 36.9 years (15 to 74) were treated between January 1992 and December 2012 by exchange nailing. Of which 33 (32%) were initially open injuries. The median time from primary fixation to exchange nailing was 6.5 months (interquartile range (IQR) 4.3 to 9.8 months). The main outcome measures were union, number of secondary fixation procedures required to achieve union and time to union. Univariate analysis and multiple regression were used to identify risk factors for failure to achieve union. Multiple causes for the primary nonunion were found for 28 (27%) tibiae, with infection present in 32 (31%). Six patients were lost to follow-up. Further surgical procedures were required in 35 (36%) nonunions. Other fixation modalities were required in five fractures. A single nail exchange procedure achieved union in 60/96 (63%) of all nonunions. Only 11 out of 31 infected nonunions (35.4%) healed after one exchange nail procedure. Up to five repeated exchange nailings, with or without bone grafting, ultimately achieved union in 89 (93%) fractures. The median time to union after exchange nailing was 8.7 months (IQR 5.7 to 14.0 months). Univariate analysis confirmed that an oligotrophic/atrophic pattern of nonunion (p = 0.002), a bone gap of 5 mm or more (p = 0.04) and infection (p < 0.001), were predictive for failure of exchange nailing Multiple regression analysis found that infection was the strongest predictor of failure (p < 0.001). Exchange nailing is an effective treatment for aseptic tibial diaphyseal nonunion. However, in the presence of severe infection with a highly resistant organism, or extensive sclerosis of the bone, other fixation modalities, such as Ilizarov treatment, should be considered. Exchange nailing is an effective treatment for aseptic tibial diaphyseal nonunion. ©2016 The British Editorial Society of Bone & Joint Surgery.

White light emitting diode as potential replacement of tungsten-halogen lamp for visible spectroscopy system: a case study in the measurement of mango qualities

NASA Astrophysics Data System (ADS)

Chiong, W. L.; Omar, A. F.

2017-07-01

Non-destructive technique based on visible (VIS) spectroscopy using light emitting diode (LED) as lighting was used for evaluation of the internal quality of mango fruit. The objective of this study was to investigate feasibility of white LED as lighting in spectroscopic instrumentation to predict the acidity and soluble solids content of intact Sala Mango. The reflectance spectra of the mango samples were obtained and measured in the visible range (400-700 nm) using VIS spectroscopy illuminated under different white LEDs and tungsten-halogen lamp (pro lamp). Regression models were developed by multiple linear regression to establish the relationship between spectra and internal quality. Direct calibration transfer procedure was then applied between master and slave lighting to check on the acidity prediction results after transfer. Determination of mango acidity under white LED lighting was successfully performed through VIS spectroscopy using multiple linear regression but otherwise for soluble solids content. Satisfactory results were obtained for calibration transfer between LEDs with different correlated colour temperature indicated this technique was successfully used in spectroscopy measurement between two similar light sources in prediction of internal quality of mango.

Financial Management and Control for Decision Making in Urban Local Bodies in India Using Statistical Techniques

NASA Astrophysics Data System (ADS)

Bhattacharyya, Sidhakam; Bandyopadhyay, Gautam

2010-10-01

The council of most of the Urban Local Bodies (ULBs) has a limited scope for decision making in the absence of appropriate financial control mechanism. The information about expected amount of own fund during a particular period is of great importance for decision making. Therefore, in this paper, efforts are being made to present set of findings and to establish a model of estimating receipts of own sources and payments thereof using multiple regression analysis. Data for sixty months from a reputed ULB in West Bengal have been considered for ascertaining the regression models. This can be used as a part of financial management and control procedure by the council to estimate the effect on own fund. In our study we have considered two models using multiple regression analysis. "Model I" comprises of total adjusted receipt as the dependent variable and selected individual receipts as the independent variables. Similarly "Model II" consists of total adjusted payments as the dependent variable and selected individual payments as independent variables. The resultant of Model I and Model II is the surplus or deficit effecting own fund. This may be applied for decision making purpose by the council.

Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

PubMed Central

Almedeij, Jaber

2012-01-01

Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

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

PubMed Central

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

2012-01-01

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

Constitutional basis of longevity in the cetacea: do the whales and the terrestrial mammals obey the same laws

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

Sacher, G.A.

1978-01-01

The maximum lifespans in captivity for terrestrial mammalian species can be estimated by means of a multiple linear regression of logarithm of lifespan (L) on the logarithm of adult brain weight (E) and body weight (S). This paper describes the application of regression formulas based on data from terrestrial mammals to the estimation of odontocete and mysticete lifespans. The regression formulas predict cetacean lifespans that are in accord with the data on maximum cetacean lifespans obtained in recent years by objective age determination procedures. More remarkable is the correct prediction by the regression formulas that the odontocete species have nearlymore » constant lifespans, almost independent of body weight over a 300:1 body weight range. This prediction is a consequence of the fact, remarkable in itself, that over this body weight range the Odontoceti have a brain:body allometric slope of 1/3, as compared to a slope of 2/3 for the Mammalia as a whole.« less

Leader-team congruence in power distance values and team effectiveness: the mediating role of procedural justice climate.

PubMed

Cole, Michael S; Carter, Min Z; Zhang, Zhen

2013-11-01

We examine the effect of (in)congruence between leaders' and teams' power distance values on team effectiveness. We hypothesize that the (in)congruence between these values would differentially predict team effectiveness, with procedural justice climate serving as a mediator. Using multisource data and polynomial regression, we found that similarities (and differences) between leaders' and their teams' power distance values can have consequential effects on teams' justice climate and, ultimately, their effectiveness (viz., team performance and team organizational citizenship behavior). We conclude that to fully understand the implications of power distance, one should consider the multiple perspectives of both leaders and team members. (c) 2013 APA, all rights reserved.

Serum Folate Shows an Inverse Association with Blood Pressure in a Cohort of Chinese Women of Childbearing Age: A Cross-Sectional Study

PubMed Central

Shen, Minxue; Tan, Hongzhuan; Zhou, Shujin; Retnakaran, Ravi; Smith, Graeme N.; Davidge, Sandra T.; Trasler, Jacquetta; Walker, Mark C.; Wen, Shi Wu

2016-01-01

Background It has been reported that higher folate intake from food and supplementation is associated with decreased blood pressure (BP). The association between serum folate concentration and BP has been examined in few studies. We aim to examine the association between serum folate and BP levels in a cohort of young Chinese women. Methods We used the baseline data from a pre-conception cohort of women of childbearing age in Liuyang, China, for this study. Demographic data were collected by structured interview. Serum folate concentration was measured by immunoassay, and homocysteine, blood glucose, triglyceride and total cholesterol were measured through standardized clinical procedures. Multiple linear regression and principal component regression model were applied in the analysis. Results A total of 1,532 healthy normotensive non-pregnant women were included in the final analysis. The mean concentration of serum folate was 7.5 ± 5.4 nmol/L and 55% of the women presented with folate deficiency (< 6.8 nmol/L). Multiple linear regression and principal component regression showed that serum folate levels were inversely associated with systolic and diastolic BP, after adjusting for demographic, anthropometric, and biochemical factors. Conclusions Serum folate is inversely associated with BP in non-pregnant women of childbearing age with high prevalence of folate deficiency. PMID:27182603

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

Prognostic Factors for Neurologic Outcome in Patients with Carotid Artery Stenting

PubMed Central

Hung, Chi-Sheng; Lin, Mao-Shin; Chen, Ying-Hsien; Huang, Ching-Chang; Li, Hung-Yuan; Kao, Hsien-Li

2016-01-01

Background Carotid artery stenting (CAS) is a valid treatment for patients with carotid artery stenosis. The long-term outcome and prognostic factors in Asian population after CAS are not clear. This study aimed to identify the prognostic factors among Asian patients who have undergone CAS. Methods We retrospectively analyzed 246 patients with CAS. Annual carotid duplex ultrasound was used to identify restenosis. Peri-procedural complications, restenosis, neurologic outcomes, and mortality were recorded. Cox regression analyses were used to identify prognostic factors. Results The mean follow-up time was 49.2 months. Procedural success was achieved in 237 patients (98.3%), and protection devices were used in 208 patients (84.5%). Within 30 days of CAS, 13 (4.3% per procedure) peri-procedural complications occurred. During the follow-up period, 24 (9.7%) patients developed restenosis, and 37 (15.0%) developed ischemic strokes. In a multiple logistic regression analysis, head and neck radiotherapy [hazard ratio (HR) = 9.9, 95% confidence interval (CI), 3.38-29.1, p < .001], stent diameter (HR = 0.72, 95% CI, 0.58-0.89, p = .003), and predilatation (HR = 3.08 95% CI, 1.21-7.81, p = .018) were independent predictors for restenosis. In Cox regression analysis, hypercholesterolemia (HR = 0.25, 95% CI, 0.07-0.94, p = .04), head and neck radiotherapy (HR = 6.2, 95% CI, 1.8-21.3, p = .004), and restenosis (HR = 3.6, 95% CI, 1.1-11.18, p = .04) were predictors for recurrent ipsilateral ischemic stroke. Conclusions CAS provides reliable long-term results in Asian patients with carotid stenosis. Restenosis is associated with an increased rate of recurrent stroke and should be monitored carefully following CAS. PMID:27122951

Optimal design application on the advanced aeroelastic rotor blade

NASA Technical Reports Server (NTRS)

Wei, F. S.; Jones, R.

1985-01-01

The vibration and performance optimization procedure using regression analysis was successfully applied to an advanced aeroelastic blade design study. The major advantage of this regression technique is that multiple optimizations can be performed to evaluate the effects of various objective functions and constraint functions. The data bases obtained from the rotorcraft flight simulation program C81 and Myklestad mode shape program are analytically determined as a function of each design variable. This approach has been verified for various blade radial ballast weight locations and blade planforms. This method can also be utilized to ascertain the effect of a particular cost function which is composed of several objective functions with different weighting factors for various mission requirements without any additional effort.

Reduction of interferences in graphite furnace atomic absorption spectrometry by multiple linear regression modelling

NASA Astrophysics Data System (ADS)

Grotti, Marco; Abelmoschi, Maria Luisa; Soggia, Francesco; Tiberiade, Christian; Frache, Roberto

2000-12-01

The multivariate effects of Na, K, Mg and Ca as nitrates on the electrothermal atomisation of manganese, cadmium and iron were studied by multiple linear regression modelling. Since the models proved to efficiently predict the effects of the considered matrix elements in a wide range of concentrations, they were applied to correct the interferences occurring in the determination of trace elements in seawater after pre-concentration of the analytes. In order to obtain a statistically significant number of samples, a large volume of the certified seawater reference materials CASS-3 and NASS-3 was treated with Chelex-100 resin; then, the chelating resin was separated from the solution, divided into several sub-samples, each of them was eluted with nitric acid and analysed by electrothermal atomic absorption spectrometry (for trace element determinations) and inductively coupled plasma optical emission spectrometry (for matrix element determinations). To minimise any other systematic error besides that due to matrix effects, accuracy of the pre-concentration step and contamination levels of the procedure were checked by inductively coupled plasma mass spectrometric measurements. Analytical results obtained by applying the multiple linear regression models were compared with those obtained with other calibration methods, such as external calibration using acid-based standards, external calibration using matrix-matched standards and the analyte addition technique. Empirical models proved to efficiently reduce interferences occurring in the analysis of real samples, allowing an improvement of accuracy better than for other calibration methods.

Using an innovative multiple regression procedure in a cancer population (Part 1): detecting and probing relationships of common interacting symptoms (pain, fatigue/weakness, sleep problems) as a strategy to discover influential symptom pairs and clusters

PubMed Central

Francoeur, Richard B

2015-01-01

Background The majority of patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. Improved methods are needed to detect and interpret interactions among symptoms or diesease markers to reveal influential pairs or clusters. In prior work, I developed and validated sequential residual centering (SRC), a method that improves the sensitivity of multiple regression to detect interactions among predictors, by conditioning for multicollinearity (shared variation) among interactions and component predictors. Materials and methods Using a hypothetical three-way interaction among pain, fatigue, and sleep to predict depressive affect, I derive and explain SRC multiple regression. Subsequently, I estimate raw and SRC multiple regressions using real data for these symptoms from 268 palliative radiation outpatients. Results Unlike raw regression, SRC reveals that the three-way interaction (pain × fatigue/weakness × sleep problems) is statistically significant. In follow-up analyses, the relationship between pain and depressive affect is aggravated (magnified) within two partial ranges: 1) complete-to-some control over fatigue/weakness when there is complete control over sleep problems (ie, a subset of the pain–fatigue/weakness symptom pair), and 2) no control over fatigue/weakness when there is some-to-no control over sleep problems (ie, a subset of the pain–fatigue/weakness–sleep problems symptom cluster). Otherwise, the relationship weakens (buffering) as control over fatigue/weakness or sleep problems diminishes. Conclusion By reducing the standard error, SRC unmasks a three-way interaction comprising a symptom pair and cluster. Low-to-moderate levels of the moderator variable for fatigue/weakness magnify the relationship between pain and depressive affect. However, when the comoderator variable for sleep problems accompanies fatigue/weakness, only frequent or unrelenting levels of both symptoms magnify the relationship. These findings suggest that a countervailing mechanism involving depressive affect could account for the effectiveness of a cognitive behavioral intervention to reduce the severity of a pain, fatigue, and sleep disturbance cluster in a previous randomized trial. PMID:25565865

Using an innovative multiple regression procedure in a cancer population (Part 1): detecting and probing relationships of common interacting symptoms (pain, fatigue/weakness, sleep problems) as a strategy to discover influential symptom pairs and clusters.

PubMed

Francoeur, Richard B

2015-01-01

The majority of patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. Improved methods are needed to detect and interpret interactions among symptoms or diesease markers to reveal influential pairs or clusters. In prior work, I developed and validated sequential residual centering (SRC), a method that improves the sensitivity of multiple regression to detect interactions among predictors, by conditioning for multicollinearity (shared variation) among interactions and component predictors. Using a hypothetical three-way interaction among pain, fatigue, and sleep to predict depressive affect, I derive and explain SRC multiple regression. Subsequently, I estimate raw and SRC multiple regressions using real data for these symptoms from 268 palliative radiation outpatients. Unlike raw regression, SRC reveals that the three-way interaction (pain × fatigue/weakness × sleep problems) is statistically significant. In follow-up analyses, the relationship between pain and depressive affect is aggravated (magnified) within two partial ranges: 1) complete-to-some control over fatigue/weakness when there is complete control over sleep problems (ie, a subset of the pain-fatigue/weakness symptom pair), and 2) no control over fatigue/weakness when there is some-to-no control over sleep problems (ie, a subset of the pain-fatigue/weakness-sleep problems symptom cluster). Otherwise, the relationship weakens (buffering) as control over fatigue/weakness or sleep problems diminishes. By reducing the standard error, SRC unmasks a three-way interaction comprising a symptom pair and cluster. Low-to-moderate levels of the moderator variable for fatigue/weakness magnify the relationship between pain and depressive affect. However, when the comoderator variable for sleep problems accompanies fatigue/weakness, only frequent or unrelenting levels of both symptoms magnify the relationship. These findings suggest that a countervailing mechanism involving depressive affect could account for the effectiveness of a cognitive behavioral intervention to reduce the severity of a pain, fatigue, and sleep disturbance cluster in a previous randomized trial.

Controlling Type I Error Rates in Assessing DIF for Logistic Regression Method Combined with SIBTEST Regression Correction Procedure and DIF-Free-Then-DIF Strategy

ERIC Educational Resources Information Center

Shih, Ching-Lin; Liu, Tien-Hsiang; Wang, Wen-Chung

2014-01-01

The simultaneous item bias test (SIBTEST) method regression procedure and the differential item functioning (DIF)-free-then-DIF strategy are applied to the logistic regression (LR) method simultaneously in this study. These procedures are used to adjust the effects of matching true score on observed score and to better control the Type I error…

Intradural Procedural Time to Assess Technical Difficulty of Superciliary Keyhole and Pterional Approaches for Unruptured Middle Cerebral Artery Aneurysms

PubMed Central

Choi, Yeon-Ju; Son, Wonsoo; Park, Ki-Su

2016-01-01

Objective This study used the intradural procedural time to assess the overall technical difficulty involved in surgically clipping an unruptured middle cerebral artery (MCA) aneurysm via a pterional or superciliary approach. The clinical and radiological variables affecting the intradural procedural time were investigated, and the intradural procedural time compared between a superciliary keyhole approach and a pterional approach. Methods During a 5.5-year period, patients with a single MCA aneurysm were enrolled in this retrospective study. The selection criteria for a superciliary keyhole approach included : 1) maximum diameter of the unruptured MCA aneurysm <15 mm, 2) neck diameter of the MCA aneurysm <10 mm, and 3) aneurysm location involving the sphenoidal or horizontal segment of MCA (M1) segment and MCA bifurcation, excluding aneurysms distal to the MCA genu. Meanwhile, the control comparison group included patients with the same selection criteria as for a superciliary approach, yet who preferred a pterional approach to avoid a postoperative facial wound or due to preoperative skin trouble in the supraorbital area. To determine the variables affecting the intradural procedural time, a multiple regression analysis was performed using such data as the patient age and gender, maximum aneurysm diameter, aneurysm neck diameter, and length of the pre-aneurysm M1 segment. In addition, the intradural procedural times were compared between the superciliary and pterional patient groups, along with the other variables. Results A total of 160 patients underwent a superciliary (n=124) or pterional (n=36) approach for an unruptured MCA aneurysm. In the multiple regression analysis, an increase in the diameter of the aneurysm neck (p<0.001) was identified as a statistically significant factor increasing the intradural procedural time. A Pearson correlation analysis also showed a positive correlation (r=0.340) between the neck diameter and the intradural procedural time. When comparing the superciliary and pterional groups, no statistically significant between-group difference was found in terms of the intradural procedural time reflecting the technical difficulty (mean±standard deviation : 29.8±13.0 min versus 27.7±9.6 min). Conclusion A superciliary keyhole approach can be a useful alternative to a pterional approach for an unruptured MCA aneurysm with a maximum diameter <15 mm and neck diameter <10 mm, representing no more of a technical challenge. For both surgical approaches, the technical difficulty increases along with the neck diameter of the MCA aneurysm. PMID:27847568

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

Examination of a Regressive Prompt-Delay Procedure for Improving Sight-Word Reading

ERIC Educational Resources Information Center

Daly, Edward J., III.; Hess, Polly M.; Sommerhalder, Mackenzie; Strong, Whitney; Johnsen, Mallory; O'Connor, Maureen A.; Young, Nicholas D.

2016-01-01

The current two-experiment study examined the effects of a regressive prompt-delay procedure on sight-word reading of four elementary school students. In contrast to traditional progressive prompt-delay procedures in which the latency of prompts is increased, the regressive prompt-delay latency is decreased over time. Data indicate that…

Reiki therapy for postoperative oral pain in pediatric patients: pilot data from a double-blind, randomized clinical trial.

PubMed

Kundu, Anjana; Lin, Yuting; Oron, Assaf P; Doorenbos, Ardith Z

2014-02-01

To examine the effects of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. This was a double-blind, randomized controlled study of children undergoing dental procedures. Participants were randomly assigned to receive either Reiki therapy or the control therapy (sham Reiki) preoperatively. Postoperative pain scores, opioid requirements, and side effects were assessed. Family members were also asked about perioperative care satisfaction. Multiple linear regressions were used for analysis. Thirty-eight children participated. The blinding procedure was successful. No statistically significant difference was observed between groups on all outcome measures. Our study provides a successful example of a blinding procedure for Reiki therapy among children in the perioperative period. This study does not support the effectiveness of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. Copyright © 2013 Elsevier Ltd. All rights reserved.

Reiki therapy for postoperative oral pain in pediatric patients: Pilot data from a double-blind, randomized clinical trial

PubMed Central

Kundu, Anjana; Lin, Yuting; Oron, Assaf P.; Doorenbos, Ardith Z.

2014-01-01

Purpose To examine the effects of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. Methods This was a double-blind, randomized controlled study of children undergoing dental procedures. Participants were randomly assigned to receive either Reiki therapy or the control therapy (sham Reiki) preoperatively. Postoperative pain scores, opioid requirements, and side effects were assessed. Family members were also asked about perioperative care satisfaction. Multiple linear regressions were used for analysis. Results Thirty-eight children participated. The blinding procedure was successful. No statistically significant difference was observed between groups on all outcome measures. Implications Our study provides a successful example of a blinding procedure for Reiki therapy among children in the perioperative period. This study does not support the effectiveness of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. PMID:24439640

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

PubMed

Brownstein, Naomi C; Cai, Jianwen; Slade, Gary D; Bair, Eric

2015-12-30

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study. Copyright © 2015 John Wiley & Sons, Ltd.

Diabetes and Risk of Surgical Site Infection: A systematic review and meta-analysis

PubMed Central

Kaye, Keith S.; Knott, Caitlin; Nguyen, Huong; Santarossa, Maressa; Evans, Richard; Bertran, Elizabeth; Jaber, Linda

2016-01-01

Objective To determine the independent association between diabetes and SSI across multiple surgical procedures. Design Systematic review and meta-analysis. Methods Studies indexed in PubMed published between December 1985 and through July 2015 were identified through the search terms “risk factors” or “glucose” and “surgical site infection”. A total of 3,631 abstracts were identified through the initial search terms. Full texts were reviewed for 522 articles. Of these, 94 articles met the criteria for inclusion. Standardized data collection forms were used to extract study-specific estimates for diabetes, blood glucose levels, and body mass index (BMI). Random-effects meta-analysis was used to generate pooled estimates and meta-regression was used to evaluate specific hypothesized sources of heterogeneity. Results The primary outcome was SSI, as defined by the Centers for Disease Control and Prevention surveillance criteria. The overall effect size for the association between diabetes and SSI was OR=1.53 (95% Predictive Interval 1.11, 2.12, I2: 57.2%). SSI class, study design, or patient BMI did not significantly impact study results in a meta-regression model. The association was higher for cardiac surgery 2.03 (95% Predictive Interval 1.13, 4.05) compared to surgeries of other types (p=0.001). Conclusion These results support the consideration of diabetes as an independent risk factor for SSIs for multiple surgical procedure types. Continued efforts are needed to improve surgical outcomes for diabetic patients. PMID:26503187

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

NASA Astrophysics Data System (ADS)

Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

2016-12-01

The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

A Semiparametric Change-Point Regression Model for Longitudinal Observations.

PubMed

Xing, Haipeng; Ying, Zhiliang

2012-12-01

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a semiparametric change-point regression model, in which the error process (stochastic component) is nonparametric and the baseline mean function (functional part) is completely unspecified, the observation times are allowed to be subject-specific, and the number, locations and magnitudes of change-points are unknown and need to be estimated. We further develop an estimation procedure which combines the recent advance in semiparametric analysis based on counting process argument and multiple change-points inference, and discuss its large sample properties, including consistency and asymptotic normality, under suitable regularity conditions. Simulation results show that the proposed methods work well under a variety of scenarios. An application to a real data set is also given.

Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

PubMed Central

Morris, Mark; Sellers, William I.

2015-01-01

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints. PMID:25780778

Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras.

PubMed

Peyer, Kathrin E; Morris, Mark; Sellers, William I

2015-01-01

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm

NASA Astrophysics Data System (ADS)

Creaco, E.; Berardi, L.; Sun, Siao; Giustolisi, O.; Savic, D.

2016-04-01

The growing availability of field data, from information and communication technologies (ICTs) in "smart" urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multiobjective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure.

Estimating magnitude and frequency of peak discharges for rural, unregulated, streams in West Virginia

USGS Publications Warehouse

Wiley, J.B.; Atkins, John T.; Tasker, Gary D.

2000-01-01

Multiple and simple least-squares regression models for the log10-transformed 100-year discharge with independent variables describing the basin characteristics (log10-transformed and untransformed) for 267 streamflow-gaging stations were evaluated, and the regression residuals were plotted as areal distributions that defined three regions of the State, designated East, North, and South. Exploratory data analysis procedures identified 31 gaging stations at which discharges are different than would be expected for West Virginia. Regional equations for the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year peak discharges were determined by generalized least-squares regression using data from 236 gaging stations. Log10-transformed drainage area was the most significant independent variable for all regions.Equations developed in this study are applicable only to rural, unregulated, streams within the boundaries of West Virginia. The accuracy of estimating equations is quantified by measuring the average prediction error (from 27.7 to 44.7 percent) and equivalent years of record (from 1.6 to 20.0 years).

Linkage mapping of beta 2 EEG waves via non-parametric regression.

PubMed

Ghosh, Saurabh; Begleiter, Henri; Porjesz, Bernice; Chorlian, David B; Edenberg, Howard J; Foroud, Tatiana; Goate, Alison; Reich, Theodore

2003-04-01

Parametric linkage methods for analyzing quantitative trait loci are sensitive to violations in trait distributional assumptions. Non-parametric methods are relatively more robust. In this article, we modify the non-parametric regression procedure proposed by Ghosh and Majumder [2000: Am J Hum Genet 66:1046-1061] to map Beta 2 EEG waves using genome-wide data generated in the COGA project. Significant linkage findings are obtained on chromosomes 1, 4, 5, and 15 with findings at multiple regions on chromosomes 4 and 15. We analyze the data both with and without incorporating alcoholism as a covariate. We also test for epistatic interactions between regions of the genome exhibiting significant linkage with the EEG phenotypes and find evidence of epistatic interactions between a region each on chromosome 1 and chromosome 4 with one region on chromosome 15. While regressing out the effect of alcoholism does not affect the linkage findings, the epistatic interactions become statistically insignificant. Copyright 2003 Wiley-Liss, Inc.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

Sample size estimation for alternating logistic regressions analysis of multilevel randomized community trials of under-age drinking.

PubMed

Reboussin, Beth A; Preisser, John S; Song, Eun-Young; Wolfson, Mark

2012-07-01

Under-age drinking is an enormous public health issue in the USA. Evidence that community level structures may impact on under-age drinking has led to a proliferation of efforts to change the environment surrounding the use of alcohol. Although the focus of these efforts is to reduce drinking by individual youths, environmental interventions are typically implemented at the community level with entire communities randomized to the same intervention condition. A distinct feature of these trials is the tendency of the behaviours of individuals residing in the same community to be more alike than that of others residing in different communities, which is herein called 'clustering'. Statistical analyses and sample size calculations must account for this clustering to avoid type I errors and to ensure an appropriately powered trial. Clustering itself may also be of scientific interest. We consider the alternating logistic regressions procedure within the population-averaged modelling framework to estimate the effect of a law enforcement intervention on the prevalence of under-age drinking behaviours while modelling the clustering at multiple levels, e.g. within communities and within neighbourhoods nested within communities, by using pairwise odds ratios. We then derive sample size formulae for estimating intervention effects when planning a post-test-only or repeated cross-sectional community-randomized trial using the alternating logistic regressions procedure.

Estimation of Flood-Frequency Discharges for Rural, Unregulated Streams in West Virginia

USGS Publications Warehouse

Wiley, Jeffrey B.; Atkins, John T.

2010-01-01

Flood-frequency discharges were determined for 290 streamgage stations having a minimum of 9 years of record in West Virginia and surrounding states through the 2006 or 2007 water year. No trend was determined in the annual peaks used to calculate the flood-frequency discharges. Multiple and simple least-squares regression equations for the 100-year (1-percent annual-occurrence probability) flood discharge with independent variables that describe the basin characteristics were developed for 290 streamgage stations in West Virginia and adjacent states. The regression residuals for the models were evaluated and used to define three regions of the State, designated as Eastern Panhandle, Central Mountains, and Western Plateaus. Exploratory data analysis procedures identified 44 streamgage stations that were excluded from the development of regression equations representative of rural, unregulated streams in West Virginia. Regional equations for the 1.1-, 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year flood discharges were determined by generalized least-squares regression using data from the remaining 246 streamgage stations. Drainage area was the only significant independent variable determined for all equations in all regions. Procedures developed to estimate flood-frequency discharges on ungaged streams were based on (1) regional equations and (2) drainage-area ratios between gaged and ungaged locations on the same stream. The procedures are applicable only to rural, unregulated streams within the boundaries of West Virginia that have drainage areas within the limits of the stations used to develop the regional equations (from 0.21 to 1,461 square miles in the Eastern Panhandle, from 0.10 to 1,619 square miles in the Central Mountains, and from 0.13 to 1,516 square miles in the Western Plateaus). The accuracy of the equations is quantified by measuring the average prediction error (from 21.7 to 56.3 percent) and equivalent years of record (from 2.0 to 70.9 years).

Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

NASA Astrophysics Data System (ADS)

Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

2014-12-01

This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust models in terms of selected predictors and coefficients, as well as of dispersion of the estimated probabilities around the mean value for each mapped pixel. The difference in the behaviour could be interpreted as the result of overfitting effects, which heavily affect decision tree classification more than logistic regression techniques.

Ultrasensitive surveillance of sensors and processes

DOEpatents

Wegerich, Stephan W.; Jarman, Kristin K.; Gross, Kenneth C.

2001-01-01

A method and apparatus for monitoring a source of data for determining an operating state of a working system. The method includes determining a sensor (or source of data) arrangement associated with monitoring the source of data for a system, activating a method for performing a sequential probability ratio test if the data source includes a single data (sensor) source, activating a second method for performing a regression sequential possibility ratio testing procedure if the arrangement includes a pair of sensors (data sources) with signals which are linearly or non-linearly related; activating a third method for performing a bounded angle ratio test procedure if the sensor arrangement includes multiple sensors and utilizing at least one of the first, second and third methods to accumulate sensor signals and determining the operating state of the system.

Ultrasensitive surveillance of sensors and processes

DOEpatents

Wegerich, Stephan W.; Jarman, Kristin K.; Gross, Kenneth C.

1999-01-01

A method and apparatus for monitoring a source of data for determining an operating state of a working system. The method includes determining a sensor (or source of data) arrangement associated with monitoring the source of data for a system, activating a method for performing a sequential probability ratio test if the data source includes a single data (sensor) source, activating a second method for performing a regression sequential possibility ratio testing procedure if the arrangement includes a pair of sensors (data sources) with signals which are linearly or non-linearly related; activating a third method for performing a bounded angle ratio test procedure if the sensor arrangement includes multiple sensors and utilizing at least one of the first, second and third methods to accumulate sensor signals and determining the operating state of the system.

Cosmetic surgery procedures as luxury goods: measuring price and demand in facial plastic surgery.

PubMed

Alsarraf, Ramsey; Alsarraf, Nicole W; Larrabee, Wayne F; Johnson, Calvin M

2002-01-01

To evaluate the relationship between cosmetic facial plastic surgery procedure price and demand, and to test the hypothesis that these procedures function as luxury goods in the marketplace, with an upward-sloping demand curve. Data were derived from a survey that was sent to every (N = 1727) active fellow, member, or associate of the American Academy of Facial Plastic and Reconstructive Surgery, assessing the costs and frequency of 4 common cosmetic facial plastic surgery procedures (face-lift, brow-lift, blepharoplasty, and rhinoplasty) for 1999 and 1989. An economic analysis was performed to assess the relationship of price and demand for these procedures. A significant association was found between increasing surgeons' fees and total charges for cosmetic facial plastic surgery procedures and increasing demand for these procedures, as measured by their annual frequency (P

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Risk factors for indications of intraoperative blood transfusion among patients undergoing surgical treatment for colorectal adenocarcinoma.

PubMed

Gonçalves, Iara; Linhares, Marcelo; Bordin, Jose; Matos, Delcio

2009-01-01

Identification of risk factors for requiring transfusions during surgery for colorectal cancer may lead to preventive actions or alternative measures, towards decreasing the use of blood components in these procedures, and also rationalization of resources use in hemotherapy services. This was a retrospective case-control study using data from 383 patients who were treated surgically for colorectal adenocarcinoma at 'Fundação Pio XII', in Barretos-SP, Brazil, between 1999 and 2003. To recognize significant risk factors for requiring intraoperative blood transfusion in colorectal cancer surgical procedures. Univariate analyses were performed using Fisher's exact test or the chi-squared test for dichotomous variables and Student's t test for continuous variables, followed by multivariate analysis using multiple logistic regression. In the univariate analyses, height (P = 0.06), glycemia (P = 0.05), previous abdominal or pelvic surgery (P = 0.031), abdominoperineal surgery (P<0.001), extended surgery (P<0.001) and intervention with radical intent (P<0.001) were considered significant. In the multivariate analysis using logistic regression, intervention with radical intent (OR = 10.249, P<0.001, 95% CI = 3.071-34.212) and abdominoperineal amputation (OR = 3.096, P = 0.04, 95% CI = 1.445-6.623) were considered to be independently significant. This investigation allows the conclusion that radical intervention and the abdominoperineal procedure in the surgical treatment of colorectal adenocarcinoma are risk factors for requiring intraoperative blood transfusion.

Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

NASA Astrophysics Data System (ADS)

Forghani, Ali; Peralta, Richard C.

2017-10-01

The study presents a procedure using solute transport and statistical models to evaluate the performance of aquifer storage and recovery (ASR) systems designed to earn additional water rights in freshwater aquifers. The recovery effectiveness (REN) index quantifies the performance of these ASR systems. REN is the proportion of the injected water that the same ASR well can recapture during subsequent extraction periods. To estimate REN for individual ASR wells, the presented procedure uses finely discretized groundwater flow and contaminant transport modeling. Then, the procedure uses multivariate adaptive regression splines (MARS) analysis to identify the significant variables affecting REN, and to identify the most recovery-effective wells. Achieving REN values close to 100% is the desire of the studied 14-well ASR system operator. This recovery is feasible for most of the ASR wells by extracting three times the injectate volume during the same year as injection. Most of the wells would achieve RENs below 75% if extracting merely the same volume as they injected. In other words, recovering almost all the same water molecules that are injected requires having a pre-existing water right to extract groundwater annually. MARS shows that REN most significantly correlates with groundwater flow velocity, or hydraulic conductivity and hydraulic gradient. MARS results also demonstrate that maximizing REN requires utilizing the wells located in areas with background Darcian groundwater velocities less than 0.03 m/d. The study also highlights the superiority of MARS over regular multiple linear regressions to identify the wells that can provide the maximum REN. This is the first reported application of MARS for evaluating performance of an ASR system in fresh water aquifers.

Sex differences in mortality in children undergoing congenital heart disease surgery: a United States population-based study.

PubMed

Marelli, Ariane; Gauvreau, Kimberlee; Landzberg, Mike; Jenkins, Kathy

2010-09-14

The changing demographics of the adult congenital heart disease (CHD) population requires an understanding of the factors that impact patient survival to adulthood. We sought to investigate sex differences in CHD surgical mortality in children. Children <18 years old hospitalized for CHD surgery were identified using the Kids' Inpatient Database in 2000, 2003, and 2006. Demographic, diagnostic, and procedural variables were grouped according to RACHS-1 (Risk Adjustment for Congenital Heart Surgery) method. Logistic regression was used to determine the odds ratio of death in females versus males adjusting for RACHS-1 risk category, age, prematurity, major noncardiac anomalies, and multiple procedures. Analyses were stratified by RACHS-1 risk categories and age. Of 33 848 hospitalizations for CHD surgery, 54.7% were in males. Males were more likely than females to have CHD surgery in infancy, high-risk CHD surgery, and multiple CHD procedures. Females had more major noncardiac structural anomalies and more low-risk procedures. However, the adjusted risk of in-hospital death was higher in females (odds ratio, 1.21; 95% confidence interval, 1.08 to 1.36) on account of the subgroup with high-risk surgeries who were <1 year of age (odds ratio, 1.39; 95% confidence interval, 1.16 to 1.67). In this large US population study, more male children underwent CHD surgery and had high-risk procedures. Female infants who had high-risk procedures were at higher risk for death, but this accounted for a small proportion of females and is therefore unlikely to have a major impact on the changing demographics in adults in CHD.

The Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

ERIC Educational Resources Information Center

Jaccard, James; And Others

1990-01-01

Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis

PubMed Central

Kim, Seong-Gil

2018-01-01

Background The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. Material/Methods This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. Results In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). Conclusions Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement. PMID:29760375

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis.

PubMed

Kim, Seong-Gil; Kim, Wan-Soo

2018-05-15

BACKGROUND The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. MATERIAL AND METHODS This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. RESULTS In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). CONCLUSIONS Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement.

Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

ERIC Educational Resources Information Center

Warne, Russell T.

2011-01-01

Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Comparison of bioavailable vanadium in alfalfa rhizosphere soil extracted by an improved BCR procedure and EDTA, HCl, and NaNO₃ single extractions in a pot experiment with V-Cd treatments.

PubMed

Yang, Jie; Teng, Yanguo; Zuo, Rui; Song, Liuting

2015-06-01

The BCR sequential extraction procedure was compared with EDTA, HCl, and NaNO3 single extractions for evaluating vanadium bioavailability in alfalfa rhizosphere soil. The amounts of vanadium extracted by these methods were in the following order: BCR (bioavailable V) > EDTA ≈ HCl > NaNO3. Both correlation analysis and stepwise regression were adopted to illustrate the extractable vanadium between different reagents. The correlation coefficients between extracted vanadium and the vanadium contents in alfalfa roots were R NaNO3 = 0.948, R HCl = 0.902, R EDTA = 0.816, and R bioavailable V = 0.819. The stepwise multiple regression equation of the NaNO3 extraction was the most significant at a 95 % confidence interval. The influence of pH, total organic carbon, and cadmium content of soil to vanadium bioavailability were not definite. In summary, both the BCR sequential extraction and the single extraction methods were valid approaches for predicting vanadium bioavailability in alfalfa rhizosphere soil, especially the single extractions.

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.; Roberts, J.W.

1990-01-01

Multiple-regression equations are presented for estimating flood-peak discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years at ungaged sites on rural, unregulated streams in Ohio. The average standard errors of prediction for the equations range from 33.4% to 41.4%. Peak discharge estimates determined by log-Pearson Type III analysis using data collected through the 1987 water year are reported for 275 streamflow-gaging stations. Ordinary least-squares multiple-regression techniques were used to divide the State into three regions and to identify a set of basin characteristics that help explain station-to- station variation in the log-Pearson estimates. Contributing drainage area, main-channel slope, and storage area were identified as suitable explanatory variables. Generalized least-square procedures, which include historical flow data and account for differences in the variance of flows at different gaging stations, spatial correlation among gaging station records, and variable lengths of station record were used to estimate the regression parameters. Weighted peak-discharge estimates computed as a function of the log-Pearson Type III and regression estimates are reported for each station. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site located on the same stream. Limitations and shortcomings cited in an earlier report on the magnitude and frequency of floods in Ohio are addressed in this study. Geographic bias is no longer evident for the Maumee River basin of northwestern Ohio. No bias is found to be associated with the forested-area characteristic for the range used in the regression analysis (0.0 to 99.0%), nor is this characteristic significant in explaining peak discharges. Surface-mined area likewise is not significant in explaining peak discharges, and the regression equations are not biased when applied to basins having approximately 30% or less surface-mined area. Analyses of residuals indicate that the equations tend to overestimate flood-peak discharges for basins having approximately 30% or more surface-mined area. (USGS)

On the use of regression analysis for the estimation of human biological age.

PubMed

Krøll, J; Saxtrup, O

2000-01-01

The present investigation compares three linear regression procedures for the definition of human biological age (bioage). As a model system for bioage definition is used the variations with age of blood hemoglobin (B-hemoglobin) in males in the age range 50-95 years. The bioage measures compared are: 1: P-bioage; defined from regression of chronological age on B-hemoglobin results. 2: AC-bioage; obtained by indirect regression, using in reverse the equation describing the regression of B-hemoglobin on age in a reference population. 3: BC-bioage; defined by orthogonal regression on the reference regression line of B-hemoglobin on age. It is demonstrated that the P-bioage measure gives an overestimation of the bioage in the younger and an underestimation in the older individuals. This 'regression to the mean' is avoided using the indirect regression procedures. Here the relatively low SD of the BC-bioage measure results from the inclusion of individual chronological age in the orthogonal regression procedure. Observations on male blood donors illustrates the variation of the AC- and BC-bioage measures in the individual.

Associations between body-mass index and surgery for rotator cuff tendinitis.

PubMed

Wendelboe, Aaron M; Hegmann, Kurt T; Gren, Lisa H; Alder, Stephen C; White, George L; Lyon, Joseph L

2004-04-01

Rotator cuff tendinopathy is a common entity. We hypothesized that obesity, because of biomechanical and systemic risk factors, increases the risks of rotator cuff tendinitis, tears, and related surgical procedures. A frequency-matched case-control study was conducted. Three hundred and eleven patients who were fifty-three to seventy-seven years old and who underwent rotator cuff repair, arthroscopy, and/or other repair of the shoulder in a large hospital from 1992 to 2000 were included in the study. These surgical procedures were used as proxies for the risk of rotator cuff tendinitis. These patients were age and frequency-matched to 933 controls, who were randomly drawn from a pool of 10,943 potential controls consisting of Utah state residents who were enrolled in a large cancer-screening trial. Age-adjusted odds ratios were calculated with use of the International Classification of Diseases, Ninth Revision procedural codes and body-mass-index groups. The data were stratified according to gender and age. Multiple linear regression analyses also were performed. There was an association between increasing body-mass index and shoulder repair surgery. The highest odds ratios for both men (odds ratio = 3.13; 95% confidence interval = 1.29 to 7.61) and women (odds ratio = 3.51; 95% confidence interval = 1.80 to 6.85) were for individuals with a body-mass index of > or =35.0 kg/m(2). Tests for trend also were highly significant for both men (p = 0.002) and women (p < or = 0.001). Multiple linear regression analysis also indicated a significant association between increasing body-mass index and shoulder surgery (beta = 1.57; 95% confidence interval = 0.97 to 2.17; p < or = 0.001). There is an association between obesity and shoulder repair surgery in men and women who are fifty-three to seventy-seven years of age. The results of the present study suggest that increasing body-mass index is a risk factor for rotator cuff tendinitis and related conditions.

Antecedents of organizational citizenship behavior among Iranian nurses: a multicenter study.

PubMed

Taghinezhad, Fakhredin; Safavi, Mahboobe; Raiesifar, Afsaneh; Yahyavi, Sayed Hossein

2015-10-08

Organizational citizenship behavior (OCB) improves efficiency and employees' participation and generally provides a good ambiance. This study was conducted to determine the role of job satisfaction (JS), organizational commitment (OC) and procedural justice (PJ) in explaining OCB among nurses working in fifteen educational-treatment centers in Tehran-Iran, to provide guidelines for health care managers' further understanding of how to encourage citizenship behavior among nurses. In this multi-center descriptive-correlational study 373 nurses were evaluated through a Multi-stage cluster sampling method after obtaining approval from the Ethics Committee of Islamic Azad University, Tehran Medical Branch and Tehran University of Medical Sciences Research Deputy. Nurses who signed the informed consent and holding a bachelor or master degree, having a minimum one year of job experience and not having organizational management position during the questionnaire distribution were included in the study. In order to collect data, Demographic questionnaire, Podsakoff et al. (Leadersh Q 1(2):107-142, 1990) OCB questionnaire, OC questionnaire, Aelterman et al. (Educ Stud 33(3):285-297, 2007) JS questionnaire and PJ questionnaire were used. These questionnaires were translated into Persian and content validity was confirmed by an expert group; their reliability was calculated by the internal consistency Cronbach alpha coefficient and it was satisfied. Data were analyzed by descriptive statistics, Comparative mean tests, correlation coefficient and multiple-regression in the SPSS software version 11. The general mean and all five aspects of OCB that ranked higher than 3 were evaluated in a "quite desired" state. The mean for perceived procedural justice, the general mean for JS and the mean of general grade for OC from the nurses' was in "quite desired" state. Finding from multiple regression indicated that OC and PJ exhibit about 19 % of OCB variance totally which is statistically significant (P < 0.01). JS had no significant impact on explaining OCB. OC was the strongest predictor of nurses' OCB followed by perceived procedural justice. So, improving these factors can initiate better citizenship behavior among nurses.

Factors affecting outcome of triceps motor branch transfer for isolated axillary nerve injury.

PubMed

Lee, Joo-Yup; Kircher, Michelle F; Spinner, Robert J; Bishop, Allen T; Shin, Alexander Y

2012-11-01

Triceps motor branch transfer has been used in upper brachial plexus injury and is potentially effective for isolated axillary nerve injury in lieu of sural nerve grafting. We evaluated the functional outcome of this procedure and determined factors that influenced the outcome. A retrospective chart review was performed of 21 patients (mean age, 38 y; range, 16-79 y) who underwent triceps motor branch transfer for the treatment of isolated axillary nerve injury. Deltoid muscle strength was evaluated using the modified British Medical Research Council grading at the last follow-up (mean, 21 mo; range, 12-41 mo). The following variables were analyzed to determine whether they affected the outcome of the nerve transfer: the age and sex of the patient, delay from injury to surgery, body mass index (BMI), severity of trauma, and presence of rotator cuff lesions. The Spearman correlation coefficient and multiple linear regression were performed for statistical analysis. The average Medical Research Council grade of deltoid muscle strength was 3.5 ± 1.1. Deltoid muscle strength correlated with the age of the patient, delay from injury to surgery, and BMI of the patient. Five patients failed to achieve more than M3 grade. Among them, 4 patients were older than 50 years and 1 was treated 14 months after injury. In the multiple linear regression model, the delay from injury to surgery, age of the patient, and BMI of the patient were the important factors, in that order, that affected the outcome of this procedure. Isolated axillary nerve injury can be treated successfully with triceps motor branch transfer. However, outstanding outcomes are not universal, with one fourth failing to achieve M3 strength. The outcome of this procedure is affected by the delay from injury to surgery and the age and BMI of the patient. Copyright © 2012 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Additivity of nonlinear biomass equations

Treesearch

Bernard R. Parresol

2001-01-01

Two procedures that guarantee the property of additivity among the components of tree biomass and total tree biomass utilizing nonlinear functions are developed. Procedure 1 is a simple combination approach, and procedure 2 is based on nonlinear joint-generalized regression (nonlinear seemingly unrelated regressions) with parameter restrictions. Statistical theory is...

Occult glove perforation during ophthalmic surgery.

PubMed Central

Apt, L; Miller, K M

1992-01-01

We examined the latex surgical gloves used by 56 primary surgeons in 454 ophthalmic surgical procedures performed over a 7-month period. Of five techniques used to detect pinholes, air inflation with water submersion and compression was found to be the most sensitive, yielding a 6.80% prevalence in control glove pairs and a 21.8% prevalence in postoperative study glove pairs, for a 15.0% incidence of surgically induced perforations (P = 0.000459). The lowest postoperative perforation rate was 11.4% for cataract and intraocular lens surgery, and the highest was 41.7% for oculoplastic procedures. Factors that correlated significantly with the presence of glove perforations as determined by multiple logistic regression analysis were oculoplastic and pediatric ophthalmology and strabismus surgical procedures, surgeon's status as a fellow in training, operating time, and glove size. The thumb and index finger of the nondominant hand contained the largest numbers of pinholes. These data suggest strategies for reducing the risk of cross-infection during ophthalmic surgery. PMID:1494836

Social determinants of cataract surgery utilization in south India. The Operations Research Group.

PubMed

Brilliant, G E; Lepkowski, J M; Zurita, B; Thulasiraj, R D

1991-04-01

A field trial was conducted to compare the effects of eight health education and economic incentive interventions on the awareness and acceptance of cataract surgery. Cataract screening and follow-up surgery were offered to more than 19,000 residents age 40 years and older in a probability sample of 90 villages in south India. Eight months after intervention, an evaluation was conducted to identify those in need of surgery who had been operated on. Two principal measures of program effectiveness are examined: awareness of cataract surgery and acceptance of the surgery. The type of intervention had a negligible effect on awareness of cataract surgery. A multiple logistic regression analysis revealed that individuals who were aware of surgery tended to be male, literate, and more affluent than those who were unaware of that option. Interventions that covered the complete costs of surgery had higher surgery acceptance rates. One health education strategy, house-to-house visits by a subject with aphakia, increased acceptance of the procedure more than others. In a multiple logistic regression analysis of acceptance rates, persons accepting surgery tended to be male; other factors were not important in explaining variation in acceptance rates.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

PubMed

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

Treesearch

John W. Edwards; Susan C. Loeb; David C. Guynn

1994-01-01

Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

Predicting the demand of physician workforce: an international model based on "crowd behaviors".

PubMed

Tsai, Tsuen-Chiuan; Eliasziw, Misha; Chen, Der-Fang

2012-03-26

Appropriateness of physician workforce greatly influences the quality of healthcare. When facing the crisis of physician shortages, the correction of manpower always takes an extended time period, and both the public and health personnel suffer. To calculate an appropriate number of Physician Density (PD) for a specific country, this study was designed to create a PD prediction model, based on health-related data from many countries. Twelve factors that could possibly impact physicians' demand were chosen, and data of these factors from 130 countries (by reviewing 195) were extracted. Multiple stepwise-linear regression was used to derive the PD prediction model, and a split-sample cross-validation procedure was performed to evaluate the generalizability of the results. Using data from 130 countries, with the consideration of the correlation between variables, and preventing multi-collinearity, seven out of the 12 predictor variables were selected for entry into the stepwise regression procedure. The final model was: PD = (5.014 - 0.128 × proportion under age 15 years + 0.034 × life expectancy)2, with R2 of 80.4%. Using the prediction equation, 70 countries had PDs with "negative discrepancy", while 58 had PDs with "positive discrepancy". This study provided a regression-based PD model to calculate a "norm" number of PD for a specific country. A large PD discrepancy in a country indicates the needs to examine physician's workloads and their well-being, the effectiveness/efficiency of medical care, the promotion of population health and the team resource management.

Assessing NARCCAP climate model effects using spatial confidence regions.

PubMed

French, Joshua P; McGinnis, Seth; Schwartzman, Armin

2017-01-01

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data

PubMed Central

Tian, Ting; McLachlan, Geoffrey J.; Dieters, Mark J.; Basford, Kaye E.

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances. PMID:26689369

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.

PubMed

Tian, Ting; McLachlan, Geoffrey J; Dieters, Mark J; Basford, Kaye E

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances.

Mechanisms of Optical Regression Following Corneal Laser Refractive Surgery: Epithelial and Stromal Responses

PubMed Central

MOSHIRFAR, Majid; DESAUTELS, Jordan D.; WALKER, Brian D.; MURRI, Michael S.; BIRDSONG, Orry C.; HOOPES, Phillip C. Sr

2018-01-01

Laser vision correction is a safe and effective method of reducing spectacle dependence. Photorefractive Keratectomy (PRK), Laser In Situ Keratomileusis (LASIK), and Small-Incision Lenticule Extraction (SMILE) can accurately correct myopia, hyperopia, and astigmatism. Although these procedures are nearing optimization in terms of their ability to produce a desired refractive target, the long term cellular responses of the cornea to these procedures can cause patients to regress from the their ideal postoperative refraction. In many cases, refractive regression requires follow up enhancement surgeries, presenting additional risks to patients. Although some risk factors underlying refractive regression have been identified, the exact mechanisms have not been elucidated. It is clear that cellular proliferation events are important mediators of optical regression. This review focused specifically on cellular changes to the corneal epithelium and stroma, which may influence postoperative visual regression following LASIK, PRK, and SMILE procedures. PMID:29644238

Fishing in the Amazonian forest: a gendered social network puzzle

PubMed Central

Díaz-Reviriego, I.; Fernández-Llamazares, Á.; Howard, P.L; Molina, JL; Reyes-García, V

2016-01-01

We employ social network analysis (SNA) to describe the structure of subsistence fishing social networks and to explore the relation between fishers’ emic perceptions of fishing expertise and their position in networks. Participant observation and quantitative methods were employed among the Tsimane’ Amerindians of the Bolivian Amazonia. A multiple regression quadratic assignment procedure was used to explore the extent to which gender, kinship, and age homophilies influence the formation of fishing networks. Logistic regressions were performed to determine the association between the fishers’ expertise, their socio-demographic identities, and network centrality. We found that fishing networks are gendered and that there is a positive association between fishers’ expertise and centrality in networks, an association that is more striking for women than for men. We propose that a social network perspective broadens understanding of the relations that shape the intracultural distribution of fishing expertise as well as natural resource access and use. PMID:28479670

Fishing in the Amazonian forest: a gendered social network puzzle.

PubMed

Díaz-Reviriego, I; Fernández-Llamazares, Á; Howard, P L; Molina, J L; Reyes-García, V

2017-01-01

We employ social network analysis (SNA) to describe the structure of subsistence fishing social networks and to explore the relation between fishers' emic perceptions of fishing expertise and their position in networks. Participant observation and quantitative methods were employed among the Tsimane' Amerindians of the Bolivian Amazonia. A multiple regression quadratic assignment procedure was used to explore the extent to which gender, kinship, and age homophilies influence the formation of fishing networks. Logistic regressions were performed to determine the association between the fishers' expertise, their socio-demographic identities, and network centrality. We found that fishing networks are gendered and that there is a positive association between fishers' expertise and centrality in networks, an association that is more striking for women than for men. We propose that a social network perspective broadens understanding of the relations that shape the intracultural distribution of fishing expertise as well as natural resource access and use.

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

Incidence and predictors of radial artery spasm during transradial coronary angiography and intervention.

PubMed

Jia, De-An; Zhou, Yu-Jie; Shi, Dong-Mei; Liu, Yu-Yang; Wang, Jian-Long; Liu, Xiao-Li; Wang, Zhi-Jian; Yang, Shi-Wei; Ge, Hai-Long; Hu, Bin; Yan, Zhen-Xian; Chen, Yi; Gao, Fei

2010-04-05

Radial artery spasm (RAS) is the most common complication in transradial coronary angiography and intervention. In this study, we designed to investigate the incidence of RAS during transradial procedures in Chinese, find out the independent predictors through multiple regression, and analyze the clinical effect of RAS during follow-up. Patients arranged to receive transradial coronary angiography and intervention were consecutively enrolled. The incidence of RAS was recorded. Univariate analysis was performed to find out the influence factors of RAS, and logistic regression analysis was performed to find out the independent predictors of RAS. The patients were asked to return 1 month later for the assessment of the radial access. The incidence of RAS was 7.8% (112/1427) in all the patients received transradial procedure. Univariate analysis indicates that young (P = 0.038), female (P = 0.026), small diameter of radial artery (P < 0.001), diabetes (P = 0.026), smoking (P = 0.019), moderate or severe pain during radial artery cannulation (P < 0.001), unsuccessful access at first attempt (P = 0.002), big sheath (P = 0.004), number of catheters (> 3) (P = 0.048), rapid baseline heart rate (P = 0.032) and long operation time (P = 0.021) were associated with RAS. Logistic regression showed that female (OR = 1.745, 95%CI: 1.148 - 3.846, P = 0.024), small radial artery diameter (OR = 4.028, 95%CI: 1.264 - 12.196, P = 0.008), diabetes (OR = 2.148, 95%CI: 1.579 - 7.458, P = 0.019) and unsuccessful access at first attempt (OR = 1.468, 95%CI: 1.212 - 2.591, P = 0.032) were independent predictors of RAS. Follow-up at (28 +/- 7) days after the procedure showed that, compared with non-spasm patients, the RAS patients had higher portion of pain (11.8% vs. 6.2%, P = 0.043). The occurrences of hematoma (7.3% vs. 5.6%, P = 0.518) and radial artery occlusion (3.6% vs. 2.6%, P = 0.534) were similar. The incidence of RAS during transradial coronary procedure was 7.8%. Logistic regression analysis showed that female, small radial artery diameter, diabetes and unsuccessful access at first attempt were the independent predictors of RAS.

A baseline-free procedure for transformation models under interval censorship.

PubMed

Gu, Ming Gao; Sun, Liuquan; Zuo, Guoxin

2005-12-01

An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework. The baseline-free procedure results a simplified and stable computation algorithm for some complicated and important semiparametric models, such as frailty models and heteroscedastic hazard/rank regression models, where the estimation procedures so far available involve estimation of the infinite dimensional baseline function. A detailed computational algorithm using Markov Chain Monte Carlo stochastic approximation is presented. The proposed procedure is demonstrated through extensive simulation studies, showing the validity of asymptotic consistency and normality. We also illustrate the procedure with a real data set from a study of breast cancer. A heuristic argument showing that the score function is a mean zero martingale is provided.

Using Data Mining for Wine Quality Assessment

NASA Astrophysics Data System (ADS)

Cortez, Paulo; Teixeira, Juliana; Cerdeira, António; Almeida, Fernando; Matos, Telmo; Reis, José

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.

Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure.

PubMed

Fieuws, Steffen; Willems, Guy; Larsen-Tangmose, Sara; Lynnerup, Niels; Boldsen, Jesper; Thevissen, Patrick

2016-03-01

When an estimate of age is needed, typically multiple indicators are present as found in skeletal or dental information. There exists a vast literature on approaches to estimate age from such multivariate data. Application of Bayes' rule has been proposed to overcome drawbacks of classical regression models but becomes less trivial as soon as the number of indicators increases. Each of the age indicators can lead to a different point estimate ("the most plausible value for age") and a prediction interval ("the range of possible values"). The major challenge in the combination of multiple indicators is not the calculation of a combined point estimate for age but the construction of an appropriate prediction interval. Ignoring the correlation between the age indicators results in intervals being too small. Boldsen et al. (2002) presented an ad-hoc procedure to construct an approximate confidence interval without the need to model the multivariate correlation structure between the indicators. The aim of the present paper is to bring under attention this pragmatic approach and to evaluate its performance in a practical setting. This is all the more needed since recent publications ignore the need for interval estimation. To illustrate and evaluate the method, Köhler et al. (1995) third molar scores are used to estimate the age in a dataset of 3200 male subjects in the juvenile age range.

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

PubMed

Fernandes, David Douglas Sousa; Gomes, Adriano A; Costa, Gean Bezerra da; Silva, Gildo William B da; Véras, Germano

2011-12-15

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant. Copyright © 2011 Elsevier B.V. All rights reserved.

Multiple Ordinal Regression by Maximizing the Sum of Margins

PubMed Central

Hamsici, Onur C.; Martinez, Aleix M.

2016-01-01

Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. PMID:26529784

Bon-EV: an improved multiple testing procedure for controlling false discovery rates.

PubMed

Li, Dongmei; Xie, Zidian; Zand, Martin; Fogg, Thomas; Dye, Timothy

2017-01-03

Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg's and Storey's q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey's q-value procedure has higher power and lower stability than Benjamini-Hochberg's procedure. To improve upon the stability of Storey's q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni's approach. Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey's q-value procedure and also result in better FDR control and higher stability than Storey's q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey's q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey's q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg's procedure. For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey's q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

A comparison of multiple imputation methods for incomplete longitudinal binary data.

PubMed

Yamaguchi, Yusuke; Misumi, Toshihiro; Maruo, Kazushi

2018-01-01

Longitudinal binary data are commonly encountered in clinical trials. Multiple imputation is an approach for getting a valid estimation of treatment effects under an assumption of missing at random mechanism. Although there are a variety of multiple imputation methods for the longitudinal binary data, a limited number of researches have reported on relative performances of the methods. Moreover, when focusing on the treatment effect throughout a period that has often been used in clinical evaluations of specific disease areas, no definite investigations comparing the methods have been available. We conducted an extensive simulation study to examine comparative performances of six multiple imputation methods available in the SAS MI procedure for longitudinal binary data, where two endpoints of responder rates at a specified time point and throughout a period were assessed. The simulation study suggested that results from naive approaches of a single imputation with non-responders and a complete case analysis could be very sensitive against missing data. The multiple imputation methods using a monotone method and a full conditional specification with a logistic regression imputation model were recommended for obtaining unbiased and robust estimations of the treatment effect. The methods were illustrated with data from a mental health research.

Detection of multiple perturbations in multi-omics biological networks.

PubMed

Griffin, Paula J; Zhang, Yuqing; Johnson, William Evan; Kolaczyk, Eric D

2018-05-17

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA). © 2018, The International Biometric Society.

Pain behaviors observed during six common procedures: results from Thunder Project II.

PubMed

Puntillo, Kathleen A; Morris, Ann B; Thompson, Carol L; Stanik-Hutt, Julie; White, Cheri A; Wild, Lorie R

2004-02-01

Patients frequently display behaviors during procedures that may be pain related. Clinicians often rely on the patient's demonstration of behaviors as a cue to presence of pain. The purpose of this study was to identify specific pain-related behaviors and factors that predict the degree of behavioral responses during the following procedures: turning, central venous catheter insertion, wound drain removal, wound care, tracheal suctioning, and femoral sheath removal. Prospective, descriptive study. Multiple units in 169 hospitals in United States, Canada, England, and Australia. A total of 5,957 adult patients who underwent one of the six procedures. None. A 30-item behavior observation tool was used to note patients' behaviors before and during a procedure. By comparing behaviors exhibited before and during the procedure as well as behaviors in those with and without procedural pain (as noted on a 0-10 numeric rating scale), we identified specific procedural pain behaviors: grimacing, rigidity, wincing, shutting of eyes, verbalization, moaning, and clenching of fists. On average, there were significantly more behaviors exhibited by patients with vs. without procedural pain (3.5 vs. 1.8 behaviors; t = 38.3, df = 5072.5; 95% confidence interval, 1.6-1.8). Patients with procedural pain were at least three times more likely to have increased behavioral responses than patients without procedural pain. A simultaneous regression model determined that 33% of the variance in amount of pain behaviors exhibited during a procedure was explained by three factors: degree of procedural pain intensity, degree of procedural distress, and undergoing the turning procedure. Because of the strong relationship between procedural pain and behavioral responses, clinicians can use behavioral responses of verbal and nonverbal patients to plan for, implement, and evaluate analgesic interventions.

Complications after procedures of photorefractive keratectomy

NASA Astrophysics Data System (ADS)

Gierek-Ciaciura, Stanislawa

1998-10-01

Purpose: The aim of this study was to investigate the saveness of the PRK procedures. Material and method: 151 eyes after PRK for correction of myopia and 112 after PRK for correction of myopic astigmatism were examined. All PRK procedures have been performed with an excimer laser manufactured by Aesculap Meditec. Results: Haze, regression, decentration infection and overcorrection were found. Conclusions: The most often complication is regression. Corneal inflammation in the early postoperative period may cause the regression or haze. The greater corrected refractive error the greater haze degree. Haze decreases with time.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

Modification of the Mantel-Haenszel and Logistic Regression DIF Procedures to Incorporate the SIBTEST Regression Correction

ERIC Educational Resources Information Center

DeMars, Christine E.

2009-01-01

The Mantel-Haenszel (MH) and logistic regression (LR) differential item functioning (DIF) procedures have inflated Type I error rates when there are large mean group differences, short tests, and large sample sizes.When there are large group differences in mean score, groups matched on the observed number-correct score differ on true score,…

Risk Factors for Proximal Junctional Kyphosis Associated With Dual-rod Growing-rod Surgery for Early-onset Scoliosis.

PubMed

Watanabe, Kota; Uno, Koki; Suzuki, Teppei; Kawakami, Noriaki; Tsuji, Taichi; Yanagida, Haruhisa; Ito, Manabu; Hirano, Toru; Yamazaki, Ken; Minami, Shohei; Taneichi, Hiroshi; Imagama, Shiro; Takeshita, Katsushi; Yamamoto, Takuya; Matsumoto, Morio

2016-10-01

A retrospective, multicenter study. To identify risk factors for proximal junctional kyphosis (PJK) when treating early-onset scoliosis (EOS) with dual-rod growing-rod (GR) procedure. The risk factors for PJK associated with GR treatment for EOS have not been adequately studied. We evaluated clinical and radiographic results from 88 patients with EOS who underwent dual-rod GR surgery in 12 spine centers in Japan. The mean age at the time of the initial surgery was 6.5±2.2 years (range, 1.5-9.8 y), and the mean follow-up period was 3.9±2.6 years (range, 2.0-12.0 y). Risk factors for PJK were analyzed by binomial multiple logistic regression analysis. The potential factors analyzed were sex, etiology, age, the number of rod-lengthening procedures, coronal and sagittal parameters on radiographs, the type of foundation (pedicle screws or hooks), the uppermost level of the proximal foundation, and the lowermost level of the distal foundation. PJK developed in 23 patients (26%); in 19 of these, the proximal foundation became dislodged following PJK. Binomial multiple logistic regression analysis identified the following significant independent risk factors for PJK: a lower instrumented vertebra at or cranial to L3 [odds ratio (OR), 3.32], a proximal thoracic scoliosis of ≥40 degrees (OR, 2.95), and a main thoracic kyphosis of ≥60 degrees (OR, 5.08). The significant independent risk factors for PJK during dual-rod GR treatment for EOS were a lower instrumented vertebra at or cranial to L3, a proximal thoracic scoliosis of ≥40 degrees, and a main thoracic kyphosis of ≥60 degrees.

Nationwide Multicenter Reference Interval Study for 28 Common Biochemical Analytes in China.

PubMed

Xia, Liangyu; Chen, Ming; Liu, Min; Tao, Zhihua; Li, Shijun; Wang, Liang; Cheng, Xinqi; Qin, Xuzhen; Han, Jianhua; Li, Pengchang; Hou, Li'an; Yu, Songlin; Ichihara, Kiyoshi; Qiu, Ling

2016-03-01

A nationwide multicenter study was conducted in the China to explore sources of variation of reference values and establish reference intervals for 28 common biochemical analytes, as a part of the International Federation of Clinical Chemistry and Laboratory Medicine, Committee on Reference Intervals and Decision Limits (IFCC/C-RIDL) global study on reference values. A total of 3148 apparently healthy volunteers were recruited in 6 cities covering a wide area in China. Blood samples were tested in 2 central laboratories using Beckman Coulter AU5800 chemistry analyzers. Certified reference materials and value-assigned serum panel were used for standardization of test results. Multiple regression analysis was performed to explore sources of variation. Need for partition of reference intervals was evaluated based on 3-level nested ANOVA. After secondary exclusion using the latent abnormal values exclusion method, reference intervals were derived by a parametric method using the modified Box-Cox formula. Test results of 20 analytes were made traceable to reference measurement procedures. By the ANOVA, significant sex-related and age-related differences were observed in 12 and 12 analytes, respectively. A small regional difference was observed in the results for albumin, glucose, and sodium. Multiple regression analysis revealed BMI-related changes in results of 9 analytes for man and 6 for woman. Reference intervals of 28 analytes were computed with 17 analytes partitioned by sex and/or age. In conclusion, reference intervals of 28 common chemistry analytes applicable to Chinese Han population were established by use of the latest methodology. Reference intervals of 20 analytes traceable to reference measurement procedures can be used as common reference intervals, whereas others can be used as the assay system-specific reference intervals in China.

Nationwide Multicenter Reference Interval Study for 28 Common Biochemical Analytes in China

PubMed Central

Xia, Liangyu; Chen, Ming; Liu, Min; Tao, Zhihua; Li, Shijun; Wang, Liang; Cheng, Xinqi; Qin, Xuzhen; Han, Jianhua; Li, Pengchang; Hou, Li’an; Yu, Songlin; Ichihara, Kiyoshi; Qiu, Ling

2016-01-01

Abstract A nationwide multicenter study was conducted in the China to explore sources of variation of reference values and establish reference intervals for 28 common biochemical analytes, as a part of the International Federation of Clinical Chemistry and Laboratory Medicine, Committee on Reference Intervals and Decision Limits (IFCC/C-RIDL) global study on reference values. A total of 3148 apparently healthy volunteers were recruited in 6 cities covering a wide area in China. Blood samples were tested in 2 central laboratories using Beckman Coulter AU5800 chemistry analyzers. Certified reference materials and value-assigned serum panel were used for standardization of test results. Multiple regression analysis was performed to explore sources of variation. Need for partition of reference intervals was evaluated based on 3-level nested ANOVA. After secondary exclusion using the latent abnormal values exclusion method, reference intervals were derived by a parametric method using the modified Box–Cox formula. Test results of 20 analytes were made traceable to reference measurement procedures. By the ANOVA, significant sex-related and age-related differences were observed in 12 and 12 analytes, respectively. A small regional difference was observed in the results for albumin, glucose, and sodium. Multiple regression analysis revealed BMI-related changes in results of 9 analytes for man and 6 for woman. Reference intervals of 28 analytes were computed with 17 analytes partitioned by sex and/or age. In conclusion, reference intervals of 28 common chemistry analytes applicable to Chinese Han population were established by use of the latest methodology. Reference intervals of 20 analytes traceable to reference measurement procedures can be used as common reference intervals, whereas others can be used as the assay system-specific reference intervals in China. PMID:26945390

The use of lipid-lowering therapy for secondary prevention in patients undergoing percutaneous coronary intervention

PubMed Central

Ma, Jessica M; Jackevicius, Cynthia A; Genus, Uchenwa; Dzavik, Vladimir

2006-01-01

BACKGROUND Recent literature suggests that lipid-lowering therapy may have an early beneficial effect among patients undergoing percutaneous coronary intervention (PCI) because the therapy decreases cardiac mortality, morbidity and possibly restenosis. OBJECTIVE The primary objective of the present study was to determine the proportion of PCI patients receiving lipid-lowering therapy at a large, tertiary-care referral centre. METHODS Patients undergoing a first PCI between August 2000 and August 2002 with corresponding inpatient medication information were included in the study. Patient demographics, procedural variables, and lipid-lowering and other evidence-based cardiac medication data were collected. A multiple logistical regression model was constructed to evaluate the factors associated with the use of lipid-lowering therapy. RESULTS Of the 3254 cases included in the analyses, 52% were elective, 44% were urgent or salvage, and 4% were emergent. The mean patient age was 63 years, and 73% of patients were male. Over 76% of patients were receiving lipid-lowering therapy at the time of PCI. Patient use of other medications was as follows: acetylsalicylic acid in 96%, beta-blocker in 80% and angiotensin-converting enzyme inhibitor in 59%. In the multiple regression analysis, variables significantly associated with lipid-lowering therapy use included hypercholesterolemia, beta-blocker use, angiotensin-converting enzyme inhibitor use, case urgency, prior coronary artery bypass graft surgery, age and sex. CONCLUSION Lipid-lowering therapy use rates exceeded those previously reported in the literature. Women and patients undergoing elective procedures appear to be treated less often with lipid-lowering therapy. There remains an opportunity to further optimize use in this high-risk cohort at time of PCI. PMID:16639478

HIV health center affiliation networks of black men who have sex with men: disentangling fragmented patterns of HIV prevention service utilization.

PubMed

Schneider, John A; Walsh, Tim; Cornwell, Benjamin; Ostrow, David; Michaels, Stuart; Laumann, Edward O

2012-08-01

In the United States, black men who have sex with men (BMSM) are at highest risk for HIV infection and are at high risk for limited health service utilization. We describe HIV health center (HHC) affiliation network patterns and their potential determinants among urban BMSM. The Men's Assessment of Social and Risk Network instrument was used to elicit HHC utilization, as reported by study respondents recruited through respondent-driven sampling. In 2010, 204 BMSM were systematically recruited from diverse venues in Chicago, IL. A 2-mode data set was constructed that included study participants and 9 diverse HHCs. Associations between individual-level characteristics and HHC utilization were analyzed using Multiple Regression Quadratic Assignment Procedure. Visualization analyses included computation of HHC centrality and faction membership. High utilization of HHCs (45.9%-70.3%) was evident among BMSM, 44.4% who were HIV infected. Multiple Regression Quadratic Assignment Procedure revealed that age, social network size, and HIV status were associated with HHC affiliation patterns (coeff., 0.13-0.27; all P < 0.05). With the exception of one HHC, HHCs offering HIV prevention services to HIV-infected participants occupied peripheral positions within the network of health centers. High-risk HIV-uninfected participants affiliated most with an HHC that offers only treatment services. Subcategories of BMSM in this sample affiliated with HHCs that may not provide appropriate HIV prevention services. Using 2-mode data, public health authorities may be better able to match prevention services to BMSM need; in particular, HIV prevention services for high-risk HIV-uninfected men and HIV "prevention for positives" services for HIV-infected men.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

An examination of predictive variables toward graduation of minority students in science at a selected urban university

NASA Astrophysics Data System (ADS)

Hunter, Evelyn M. Irving

1998-12-01

The purpose of this study was to examine the relationship and predictive power of the variables gender, high school GPA, class rank, SAT scores, ACT scores, and socioeconomic status on the graduation rates of minority college students majoring in the sciences at a selected urban university. Data was examined on these variables as they related to minority students majoring in science. The population consisted of 101 minority college students who had majored in the sciences from 1986 to 1996 at an urban university in the southwestern region of Texas. A non-probability sampling procedure was used in this study. The non-probability sampling procedure in this investigation was incidental sampling technique. A profile sheet was developed to record the information regarding the variables. The composite scores from SAT and ACT testing were used in the study. The dichotomous variables gender and socioeconomic status were dummy coded for analysis. For the gender variable, zero (0) indicated male, and one (1) indicated female. Additionally, zero (0) indicated high SES, and one (1) indicated low SES. Two parametric procedures were used to analyze the data in this investigation. They were the multiple correlation and multiple regression procedures. Multiple correlation is a statistical technique that indicates the relationship between one variable and a combination of two other variables. The variables socioeconomic status and GPA were found to contribute significantly to the graduation rates of minority students majoring in all sciences when combined with chemistry (Hypotheses Two and Four). These variables accounted for 7% and 15% of the respective variance in the graduation rates of minority students in the sciences and in chemistry. Hypotheses One and Three, the predictor variables gender, high school GPA, SAT Total Scores, class rank, and socioeconomic status did not contribute significantly to the graduation rates of minority students in biology and pharmacy.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Lipreading in the prelingually deaf: what makes a skilled speechreader?

PubMed

Rodríguez Ortiz, Isabel de los Reyes

2008-11-01

Lipreading proficiency was investigated in a group of hearing-impaired people, all of them knowing Spanish Sign Language (SSL). The aim of this study was to establish the relationships between lipreading and some other variables (gender, intelligence, audiological variables, participants' education, parents' education, communication practices, intelligibility, use of SSL). The 32 participants were between 14 and 47 years of age. They all had sensorineural hearing losses (from severe to profound). The lipreading procedures comprised identification of words in isolation. The words selected for presentation in isolation were spoken by the same talker. Identification of words required participants to select their responses from set of four pictures appropriately labelled. Lipreading was significantly correlated with intelligence and intelligibility. Multiple regression analyses were used to obtain a prediction equation for the lipreading measures. As a result of this procedure, it is concluded that proficient deaf lipreaders are more intelligent and their oral speech was more comprehensible for others.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

[Factors affecting the DAPI fluorescence direct count in the tidal river sediment].

PubMed

Chen, Chen; Huang, Shan; Wu, Qun-he; Li, Rui-yi; Zhang, Ren-duo

2010-08-01

The factors affecting the DAPI (4', 6-diamidino-2-phenylidole) fluorescence direct count in the tidal river sediment were examined. Sediment samples were collected from the Guangzhou section of the Pearl River. Besides sediment texture and organic matter, an improved staining procedure and the involved parameters were analyzed. Results showed that the procedure with the sediment with 2000 fold dilution and ultrasonic water bath for 10 min, and with a final DAPI concentration of 10 microg x mL(-1) and staining time for more than 30 min produced the optimum results of DAPI direct count in the sediment. The total bacterial number was correlated to the proportion of the non-nucleoid-containing cells to the total bacterial number (r = 0.587, p = 0.004). The organic matter content also correlated to the ration. The clay content had a strong correlation with the organic matter, through which the clay content also affected the ratio. A multiple regression analysis between the ration versus the organic matter, the total bacterial number, and the clay content showed that the regression equation fit the measure values satisfactorily (r = 0.694). These results indicated that the above factors needed to be considered in the applications of the DAPI fluorescence direct counting method to the tidal river sediment.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Association between Exposure of Young Children to Procedures Requiring General Anesthesia and Learning and Behavioral Outcomes in a Population-based Birth Cohort.

PubMed

Hu, Danqing; Flick, Randall P; Zaccariello, Michael J; Colligan, Robert C; Katusic, Slavica K; Schroeder, Darrell R; Hanson, Andrew C; Buenvenida, Shonie L; Gleich, Stephen J; Wilder, Robert T; Sprung, Juraj; Warner, David O

2017-08-01

Exposure of young animals to general anesthesia causes neurodegeneration and lasting behavioral abnormalities; whether these findings translate to children remains unclear. This study used a population-based birth cohort to test the hypothesis that multiple, but not single, exposures to procedures requiring general anesthesia before age 3 yr are associated with adverse neurodevelopmental outcomes. A retrospective study cohort was assembled from children born in Olmsted County, Minnesota, from 1996 to 2000 (inclusive). Propensity matching selected children exposed and not exposed to general anesthesia before age 3 yr. Outcomes ascertained via medical and school records included learning disabilities, attention-deficit/hyperactivity disorder, and group-administered ability and achievement tests. Analysis methods included proportional hazard regression models and mixed linear models. For the 116 multiply exposed, 457 singly exposed, and 463 unexposed children analyzed, multiple, but not single, exposures were associated with an increased frequency of both learning disabilities and attention-deficit/hyperactivity disorder (hazard ratio for learning disabilities = 2.17 [95% CI, 1.32 to 3.59], unexposed as reference). Multiple exposures were associated with decreases in both cognitive ability and academic achievement. Single exposures were associated with modest decreases in reading and language achievement but not cognitive ability. These findings in children anesthetized with modern techniques largely confirm those found in an older birth cohort and provide additional evidence that children with multiple exposures are more likely to develop adverse outcomes related to learning and attention. Although a robust association was observed, these data do not determine whether anesthesia per se is causal.

The minimal residual QR-factorization algorithm for reliably solving subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm (MRQR). This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem. The advantages of the new procedure are highlighted by a numerical example.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

Stroke secondary to multiple spontaneous cholesterol emboli.

PubMed

Pascual, M; Baumgartner, J M; Bounameaux, H

1991-01-01

We describe one male, 49-year-old diabetic patient in whom regressive stroke with aphasia and right-sided hemiparesia was related to multiple small emboli in the left paraventricular cortex. Simultaneous presence of several cholesterol emboli in the left eye ground and detection of an atheromatous plaque at the homolateral carotid bifurcation let assume that the cerebral emboli originated from that plaque and also consisted of cholesterol crystals. The patient was discharged on low-dose aspirin (100 mg/day) after neurologic improvement. Follow-up at one year revealed clinical stability, recurrence of the cholesterol emboli at the eye ground examination and no change of the carotid plaque. Cholesterol embolization with renal failure, hypertension and peripheral arterial occlusions causing skin ulcerations is classical in case of atheromatous aortic disease but stroke has rarely been reported in this syndrome. However, more frequent use of invasive procedures (arteriography, transluminal angioplasty, vascular surgery) or thrombolytic treatment might increase its incidence in the near future.

Assessing NARCCAP climate model effects using spatial confidence regions

PubMed Central

French, Joshua P.; McGinnis, Seth; Schwartzman, Armin

2017-01-01

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference. PMID:28936474

Novel risk score of contrast-induced nephropathy after percutaneous coronary intervention.

PubMed

Ji, Ling; Su, XiaoFeng; Qin, Wei; Mi, XuHua; Liu, Fei; Tang, XiaoHong; Li, Zi; Yang, LiChuan

2015-08-01

Contrast-induced nephropathy (CIN) post-percutaneous coronary intervention (PCI) is a major cause of acute kidney injury. In this study, we established a comprehensive risk score model to assess risk of CIN after PCI procedure, which could be easily used in a clinical environment. A total of 805 PCI patients, divided into analysis cohort (70%) and validation cohort (30%), were enrolled retrospectively in this study. Risk factors for CIN were identified using univariate analysis and multivariate logistic regression in the analysis cohort. Risk score model was developed based on multiple regression coefficients. Sensitivity and specificity of the new risk score system was validated in the validation cohort. Comparisons between the new risk score model and previous reported models were applied. The incidence of post-PCI CIN in the analysis cohort (n = 565) was 12%. Considerably high CIN incidence (50%) was observed in patients with chronic kidney disease (CKD). Age >75, body mass index (BMI) >25, myoglobin level, cardiac function level, hypoalbuminaemia, history of chronic kidney disease (CKD), Intra-aortic balloon pump (IABP) and peripheral vascular disease (PVD) were identified as independent risk factors of post-PCI CIN. A novel risk score model was established using multivariate regression coefficients, which showed highest sensitivity and specificity (0.917, 95%CI 0.877-0.957) compared with previous models. A new post-PCI CIN risk score model was developed based on a retrospective study of 805 patients. Application of this model might be helpful to predict CIN in patients undergoing PCI procedure. © 2015 Asian Pacific Society of Nephrology.

Estimating parasitic sea lamprey abundance in Lake Huron from heterogenous data sources

USGS Publications Warehouse

Young, Robert J.; Jones, Michael L.; Bence, James R.; McDonald, Rodney B.; Mullett, Katherine M.; Bergstedt, Roger A.

2003-01-01

The Great Lakes Fishery Commission uses time series of transformer, parasitic, and spawning population estimates to evaluate the effectiveness of its sea lamprey (Petromyzon marinus) control program. This study used an inverse variance weighting method to integrate Lake Huron sea lamprey population estimates derived from two estimation procedures: 1) prediction of the lake-wide spawning population from a regression model based on stream size and, 2) whole-lake mark and recapture estimates. In addition, we used a re-sampling procedure to evaluate the effect of trading off sampling effort between the regression and mark-recapture models. Population estimates derived from the regression model ranged from 132,000 to 377,000 while mark-recapture estimates of marked recently metamorphosed juveniles and parasitic sea lampreys ranged from 536,000 to 634,000 and 484,000 to 1,608,000, respectively. The precision of the estimates varied greatly among estimation procedures and years. The integrated estimate of the mark-recapture and spawner regression procedures ranged from 252,000 to 702,000 transformers. The re-sampling procedure indicated that the regression model is more sensitive to reduction in sampling effort than the mark-recapture model. Reliance on either the regression or mark-recapture model alone could produce misleading estimates of abundance of sea lampreys and the effect of the control program on sea lamprey abundance. These analyses indicate that the precision of the lakewide population estimate can be maximized by re-allocating sampling effort from marking sea lampreys to trapping additional streams.

Estimating increment-decrement life tables with multiple covariates from panel data: the case of active life expectancy.

PubMed

Land, K C; Guralnik, J M; Blazer, D G

1994-05-01

A fundamental limitation of current multistate life table methodology-evident in recent estimates of active life expectancy for the elderly-is the inability to estimate tables from data on small longitudinal panels in the presence of multiple covariates (such as sex, race, and socioeconomic status). This paper presents an approach to such an estimation based on an isomorphism between the structure of the stochastic model underlying a conventional specification of the increment-decrement life table and that of Markov panel regression models for simple state spaces. We argue that Markov panel regression procedures can be used to provide smoothed or graduated group-specific estimates of transition probabilities that are more stable across short age intervals than those computed directly from sample data. We then join these estimates with increment-decrement life table methods to compute group-specific total, active, and dependent life expectancy estimates. To illustrate the methods, we describe an empirical application to the estimation of such life expectancies specific to sex, race, and education (years of school completed) for a longitudinal panel of elderly persons. We find that education extends both total life expectancy and active life expectancy. Education thus may serve as a powerful social protective mechanism delaying the onset of health problems at older ages.

A rotor optimization using regression analysis

NASA Technical Reports Server (NTRS)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Basis Selection for Wavelet Regression

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Lau, Sonie (Technical Monitor)

1998-01-01

A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using cross-validation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled functions widely used in the wavelet regression literature. The results of the method are contrasted with other published methods.

Polymorphism Thr160Thr in SRD5A1, involved in the progesterone metabolism, modifies postmenopausal breast cancer risk associated with menopausal hormone therapy.

PubMed

Hein, R; Abbas, S; Seibold, P; Salazar, R; Flesch-Janys, D; Chang-Claude, J

2012-01-01

Menopausal hormone therapy (MHT) is associated with an increased breast cancer risk in postmenopausal women, with combined estrogen-progestagen therapy posing a greater risk than estrogen monotherapy. However, few studies focused on potential effect modification of MHT-associated breast cancer risk by genetic polymorphisms in the progesterone metabolism. We assessed effect modification of MHT use by five coding single nucleotide polymorphisms (SNPs) in the progesterone metabolizing enzymes AKR1C3 (rs7741), AKR1C4 (rs3829125, rs17134592), and SRD5A1 (rs248793, rs3736316) using a two-center population-based case-control study from Germany with 2,502 postmenopausal breast cancer patients and 4,833 matched controls. An empirical-Bayes procedure that tests for interaction using a weighted combination of the prospective and the retrospective case-control estimators as well as standard prospective logistic regression were applied to assess multiplicative statistical interaction between polymorphisms and duration of MHT use with regard to breast cancer risk assuming a log-additive mode of inheritance. No genetic marginal effects were observed. Breast cancer risk associated with duration of combined therapy was significantly modified by SRD5A1_rs3736316, showing a reduced risk elevation in carriers of the minor allele (p (interaction,empirical-Bayes) = 0.006 using the empirical-Bayes method, p (interaction,logistic regression) = 0.013 using logistic regression). The risk associated with duration of use of monotherapy was increased by AKR1C3_rs7741 in minor allele carriers (p (interaction,empirical-Bayes) = 0.083, p (interaction,logistic regression) = 0.029) and decreased in minor allele carriers of two SNPs in AKR1C4 (rs3829125: p (interaction,empirical-Bayes) = 0.07, p (interaction,logistic regression) = 0.021; rs17134592: p (interaction,empirical-Bayes) = 0.101, p (interaction,logistic regression) = 0.038). After Bonferroni correction for multiple testing only SRD5A1_rs3736316 assessed using the empirical-Bayes method remained significant. Postmenopausal breast cancer risk associated with combined therapy may be modified by genetic variation in SRD5A1. Further well-powered studies are, however, required to replicate our finding.

The effects of normal aging on multiple aspects of financial decision-making.

PubMed

Bangma, Dorien F; Fuermaier, Anselm B M; Tucha, Lara; Tucha, Oliver; Koerts, Janneke

2017-01-01

Financial decision-making (FDM) is crucial for independent living. Due to cognitive decline that accompanies normal aging, older adults might have difficulties in some aspects of FDM. However, an improved knowledge, personal experience and affective decision-making, which are also related to normal aging, may lead to a stable or even improved age-related performance in some other aspects of FDM. Therefore, the present explorative study examines the effects of normal aging on multiple aspects of FDM. One-hundred and eighty participants (range 18-87 years) were assessed with eight FDM tests and several standard neuropsychological tests. Age effects were evaluated using hierarchical multiple regression analyses. The validity of the prediction models was examined by internal validation (i.e. bootstrap resampling procedure) as well as external validation on another, independent, sample of participants (n = 124). Multiple regression and correlation analyses were applied to investigate the mediation effect of standard measures of cognition on the observed effects of age on FDM. On a relatively basic level of FDM (e.g., paying bills or using FDM styles) no significant effects of aging were found. However more complex FDM, such as making decisions in accordance with specific rules, becomes more difficult with advancing age. Furthermore, an older age was found to be related to a decreased sensitivity for impulsive buying. These results were confirmed by the internal and external validation analyses. Mediation effects of numeracy and planning were found to explain parts of the association between one aspect of FDM (i.e. Competence in decision rules) and age; however, these cognitive domains were not able to completely explain the relation between age and FDM. Normal aging has a negative influence on a complex aspect of FDM, however, other aspects appear to be unaffected by normal aging or improve.

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.

ERIC Educational Resources Information Center

Kromrey, Jeffrey D.; Hines, Constance V.

1995-01-01

The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…

Enhance-Synergism and Suppression Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, W. Michael

2004-01-01

Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

Risk Factors for 30-Day Complications After Thumb CMC Joint Arthroplasty: An American College of Surgeons National Surgery Quality Improvement Program Study.

PubMed

Shah, Kalpit N; Defroda, Steven F; Wang, Bo; Weiss, Arnold-Peter C

2017-12-01

The first carpometacarpal (CMC) joint is a common site of osteoarthritis, with arthroplasty being a common procedure to provide pain relief and improve function with low complications. However, little is known about risk factors that may predispose a patient for postoperative complications. All CMC joint arthroplasty from 2005 to 2015 in the prospectively collected American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database were identified. Bivariate testing and multiple logistic regressions were performed to determine which patient demographics, surgical variables and medical comorbidities were significant predictors for complications. These included wound related, cardiopulmonary, neurological and renal complications, return to the operating room (OR) and readmission. A total of 3344 patients were identified from the database. Of those, 45 patients (1.3%) experienced a complication including wound issues (0.66%), return to the OR (0.15%) and readmission (0.27%) amongst others. When performing bivariate analysis, age over 65, American Society of Anesthesiologists (ASA) Class, diabetes and renal dialysis were significant risk factors. Multiple logistic regression after adjusting for confounding factors demonstrated that insulin-dependent diabetes and ASA Class 4 had a strong trend while renal dialysis was a significant risk factor. CMC arthroplasty has a very low overall complication rate of 1.3% and wound complication rate of 0.66%. Diabetes requiring insulin and ASA Class 4 trended towards significance while renal dialysis was found to be a significant risk factors in logistic regression. This information may be useful for preoperative counseling and discussion with patients who have these risk factors.

Higher plasma level of STIM1, OPG are correlated with stent restenosis after PCI.

PubMed

Li, Haibin; Jiang, Zhian; Liu, Xiangdong; Yang, Zhihui

2015-01-01

Percutaneous Coronary Intervention (PCI) is one of the most effective treatments for Coronary Heart Disease (CHD), but the high rate of In Stent Restenosis (ISR) has plagued clinicians after PCI. We aim to investigate the correlation of plasma Stromal Interaction Molecular 1 (STIM1) and Osteoprotegerin (OPG) level with stent restenosis after PCI. A total of 100 consecutive patients with Coronary Heart Disease (CHD) received PCI procedure were recruited. Coronary angiography was performed 8 months after their PCI. Then patients were divided into 2 groups: observation group was composed by patients who existing postoperative stenosis after intervention; Control group was composed by patients with no postoperative stenosis. The plasma levels of STIM, OPG in all patients were tested before and after intervention. Pearson correlation and multiple linear regression analysis were performed to analysis the correlation between STIM, OPG level and postoperative stenosis. 35 cases were divided into observation group and other 65 were divided into control group. The plasma levels of STIM, OPG have no statistical difference before their PCI procedure, but we observed higher level of High-sensitivity C-reactive protein (Hs-CRP) existed in observation group. We observed higher level of plasma STIM, OPG in observation group when compared with control group after PCI procedure (P < 0.05). Regression analysis demonstrated that Hs-CRP, STIM1, OPG are independent risk factors for ISR. Elevated levels of plasma STIM1, OPG are independent risk factors for ISR in patients received PCI, which could provide useful information for the restenosis control after PCI.

Association Between Allergies and Psychiatric Disorders in Patients Undergoing Invasive Procedures.

PubMed

Aberle, Dwight; Wu, Stephanie E; Oklu, Rahmi; Erinjeri, Joseph; Deipolyi, Amy R

Associations between allergies and psychiatric disorders have been reported in the context of depression and suicide; psychiatric disorders may affect pain perception. To investigate the relationship of allergies with psychiatric disorders and pain perception in the context of invasive procedures, specifically during tunneled hemodialysis catheter placement. We identified 89 patients (51 men, 38 women), mean age 66 years (range: 23-96), who underwent tunneled hemodialysis catheter placement (1/2014-2/2015), recording numeric rating scale pain scores, medications, psychiatric history, allergies, and smoking status. Of 89 patients, 47 patients had no allergies, and 42 had ≥1 allergy. Patients with allergies were more likely to have a pre-existing psychiatric disorder compared to those without allergies, odds ratio 2.6 (95% CI: 1.0-6.8). Having allergies did not affect procedural sedation or postprocedural pain scores. Multiple logistic regression with age, sex, smoking, presence of allergies, psychiatric history, inpatient/outpatient status, procedure time, and procedural sedation administration as inputs and postprocedural pain as the outcome showed that the only independent predictor was receiving procedural sedation (P = 0.005). Findings corroborate anecdotal reports of allergies as a marker for psychiatric history. However, having allergies was not associated with increased pain or need for more sedation. Further studies could prospectively assess whether allergies and psychiatric disorders affect patient/doctor perceptions beyond pain during invasive procedures. Copyright © 2017 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

Surgical staging and prognosis in serous borderline ovarian tumours (BOT): A subanalysis of the AGO ROBOT study

PubMed Central

Trillsch, F; Mahner, S; Vettorazzi, E; Woelber, L; Reuss, A; Baumann, K; Keyver-Paik, M-D; Canzler, U; Wollschlaeger, K; Forner, D; Pfisterer, J; Schroeder, W; Muenstedt, K; Richter, B; Fotopoulou, C; Schmalfeldt, B; Burges, A; Ewald-Riegler, N; de Gregorio, N; Hilpert, F; Fehm, T; Meier, W; Hillemanns, P; Hanker, L; Hasenburg, A; Strauss, H-G; Hellriegel, M; Wimberger, P; Kommoss, S; Kommoss, F; Hauptmann, S; du Bois, A

2015-01-01

Background: Incomplete surgical staging is a negative prognostic factor for patients with borderline ovarian tumours (BOT). However, little is known about the prognostic impact of each individual staging procedure. Methods: Clinical parameters of 950 patients with BOT (confirmed by central reference pathology) treated between 1998 and 2008 at 24 German AGO centres were analysed. In 559 patients with serous BOT and adequate ovarian surgery, further recommended staging procedures (omentectomy, peritoneal biopsies, cytology) were evaluated applying Cox regression models with respect to progression-free survival (PFS). Results: For patients with one missing staging procedure, the hazard ratio (HR) for recurrence was 1.25 (95%-CI 0.66–2.39; P=0.497). This risk increased with each additional procedure skipped reaching statistical significance in case of two (HR 1.95; 95%-CI 1.06–3.58; P=0.031) and three missing steps (HR 2.37; 95%-CI 1.22–4.64; P=0.011). The most crucial procedure was omentectomy which retained a statistically significant impact on PFS in multiple analysis (HR 1.91; 95%-CI 1.15–3.19; P=0.013) adjusting for previously established prognostic factors as FIGO stage, tumour residuals, and fertility preservation. Conclusion: Individual surgical staging procedures contribute to the prognosis for patients with serous BOT. In this analysis, recurrence risk increased with each skipped surgical step. This should be considered when re-staging procedures following incomplete primary surgery are discussed. PMID:25562434

Pediatric ureteroscopic management of intrarenal calculi.

PubMed

Tanaka, Stacy T; Makari, John H; Pope, John C; Adams, Mark C; Brock, John W; Thomas, John C

2008-11-01

Data addressing ureteroscopic management of intrarenal calculi in prepubertal children are limited. We reviewed our experience from January 2002 through December 2007. We retrospectively reviewed ureteroscopic procedures for intrarenal calculi in children younger than 14 years. Stone-free status was determined with postoperative imaging. Multiple logistic regression analysis was used to assess the influence of preoperative factors on initial stone-free status and the need for additional procedures. Intrarenal calculi were managed ureteroscopically in 52 kidneys in 50 children with a mean age of 7.9 years (range 1.2 to 13.6). Mean stone size was 8 mm (range 1 to 16). Stone-free rate after a single ureteroscopic procedure was 50% (25 of 50 patients) on initial postoperative imaging and 58% (29 of 50) with extended followup. Initial stone-free status was dependent on preoperative stone size (p = 0.005) but not stone location. Additional stone procedures were required in 18 upper tracts. Younger patient age (p = 0.04) and larger preoperative stone size (p = 0.002) were associated with the need for additional procedures. Additional procedures were required in more than half of the stones 6 mm or larger but in no stone smaller than 6 mm. Ureteroscopy is a safe method for the treatment of intrarenal calculi in the prepubertal population. Our ureteroscopic stone-free rate for intrarenal stones is lower than that reported for ureteral stones. Parents should be informed that additional procedures will likely be required, especially in younger patients and those with stones larger than 6 mm.

Surgical staging and prognosis in serous borderline ovarian tumours (BOT): a subanalysis of the AGO ROBOT study.

PubMed

Trillsch, F; Mahner, S; Vettorazzi, E; Woelber, L; Reuss, A; Baumann, K; Keyver-Paik, M-D; Canzler, U; Wollschlaeger, K; Forner, D; Pfisterer, J; Schroeder, W; Muenstedt, K; Richter, B; Fotopoulou, C; Schmalfeldt, B; Burges, A; Ewald-Riegler, N; de Gregorio, N; Hilpert, F; Fehm, T; Meier, W; Hillemanns, P; Hanker, L; Hasenburg, A; Strauss, H-G; Hellriegel, M; Wimberger, P; Kommoss, S; Kommoss, F; Hauptmann, S; du Bois, A

2015-02-17

Incomplete surgical staging is a negative prognostic factor for patients with borderline ovarian tumours (BOT). However, little is known about the prognostic impact of each individual staging procedure. Clinical parameters of 950 patients with BOT (confirmed by central reference pathology) treated between 1998 and 2008 at 24 German AGO centres were analysed. In 559 patients with serous BOT and adequate ovarian surgery, further recommended staging procedures (omentectomy, peritoneal biopsies, cytology) were evaluated applying Cox regression models with respect to progression-free survival (PFS). For patients with one missing staging procedure, the hazard ratio (HR) for recurrence was 1.25 (95%-CI 0.66-2.39; P=0.497). This risk increased with each additional procedure skipped reaching statistical significance in case of two (HR 1.95; 95%-CI 1.06-3.58; P=0.031) and three missing steps (HR 2.37; 95%-CI 1.22-4.64; P=0.011). The most crucial procedure was omentectomy which retained a statistically significant impact on PFS in multiple analysis (HR 1.91; 95%-CI 1.15-3.19; P=0.013) adjusting for previously established prognostic factors as FIGO stage, tumour residuals, and fertility preservation. Individual surgical staging procedures contribute to the prognosis for patients with serous BOT. In this analysis, recurrence risk increased with each skipped surgical step. This should be considered when re-staging procedures following incomplete primary surgery are discussed.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Incremental Net Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, Michael

2005-01-01

A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

Floating Data and the Problem with Illustrating Multiple Regression.

ERIC Educational Resources Information Center

Sachau, Daniel A.

2000-01-01

Discusses how to introduce basic concepts of multiple regression by creating a large-scale, three-dimensional regression model using the classroom walls and floor. Addresses teaching points that should be covered and reveals student reaction to the model. Finds that the greatest benefit of the model is the low fear, walk-through, nonmathematical…

Two Paradoxes in Linear Regression Analysis.

PubMed

Feng, Ge; Peng, Jing; Tu, Dongke; Zheng, Julia Z; Feng, Changyong

2016-12-25

Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection.

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

DTIC Science & Technology

2017-03-23

PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and

Learning the facts in medical school is not enough: which factors predict successful application of procedural knowledge in a laboratory setting?

PubMed

Schmidmaier, Ralf; Eiber, Stephan; Ebersbach, Rene; Schiller, Miriam; Hege, Inga; Holzer, Matthias; Fischer, Martin R

2013-02-22

Medical knowledge encompasses both conceptual (facts or "what" information) and procedural knowledge ("how" and "why" information). Conceptual knowledge is known to be an essential prerequisite for clinical problem solving. Primarily, medical students learn from textbooks and often struggle with the process of applying their conceptual knowledge to clinical problems. Recent studies address the question of how to foster the acquisition of procedural knowledge and its application in medical education. However, little is known about the factors which predict performance in procedural knowledge tasks. Which additional factors of the learner predict performance in procedural knowledge? Domain specific conceptual knowledge (facts) in clinical nephrology was provided to 80 medical students (3rd to 5th year) using electronic flashcards in a laboratory setting. Learner characteristics were obtained by questionnaires. Procedural knowledge in clinical nephrology was assessed by key feature problems (KFP) and problem solving tasks (PST) reflecting strategic and conditional knowledge, respectively. Results in procedural knowledge tests (KFP and PST) correlated significantly with each other. In univariate analysis, performance in procedural knowledge (sum of KFP+PST) was significantly correlated with the results in (1) the conceptual knowledge test (CKT), (2) the intended future career as hospital based doctor, (3) the duration of clinical clerkships, and (4) the results in the written German National Medical Examination Part I on preclinical subjects (NME-I). After multiple regression analysis only clinical clerkship experience and NME-I performance remained independent influencing factors. Performance in procedural knowledge tests seems independent from the degree of domain specific conceptual knowledge above a certain level. Procedural knowledge may be fostered by clinical experience. More attention should be paid to the interplay of individual clinical clerkship experiences and structured teaching of procedural knowledge and its assessment in medical education curricula.

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

PubMed

Bernhardt, Paul W; Wang, Huixia J; Zhang, Daowen

2015-05-01

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

The Heterogeneity in Retrieved Relations between the Personality Trait ‘Harm Avoidance’ and Gray Matter Volumes Due to Variations in the VBM and ROI Labeling Processing Settings

PubMed Central

Van Schuerbeek, Peter; Baeken, Chris; De Mey, Johan

2016-01-01

Concerns are raising about the large variability in reported correlations between gray matter morphology and affective personality traits as ‘Harm Avoidance’ (HA). A recent review study (Mincic 2015) stipulated that this variability could come from methodological differences between studies. In order to achieve more robust results by standardizing the data processing procedure, as a first step, we repeatedly analyzed data from healthy females while changing the processing settings (voxel-based morphology (VBM) or region-of-interest (ROI) labeling, smoothing filter width, nuisance parameters included in the regression model, brain atlas and multiple comparisons correction method). The heterogeneity in the obtained results clearly illustrate the dependency of the study outcome to the opted analysis settings. Based on our results and the existing literature, we recommended the use of VBM over ROI labeling for whole brain analyses with a small or intermediate smoothing filter (5-8mm) and a model variable selection step included in the processing procedure. Additionally, it is recommended that ROI labeling should only be used in combination with a clear hypothesis and that authors are encouraged to report their results uncorrected for multiple comparisons as supplementary material to aid review studies. PMID:27096608

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

PubMed

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression

PubMed Central

2015-01-01

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost. PMID:26574437

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

NASA Astrophysics Data System (ADS)

Otero, Noelia Felipe; Sillmann, Jana; Schnell, Jordan L.; Rust, Henning W.; Butler, Tim

2016-04-01

The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998-2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8-hour average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over Southern Europe. In general, the best model performance is found over Central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months.

Two Paradoxes in Linear Regression Analysis

PubMed Central

FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

2016-01-01

Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

PubMed

Vervloet, Marlies; Van den Noortgate, Wim; Ceulemans, Eva

2018-02-12

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

Evaluation of the magnitude and frequency of floods in urban watersheds in Phoenix and Tucson, Arizona

USGS Publications Warehouse

Kennedy, Jeffrey R.; Paretti, Nicholas V.

2014-01-01

Flooding in urban areas routinely causes severe damage to property and often results in loss of life. To investigate the effect of urbanization on the magnitude and frequency of flood peaks, a flood frequency analysis was carried out using data from urbanized streamgaging stations in Phoenix and Tucson, Arizona. Flood peaks at each station were predicted using the log-Pearson Type III distribution, fitted using the expected moments algorithm and the multiple Grubbs-Beck low outlier test. The station estimates were then compared to flood peaks estimated by rural-regression equations for Arizona, and to flood peaks adjusted for urbanization using a previously developed procedure for adjusting U.S. Geological Survey rural regression peak discharges in an urban setting. Only smaller, more common flood peaks at the 50-, 20-, 10-, and 4-percent annual exceedance probabilities (AEPs) demonstrate any increase in magnitude as a result of urbanization; the 1-, 0.5-, and 0.2-percent AEP flood estimates are predicted without bias by the rural-regression equations. Percent imperviousness was determined not to account for the difference in estimated flood peaks between stations, either when adjusting the rural-regression equations or when deriving urban-regression equations to predict flood peaks directly from basin characteristics. Comparison with urban adjustment equations indicates that flood peaks are systematically overestimated if the rural-regression-estimated flood peaks are adjusted upward to account for urbanization. At nearly every streamgaging station in the analysis, adjusted rural-regression estimates were greater than the estimates derived using station data. One likely reason for the lack of increase in flood peaks with urbanization is the presence of significant stormwater retention and detention structures within the watershed used in the study.

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

Miraglia, Roberto, E-mail: rmiraglia@ismett.edu; Maruzzelli, Luigi; Tuzzolino, Fabio

Purpose: The aim of this study was to estimate radiation exposure in pediatric liver transplants recipients who underwent biliary interventional procedures and to compare radiation exposure levels between biliary interventional procedures performed using an image intensifier-based angiographic system (IIDS) and a flat panel detector-based interventional system (FPDS). Materials and Methods: We enrolled 34 consecutive pediatric liver transplant recipients with biliary strictures between January 2008 and March 2013 with a total of 170 image-guided procedures. The dose-area product (DAP) and fluoroscopy time was recorded for each procedure. The mean age was 61 months (range 4-192), and mean weight was 17 kgmore » (range 4-41). The procedures were classified into three categories: percutaneous transhepatic cholangiography and biliary catheter placement (n = 40); cholangiography and balloon dilatation (n = 55); and cholangiography and biliary catheter change or removal (n = 75). Ninety-two procedures were performed using an IIDS. Seventy-eight procedures performed after July 2010 were performed using an FPDS. The difference in DAP between the two angiographic systems was compared using Wilcoxon rank-sum test and a multiple linear regression model. Results: Mean DAP in the three categories was significantly greater in the group of procedures performed using the IIDS compared with those performed using the FPDS. Statistical analysis showed a p value = 0.001 for the PTBD group, p = 0.0002 for the cholangiogram and balloon dilatation group, and p = 0.00001 for the group with cholangiogram and biliary catheter change or removal. Conclusion: In our selected cohort of patients, the use of an FPDS decreases radiation exposure.« less

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Insight and neurocognitive functioning in bipolar subjects.

PubMed

Shad, Mujeeb U; Prasad, Konasale; Forman, Steven D; Haas, Gretchen L; Walker, Jon D; Pisarov, Liubomir A; Goldstein, Gerald

2015-01-01

Insight concerning having a mental illness has been found to influence outcome and effectiveness of treatment. It has been studied mainly in the area of schizophrenia with few studies addressing other disorders. This study evaluates insight in individuals with bipolar disorder using the Scale to Assess Unawareness of Mental Disorder (SUMD), a comprehensive interview for evaluation of awareness of illness and attribution of symptoms. The hypothesis was that in bipolar disorder level of awareness may be associated with numerous factors including neurocognitive function, structural changes in the frontal lobes and hippocampus evaluated by MRI, neurocognitive status, severity of mania and other psychiatric symptoms and comorbid alcoholism. In order to evaluate this hypothesis 33 individuals with DSM-IV diagnosed bipolar disorder, some with and some without comorbid alcoholism, were administered the SUMD and a number of other procedures including a quantitative MRI measuring volume of the frontal lobes and hippocampus, a brief battery of neurocognitive tests, the Brief Psychiatric Rating Scale, and the Young Mania Rating Scale. The data were analyzed by comparing participants with and without alcoholism on these procedures using t tests and by linear multiple regression, with SUMD ratings of awareness and attribution as the dependent variables and variable sets from the other procedures administered as multivariate independent variables. The median score obtained from the SUMD for current awareness was in a range between full awareness and uncertainty concerning presence of a mental disorder. For attribution, the median score indicated that attribution was usually made to the illness itself. None of the differences between participants with and without comorbid alcoholism were significant for the SUMD awareness and attribution scores, neurocognitive or MRI variables. The multiple regression analyses only showed a significant degree of association between the SUMD awareness score and the Young Mania Rating Scale (r(2)=.632, p<.05). A stepwise analysis indicated that items assessing degree of insight, irritability, and sleep disturbance met criteria for entry into the regression equation. None of the regression analyses for the SUMD attribution item were significant. Apparently unlike the case for schizophrenia, most of the participants, all of whom had bipolar disorder, were aware of their symptoms and correctly related them to a mental disorder. Hypotheses concerning the relationships between degree of unawareness and possible contributors to its development including comorbid alcoholism, cognitive dysfunction and structural reduction of gray matter in the frontal region and hippocampus, were not associated with degree of unawareness but symptoms of mania were significantly associated. The apparent reason for this result is that the sample obtained a SUMD modal awareness score of 1 or 2, reflecting the area between full awareness and uncertainty about having a mental disorder. None of the participants were rated as having a 5 response reflecting the belief that s/he does not have a mental disorder. Published by Elsevier Inc.

A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates

PubMed Central

BARTROFF, JAY; SONG, JINLIN

2015-01-01

We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together we call these conditions a “rejection principle for sequential tests,” which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and to finding the maximum safe dose of a treatment. PMID:26985125

The Geometry of Enhancement in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.

2011-01-01

In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

Do different types of school mathematics development depend on different constellations of numerical versus general cognitive abilities?

PubMed

Fuchs, Lynn S; Geary, David C; Compton, Donald L; Fuchs, Douglas; Hamlett, Carol L; Seethaler, Pamela M; Bryant, Joan D; Schatschneider, Christopher

2010-11-01

The purpose of this study was to examine the interplay between basic numerical cognition and domain-general abilities (such as working memory) in explaining school mathematics learning. First graders (N = 280; mean age = 5.77 years) were assessed on 2 types of basic numerical cognition, 8 domain-general abilities, procedural calculations, and word problems in fall and then reassessed on procedural calculations and word problems in spring. Development was indexed by latent change scores, and the interplay between numerical and domain-general abilities was analyzed by multiple regression. Results suggest that the development of different types of formal school mathematics depends on different constellations of numerical versus general cognitive abilities. When controlling for 8 domain-general abilities, both aspects of basic numerical cognition were uniquely predictive of procedural calculations and word problems development. Yet, for procedural calculations development, the additional amount of variance explained by the set of domain-general abilities was not significant, and only counting span was uniquely predictive. By contrast, for word problems development, the set of domain-general abilities did provide additional explanatory value, accounting for about the same amount of variance as the basic numerical cognition variables. Language, attentive behavior, nonverbal problem solving, and listening span were uniquely predictive.

Anal endosonography and bowel function in patients undergoing different types of endorectal pull-through procedures for Hirschsprung disease.

PubMed

Stensrud, Kjetil J; Emblem, Ragnhild; Bjørnland, Kristin

2015-08-01

The reasons for fecal incontinence after surgery for Hirschsprung disease (HD) remain unclear. The aim of this study was to examine the anal sphincters by anal endosonography and manometry after transanal endorectal pull-through, with or without laparotomy or laparoscopy, in HD patients. Furthermore, we aimed to correlate these findings to bowel function. Fifty-two HD patients were followed after endorectal pull-through. Anal endosonography and manometry were performed without sedation at the age of 3 to 16 years. Endosonographic internal anal sphincter (IAS) defects were found in 24/50 patients, more frequently after transanal than transabdominal procedures (69 vs. 19%, p=0.001). In a multiple variable logistic regression model, operative approach was the only significant predictor for IAS defects. Anal resting pressure (median 40mm Hg, range 15-120) was not correlated to presence of IAS defects. Daily fecal incontinence occurred more often in patients with IAS defects (54 vs. 25%, p=0.03). Postoperative IAS defects were frequently detected and were associated with daily fecal incontinence. IAS defects occurred more often after solely transanal procedures. We propose that these surgical approaches are compared in a randomized controlled trial before solely transanal endorectal pull-through is performed as a routine procedure. Copyright © 2015 Elsevier Inc. All rights reserved.

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

Efficiency Analysis: Enhancing the Statistical and Evaluative Power of the Regression-Discontinuity Design.

ERIC Educational Resources Information Center

Madhere, Serge

An analytic procedure, efficiency analysis, is proposed for improving the utility of quantitative program evaluation for decision making. The three features of the procedure are explained: (1) for statistical control, it adopts and extends the regression-discontinuity design; (2) for statistical inferences, it de-emphasizes hypothesis testing in…

Screening and clustering of sparse regressions with finite non-Gaussian mixtures.

PubMed

Zhang, Jian

2017-06-01

This article proposes a method to address the problem that can arise when covariates in a regression setting are not Gaussian, which may give rise to approximately mixture-distributed errors, or when a true mixture of regressions produced the data. The method begins with non-Gaussian mixture-based marginal variable screening, followed by fitting a full but relatively smaller mixture regression model to the selected data with help of a new penalization scheme. Under certain regularity conditions, the new screening procedure is shown to possess a sure screening property even when the population is heterogeneous. We further prove that there exists an elbow point in the associated scree plot which results in a consistent estimator of the set of active covariates in the model. By simulations, we demonstrate that the new procedure can substantially improve the performance of the existing procedures in the content of variable screening and data clustering. By applying the proposed procedure to motif data analysis in molecular biology, we demonstrate that the new method holds promise in practice. © 2016, The International Biometric Society.

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

Suicide, hopelessness, and social desirability: a test of an interactive model.

PubMed

Holden, R R; Mendonca, J D; Serin, R C

1989-08-01

We examined the relationships among suicidal indices, hopelessness, and social desirability. Both hopelessness and a measure of social desirability that reflected a sense of general capability were significant indicators of suicidal manifestations. In particular, hierarchical multiple regression procedures demonstrated that hopelessness and social desirability interacted in the prediction of suicide variables. Results generalized across various clinical diagnostic subgroups of psychiatric patients and a sample of prisoners and across different clinically evaluated and self-reported indices of suicidal behavior. Findings are interpreted to mean that a sense of general capability buffers the link of hopelessness to suicidal behavior. Implications for understanding the cognitions associated with suicide and for improving prediction of persons at risk are discussed.

Imidazole derivatives as angiotensin II AT1 receptor blockers: Benchmarks, drug-like calculations and quantitative structure-activity relationships modeling

NASA Astrophysics Data System (ADS)

Alloui, Mebarka; Belaidi, Salah; Othmani, Hasna; Jaidane, Nejm-Eddine; Hochlaf, Majdi

2018-03-01

We performed benchmark studies on the molecular geometry, electron properties and vibrational analysis of imidazole using semi-empirical, density functional theory and post Hartree-Fock methods. These studies validated the use of AM1 for the treatment of larger systems. Then, we treated the structural, physical and chemical relationships for a series of imidazole derivatives acting as angiotensin II AT1 receptor blockers using AM1. QSAR studies were done for these imidazole derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of imidazole derivatives. Results validate the derived QSAR model.

The Role of Mediators in the Indirect Effects of Religiosity on Therapeutic Compliance in African Migrant HIV-Positive Patients.

PubMed

Mambet Doue, Constance; Roussiau, Nicolas

2016-12-01

This research investigates the indirect effects of religiosity (practice and belief) on therapeutic compliance in 81 HIV-positive patients who are migrants from sub-Saharan Africa (23 men and 58 women). Using analyses of mediation and standard multiple regression, including a resampling procedure by bootstrapping, the role of these mediators (magical-religious beliefs and nonuse of toxic substances) was tested. The results show that, through magical-religious beliefs, religiosity has a negative indirect effect, while with the nonuse of toxic substances, religious practice has a positive indirect effect. Beyond religiosity, the role of mediators is highlighted in the interaction with therapeutic compliance.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Regression Models for Identifying Noise Sources in Magnetic Resonance Images

PubMed Central

Zhu, Hongtu; Li, Yimei; Ibrahim, Joseph G.; Shi, Xiaoyan; An, Hongyu; Chen, Yashen; Gao, Wei; Lin, Weili; Rowe, Daniel B.; Peterson, Bradley S.

2009-01-01

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models. PMID:19890478

Development and validation of an environmental fragility index (EFI) for the neotropical savannah biome.

PubMed

Macedo, Diego R; Hughes, Robert M; Kaufmann, Philip R; Callisto, Marcos

2018-04-23

Augmented production and transport of fine sediments resulting from increased human activities are major threats to freshwater ecosystems, including reservoirs and their ecosystem services. To support large scale assessment of the likelihood of soil erosion and reservoir sedimentation, we developed and validated an environmental fragility index (EFI) for the Brazilian neotropical savannah. The EFI was derived from measured geoclimatic controls on sediment production (rainfall, variation of elevation and slope, geology) and anthropogenic pressures (natural cover, road density, distance from roads and urban centers) in 111 catchments upstream of four large hydroelectric reservoirs. We evaluated the effectiveness of the EFI by regressing it against a relative bed stability index (LRBS) that assesses the degree to which stream sites draining into the reservoirs are affected by excess fine sediments. We developed the EFI on 111 of these sites and validated our model on the remaining 37 independent sites. We also compared the effectiveness of the EFI in predicting LRBS with that of a multiple linear regression model (via best-subset procedure) using 7 independent variables. The EFI was significantly correlated with the LRBS, with regression R 2 values of 0.32 and 0.40, respectively, in development and validation sites. Although the EFI and multiple regression explained similar amounts of variability (R 2  = 0.32 vs 0.36), the EFI had a higher F-ratio (51.6 vs 8.5) and better AICc value (333 vs 338). Because the sites were randomly selected and well-distributed across geoclimatic controlling factors, we were able to calculate spatially-explicit EFI values for all hydrologic units within the study area (~38,500 km 2 ). This model-based inference showed that over 65% of those units had high or extreme fragility. This methodology has great potential for application in the management, recovery, and preservation of hydroelectric reservoirs and streams in tropical river basins. Copyright © 2018 Elsevier B.V. All rights reserved.

The relation between receptive grammar and procedural, declarative, and working memory in specific language impairment.

PubMed

Conti-Ramsden, Gina; Ullman, Michael T; Lum, Jarrad A G

2015-01-01

What memory systems underlie grammar in children, and do these differ between typically developing (TD) children and children with specific language impairment (SLI)? Whilst there is substantial evidence linking certain memory deficits to the language problems in children with SLI, few studies have investigated multiple memory systems simultaneously, examining not only possible memory deficits but also memory abilities that may play a compensatory role. This study examined the extent to which procedural, declarative, and working memory abilities predict receptive grammar in 45 primary school aged children with SLI (30 males, 15 females) and 46 TD children (30 males, 16 females), both on average 9;10 years of age. Regression analyses probed measures of all three memory systems simultaneously as potential predictors of receptive grammar. The model was significant, explaining 51.6% of the variance. There was a significant main effect of learning in procedural memory and a significant group × procedural learning interaction. Further investigation of the interaction revealed that procedural learning predicted grammar in TD but not in children with SLI. Indeed, procedural learning was the only predictor of grammar in TD. In contrast, only learning in declarative memory significantly predicted grammar in SLI. Thus, different memory systems are associated with receptive grammar abilities in children with SLI and their TD peers. This study is, to our knowledge, the first to demonstrate a significant group by memory system interaction in predicting grammar in children with SLI and their TD peers. In line with Ullman's Declarative/Procedural model of language and procedural deficit hypothesis of SLI, variability in understanding sentences of varying grammatical complexity appears to be associated with variability in procedural memory abilities in TD children, but with declarative memory, as an apparent compensatory mechanism, in children with SLI.

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

New Casemix Classification as an Alternative Method for Budget Allocation in Thai Oral Healthcare Service: A Pilot Study

PubMed Central

Wisaijohn, Thunthita; Pimkhaokham, Atiphan; Lapying, Phenkhae; Itthichaisri, Chumpot; Pannarunothai, Supasit; Igarashi, Isao; Kawabuchi, Koichi

2010-01-01

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oral healthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, Thai Modification (ICD-10-TM) related to OHCS was used for developing the software “Grouper”. This model was designed to allow the translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship between the factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction in variance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications, according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity between groups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCS casemix classification has a potential use in a global decision making. PMID:20936134

Risk adjustment policy options for casemix funding: international lessons in financing reform.

PubMed

Antioch, Kathryn M; Ellis, Randall P; Gillett, Steve; Borovnicar, Daniel; Marshall, Ric P

2007-09-01

This paper explores modified hospital casemix payment formulae that would refine the diagnosis-related group (DRG) system in Victoria, Australia, which already makes adjustments for teaching, severity and demographics. We estimate alternative casemix funding methods using multiple regressions for individual hospital episodes from 2001 to 2003 on 70 high-deficit DRGs, focussing on teaching hospitals where the largest deficits have occurred. Our casemix variables are diagnosis- and procedure-based severity markers, counts of diagnoses and procedures, disease types, complexity, day outliers, emergency admission and "transfers in." The results are presented for four policy options that vary according to whether all of the dollars or only some are reallocated, whether all or some hospitals are used and whether the alternatives augment or replace existing payments. While our approach identifies variables that help explain patient cost variations, hospital-level simulations suggest that the approaches explored would only reduce teaching hospital underpayment by about 10%. The implications of various policy options are discussed.

New casemix classification as an alternative method for budget allocation in thai oral healthcare service: a pilot study.

PubMed

Wisaijohn, Thunthita; Pimkhaokham, Atiphan; Lapying, Phenkhae; Itthichaisri, Chumpot; Pannarunothai, Supasit; Igarashi, Isao; Kawabuchi, Koichi

2010-01-01

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oral healthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, Thai Modification (ICD-10-TM) related to OHCS was used for developing the software "Grouper". This model was designed to allow the translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship between the factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction in variance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications, according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity between groups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCS casemix classification has a potential use in a global decision making.

Invariance levels across language versions of the PISA 2009 reading comprehension tests in Spain.

PubMed

Elosua Oliden, Paula; Mujika Lizaso, Josu

2013-01-01

The PISA project provides the basis for studying curriculum design and for comparing factors associated with school effectiveness. These studies are only valid if the different language versions are equivalent to each other. In Spain, the application of PISA in autonomous regions with their own languages means that equivalency must also be extended to the Spanish, Galician, Catalan and Basque versions of the test. The aim of this work was to analyse the equivalence among the four language versions of the Reading Comprehension Test (PISA 2009). After defining the testlet as the unit of analysis, equivalence among the language versions was analysed using two invariance testing procedures: multiple-group mean and covariance structure analyses for ordinal data and ordinal logistic regression. The procedures yielded concordant results supporting metric equivalence across all four language versions: Spanish, Basque, Galician and Catalan. The equivalence supports the estimated reading literacy score comparability among the language versions used in Spain.

Volunteering predicts happiness among older Māori and non-Māori in the New Zealand health, work, and retirement longitudinal study.

PubMed

Dulin, Patrick L; Gavala, Jhanitra; Stephens, Christine; Kostick, Marylynne; McDonald, Jennifer

2012-01-01

This study sought to understand the relationship between volunteer activity and happiness among a sample of older adult New Zealanders. It specifically sought to determine if ethnicity (Māori vs. non-Māori) and economic living standards (ELS) functioned as moderators of the relationship between volunteering and happiness. Data were garnered from the 2008 administration of the New Zealand Health, Work, and Retirement Longitudinal Study. Correlational and multiple regression procedures were employed to examine study hypotheses. Results from multiple regression analyses showed that the amount of volunteering per week was a unique predictor of the overall level of happiness. Moderation analyses indicated that ethnicity did not function as a moderator of the relationship between volunteering and happiness, but ELS did. Those with low ELS evidenced a stronger relationship between volunteering and happiness than those with high ELS. Results also indicated that Maori and those with low ELS volunteered more frequently than non-Māori and those with high ELS. This study provides evidence that volunteering is related to increased happiness, irrespective of ethnicity. It also provides further evidence that the relationship between volunteering and happiness is moderated by economic resources. Older individuals at the low end of the economic spectrum are likely to benefit more from volunteering than those at the high end.

Comparison of risk factors for tooth loss between professional drivers and white-collar workers: an internet survey.

PubMed

Suzuki, Seitaro; Yoshino, Koichi; Takayanagi, Atsushi; Ishizuka, Yoichi; Satou, Ryouichi; Kamijo, Hideyuki; Sugihara, Naoki

2016-06-10

This cross-sectional study was conducted to examine tooth loss and associated factors among professional drivers and white-collar workers. The participants were recruited by applying screening procedures to a pool of Japanese registrants in an online database. The participants were asked to complete a self-reported questionnaire. A total of 592 professional drivers and 328 white-collar workers (male, aged 30 to 69 years) were analyzed. A multiple logistic regression analysis was performed to identify differences between professional drivers and white-collar workers. The results showed that professional drivers had fewer teeth than white-collar workers (odds ratio [OR], 1.74; 95% confidence interval [95% CI], 1.150-2.625). Moreover, a second multiple logistic regression analysis revealed that several factors were associated with the number of teeth among professional drivers: diabetes mellitus (OR, 2.68; 95% CI, 1.388-5.173), duration of brushing teeth (OR, 1.66; 95% CI, 1.066-2.572), frequency of eating breakfast (OR, 2.23; 95% CI, 1.416-3.513), frequency of eating out (OR, 1.70; 95% CI, 1.086-2.671) and smoking status (OR, 2.88; 95% CI, 1.388-5.964). These findings suggest that the lifestyles of professional drivers could be related to not only their general health status, but also tooth loss.

Physiological and anthropometric determinants of rhythmic gymnastics performance.

PubMed

Douda, Helen T; Toubekis, Argyris G; Avloniti, Alexandra A; Tokmakidis, Savvas P

2008-03-01

To identify the physiological and anthropometric predictors of rhythmic gymnastics performance, which was defined from the total ranking score of each athlete in a national competition. Thirty-four rhythmic gymnasts were divided into 2 groups, elite (n = 15) and nonelite (n = 19), and they underwent a battery of anthropometric, physical fitness, and physiological measurements. The principal-components analysis extracted 6 components: anthropometric, flexibility, explosive strength, aerobic capacity, body dimensions, and anaerobic metabolism. These were used in a simultaneous multiple-regression procedure to determine which best explain the variance in rhythmic gymnastics performance. Based on the principal-component analysis, the anthropometric component explained 45% of the total variance, flexibility 12.1%, explosive strength 9.2%, aerobic capacity 7.4%, body dimensions 6.8%, and anaerobic metabolism 4.6%. Components of anthropometric (r = .50) and aerobic capacity (r = .49) were significantly correlated with performance (P < .01). When the multiple-regression model-y = 10.708 + (0.0005121 x VO2max) + (0.157 x arm span) + (0.814 x midthigh circumference) - (0.293 x body mass)-was applied to elite gymnasts, 92.5% of the variation was explained by VO2max (58.9%), arm span (12%), midthigh circumference (13.1%), and body mass (8.5%). Selected anthropometric characteristics, aerobic power, flexibility, and explosive strength are important determinants of successful performance. These findings might have practical implications for both training and talent identification in rhythmic gymnastics.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits.

PubMed

Fu, Liya; Wang, You-Gan

2011-02-15

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which clearly demonstrates the advantages of the rank regression models.

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Use of magnetic resonance imaging to predict the body composition of pigs in vivo.

PubMed

Kremer, P V; Förster, M; Scholz, A M

2013-06-01

The objective of the study was to evaluate whether magnetic resonance imaging (MRI) offers the opportunity to reliably analyze body composition of pigs in vivo. Therefore, the relation between areas of loin eye muscle and its back fat based on MRI images were used to predict body composition values measured by dual energy X-ray absorptiometry (DXA). During the study, a total of 77 pigs were studied by MRI and DXA, with a BW ranging between 42 and 102 kg. The pigs originated from different extensive or conventional breeds or crossbreds such as Cerdo Iberico, Duroc, German Landrace, German Large White, Hampshire and Pietrain. A Siemens Magnetom Open was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the loin eye area (MRI-LA) and the above back fat area (MRI-FA) of both body sides, whereas a whole body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (DXA-FM; DXA-%FM) and lean tissue mass (DXA-LM; DXA-%LM). A linear single regression analysis was performed to quantify the linear relationships between MRI- and DXA-derived traits. In addition, a stepwise regression procedure was carried out to calculate (multiple) regression equations between MRI and DXA variables (including BW). Single regression analyses showed high relationships between DXA-%FM and MRI-FA (R 2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R 2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R 2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R 2 = 0). As a result of the multiple regression analysis, DXA-LM and DXA-FM were both highly related to MRI-LA, MRI-FA and BW (R 2 = 0.96; √MSE = 1784 g, and R 2 = 0.95, √MSE = 1496 g). Therefore, it can be concluded that the use of MRI-derived images provides exact information about important 'carcass-traits' in pigs and may be used to reliably predict the body composition in vivo.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

Isolating the Effects of Training Using Simple Regression Analysis: An Example of the Procedure.

ERIC Educational Resources Information Center

Waugh, C. Keith

This paper provides a case example of simple regression analysis, a forecasting procedure used to isolate the effects of training from an identified extraneous variable. This case example focuses on results of a three-day sales training program to improve bank loan officers' knowledge, skill-level, and attitude regarding solicitation and sale of…

Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

ERIC Educational Resources Information Center

Elosua, Paula; Wells, Craig

2013-01-01

The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…

Comparison of energy expenditure to walk or run a mile in adult normal weight and overweight men and women.

PubMed

Loftin, Mark; Waddell, Dwight E; Robinson, James H; Owens, Scott G

2010-10-01

We compared the energy expenditure to walk or run a mile in adult normal weight walkers (NWW), overweight walkers (OW), and marathon runners (MR). The sample consisted of 19 NWW, 11 OW, and 20 MR adults. Energy expenditure was measured at preferred walking speed (NWW and OW) and running speed of a recently completed marathon. Body composition was assessed via dual-energy x-ray absorptiometry. Analysis of variance was used to compare groups with the Scheffe's procedure used for post hoc analysis. Multiple regression analysis was used to predict energy expenditure. Results that indicated OW exhibited significantly higher (p < 0.05) mass and fat weight than NWW or MR. Similar values were found between NWW and MR. Absolute energy expenditure to walk or run a mile was similar between groups (NWW 93.9 ± 15.0, OW 98.4 ± 29.9, MR 99.3 ± 10.8 kcal); however, significant differences were noted when energy expenditure was expressed relative to mass (MR > NWW > OW). When energy expenditure was expressed per kilogram of fat-free mass, similar values were found across groups. Multiple regression analysis yielded mass and gender as significant predictors of energy expenditure (R = 0.795, SEE = 10.9 kcal). We suggest that walking is an excellent physical activity for energy expenditure in overweight individuals that are capable of walking without predisposed conditions such as osteoarthritis or cardiovascular risk factors. Moreover, from a practical perspective, our regression equation (kcal = mass (kg) × 0.789 - gender (men = 1, women = 2) × 7.634 + 51.109) allows for the prediction of energy expenditure for a given distance (mile) rather than predicting energy expenditure for a given time (minutes).

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

PubMed

Riccardi, M; Mele, G; Pulvento, C; Lavini, A; d'Andria, R; Jacobsen, S-E

2014-06-01

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.

National Variation in Urethroplasty Cost and Predictors of Extreme Cost: A Cost Analysis With Policy Implications.

PubMed

Harris, Catherine R; Osterberg, E Charles; Sanford, Thomas; Alwaal, Amjad; Gaither, Thomas W; McAninch, Jack W; McCulloch, Charles E; Breyer, Benjamin N

2016-08-01

To determine which factors are associated with higher costs of urethroplasty procedure and whether these factors have been increasing over time. Identification of determinants of extreme costs may help reduce cost while maintaining quality. We conducted a retrospective analysis using the 2001-2010 Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS). The HCUP-NIS captures hospital charges which we converted to cost using the HCUP cost-to-charge ratio. Log cost linear regression with sensitivity analysis was used to determine variables associated with increased costs. Extreme cost was defined as the top 20th percentile of expenditure, analyzed with logistic regression, and expressed as odds ratios (OR). A total of 2298 urethroplasties were recorded in NIS over the study period. The median (interquartile range) calculated cost was $7321 ($5677-$10,000). Patients with multiple comorbid conditions were associated with extreme costs [OR 1.56, 95% confidence interval (CI) 1.19-2.04, P = .02] compared with patients with no comorbid disease. Inpatient complications raised the odds of extreme costs (OR 3.2, CI 2.14-4.75, P <.001). Graft urethroplasties were associated with extreme costs (OR 1.78, 95% CI 1.2-2.64, P = .005). Variations in patient age, race, hospital region, bed size, teaching status, payor type, and volume of urethroplasty cases were not associated with extremes of cost. Cost variation for perioperative inpatient urethroplasty procedures is dependent on preoperative patient comorbidities, postoperative complications, and surgical complexity related to graft usage. Procedural cost and cost variation are critical for understanding which aspects of care have the greatest impact on cost. Copyright © 2016 Elsevier Inc. All rights reserved.

National Variation in Urethroplasty Cost and Predictors of Extreme Cost: A Cost Analysis with Policy Implications

PubMed Central

Harris, Catherine R.; Osterberg, E. Charles; Sanford, Thomas; Alwaal, Amjad; Gaither, Thomas W.; McAninch, Jack W.; McCulloch, Charles E.; Breyer, Benjamin N.

2016-01-01

Objective To determine which factors are associated with higher urethroplasty procedural costs and whether they have been increasing or decreasing over time. Identification of determinants of extreme costs may help reduce cost while maintaining quality. Materials and Methods We conducted a retrospective analysis using the 2001–2010 Healthcare Cost and Utilization Project - Nationwide Inpatient Sample (HCUP-NIS). The HCUP-NIS captures hospital charges which we converted to cost using the HCUP Cost-to-Charge Ratio. Log cost linear regression with sensitivity analysis was used to determine variables associated with increased costs. Extreme cost was defined as the top 20th percentile of expenditure, analyzed with logistic regression and expressed as Odds Ratios (OR). Results A total of 2298 urethroplasties were recorded in NIS over the study period. The median (interquartile range) calculated costs was $7321 ($5677–$10000). Patients with multiple comorbid conditions were associated with extreme costs (OR 1.56 95% CI 1.19–2.04, p=0.02) compared to patients with no comorbid disease. Inpatient complications raised the odds of extreme costs OR 3.2 CI 2.14–4.75, p<0.001). Graft urethroplasties were associated with extreme costs (OR 1.78 95% CI 1.2–2.64, p=0.005). Variation in patient age, race, hospital region, bed size, teaching status, payer type, and volume of urethroplasty cases were not associated with extremes of cost. Conclusion Cost variation for perioperative inpatient urethroplasty procedures is dependent on preoperative patient comorbidities, postoperative complications and surgical complexity related to graft usage. Procedural cost and cost variation are critical for understanding which aspects of care have the greatest impact on cost. PMID:27107626

Factors associated with reduced radiation exposure, cost, and technical difficulty of inferior vena cava filter placement and retrieval.

PubMed

Neill, Matthew; Charles, Hearns W; Pflager, Daniel; Deipolyi, Amy R

2017-01-01

We sought to delineate factors of inferior vena cava filter placement associated with increased radiation and cost and difficult subsequent retrieval. In total, 299 procedures from August 2013 to December 2014, 252 in a fluoroscopy suite (FS) and 47 in the operating room (OR), were reviewed for radiation exposure, fluoroscopy time, filter type, and angulation. The number of retrieval devices and fluoroscopy time needed for retrieval were assessed. Multiple linear regression assessed the impact of filter type, procedure location, and patient and procedural variables on radiation dose, fluoroscopy time, and filter angulation. Logistic regression assessed the impact of filter angulation, type, and filtration duration on retrieval difficulty. Access site and filter type had no impact on radiation exposure. However, placement in the OR, compared to the FS, entailed more radiation (156.3 vs 71.4 mGy; P = 0.001), fluoroscopy time (6.1 vs 2.8 min; P < 0.001), and filter angulation (4.8° vs 2.6°; P < 0.001). Angulation was primarily dependent on filter type ( P = 0.02), with VenaTech and Denali filters associated with decreased angulation (2.2°, 2.4°) and Option filters associated with greater angulation (4.2°). Filter angulation, but not filter type or filtration duration, predicted cases requiring >1 retrieval device ( P < 0.001) and >30 min fluoroscopy time ( P = 0.02). Cost savings for placement in the FS vs OR were estimated at $444.50 per case. In conclusion, increased radiation and cost were associated with placement in the OR. Filter angulation independently predicted difficult filter retrieval; angulation was determined by filter type. Performing filter placement in the FS using specific filters may reduce radiation and cost while enabling future retrieval.

The M Word: Multicollinearity in Multiple Regression.

ERIC Educational Resources Information Center

Morrow-Howell, Nancy

1994-01-01

Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Controlling the Rate of GWAS False Discoveries

PubMed Central

Brzyski, Damian; Peterson, Christine B.; Sobczyk, Piotr; Candès, Emmanuel J.; Bogdan, Malgorzata; Sabatti, Chiara

2017-01-01

With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study. PMID:27784720

Controlling the Rate of GWAS False Discoveries.

PubMed

Brzyski, Damian; Peterson, Christine B; Sobczyk, Piotr; Candès, Emmanuel J; Bogdan, Malgorzata; Sabatti, Chiara

2017-01-01

With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study. Copyright © 2017 by the Genetics Society of America.

Relation of Post-Coronary Artery Bypass Graft Creatine Kinase-MB Elevations and New Q Waves With Long-Term Cardiovascular Death in Patients With Diabetes Mellitus and Multivessel Coronary Artery Disease.

PubMed

Domanski, Michael; Farkouh, Michael E; Zak, Victor; French, John; Alexander, John H; Bochenek, Andrzej; Hamon, Martial; Mahaffey, Kenneth; Puskas, John; Smith, Peter; Shrader, Peter; Fuster, Valentin

2016-12-01

Associations of early creatine phosphokinase-MB (CK-MB) elevation and new Q waves and their association with cardiovascular death (CVD) after coronary artery bypass grafting (CABG) have been reported, but this association has not been studied in a large population of patients with diabetes mellitus. In this study, we examine the association of periprocedural CK-MB elevations and new Q waves with CVD in the Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease trial. Cox proportional hazards regression was used to assess the relation of CK-MB elevations and new Q waves in the first 24 hours after procedure and their relation to CVD; logistic regression was used to assess odds ratios of these variables. Hazard ratios, 95% confidence intervals, and p values associated with Wald chi-square test are reported. CK-MB elevation in first 24 hours after procedure was independently associated with CVD. CVD hazard increased by 6% (p <0.001) with each multiple of CK-MB above the upper reference limit (URL); odds of new post-CABG Q waves increased by a factor of 1.08 (p <0.001); at 7× CK-MB URL, HR was >2. CK-MB URL multiples of 7, 12, and 15 were associated with new Q-wave odds ratios of 9, 16, and 27 times, respectively (p ≤0.001, C-statistic >0.70). New Q waves were independently associated with survival in the multivariate model only when CK-MB was excluded (p = 0.01). In conclusion, independent associations included (1) CVD and early post-CABG CK-MB elevation; (2) new Q waves with early post-CABG CK-MB elevation; (3) CVD with new Q waves only when CK-MB elevation is excluded from analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric

2016-01-01

Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Analyzing the effect of selected control policy measures and sociodemographic factors on alcoholic beverage consumption in Europe within the AMPHORA project: statistical methods.

PubMed

Baccini, Michela; Carreras, Giulia

2014-10-01

This paper describes the methods used to investigate variations in total alcoholic beverage consumption as related to selected control intervention policies and other socioeconomic factors (unplanned factors) within 12 European countries involved in the AMPHORA project. The analysis presented several critical points: presence of missing values, strong correlation among the unplanned factors, long-term waves or trends in both the time series of alcohol consumption and the time series of the main explanatory variables. These difficulties were addressed by implementing a multiple imputation procedure for filling in missing values, then specifying for each country a multiple regression model which accounted for time trend, policy measures and a limited set of unplanned factors, selected in advance on the basis of sociological and statistical considerations are addressed. This approach allowed estimating the "net" effect of the selected control policies on alcohol consumption, but not the association between each unplanned factor and the outcome.

Comprehensive model for predicting perceptual image quality of smart mobile devices.

PubMed

Gong, Rui; Xu, Haisong; Luo, M R; Li, Haifeng

2015-01-01

An image quality model for smart mobile devices was proposed based on visual assessments of several image quality attributes. A series of psychophysical experiments were carried out on two kinds of smart mobile devices, i.e., smart phones and tablet computers, in which naturalness, colorfulness, brightness, contrast, sharpness, clearness, and overall image quality were visually evaluated under three lighting environments via categorical judgment method for various application types of test images. On the basis of Pearson correlation coefficients and factor analysis, the overall image quality could first be predicted by its two constituent attributes with multiple linear regression functions for different types of images, respectively, and then the mathematical expressions were built to link the constituent image quality attributes with the physical parameters of smart mobile devices and image appearance factors. The procedure and algorithms were applicable to various smart mobile devices, different lighting conditions, and multiple types of images, and performance was verified by the visual data.

Improvement of the air quality in student health centers with chlorine dioxide.

PubMed

Hsu, Ching-Shan; Huang, Da-Ji; Lu, Ming-Chun

2010-04-01

This study aims to monitor bioaerosol levels of a local campus of a student health center in Taiwan and then to perform disinfection by applying chlorine dioxide. First, air samples were taken and evaluated in the six areas of the center. The average background bioaerosol levels were 714 +/- 1706 CFU/m(3) for bacterium and 802 +/- 633 CFU/m(3) for fungi. Then, chlorine dioxide was applied through three different procedures: single, multiple and regular disinfections. The results indicated that both multiple and regular disinfections can achieve efficiency levels higher than 59.0%. The regression analysis on bioaerosol levels showed that the number of people present correlating to the number of persons entering the room per door-opening, had a correlation of p < 0.05. Utilizing this analysis result, an empirical model was developed to predict indoor bioaerosol concentrations. It can be inferred that for indoor human activity of health centers, regular disinfection is a very effective process.

Multiple determinants of the abortion care experience: from the patient's perspective.

PubMed

Taylor, Diana; Postlethwaite, Debbie; Desai, Sheila; James, E Angel; Calhoun, Amanda W; Sheehan, Katharine; Weitz, Tracy A

2013-01-01

Because of the highly stigmatized nature of abortion care delivery and the restriction of abortion provision in most states, little is known about abortion care quality beyond procedural safety. This study examined which aspects of abortion care contributed to patient experiences. Data from a prospective, observational study of 9087 women aged 16 to 44 years, from 22 clinics across California, who responded to a postprocedure survey, were analyzed using mixed-effects logistic regression. Patient experience scores were very high (mean overall satisfaction = 9.4 [0-10 scale]) for all clinicians trained in abortion provision (physicians, nurse practitioners, nurse-midwives, and physician assistants). Multiple patient factors (pain rating, expectations of care, sociodemographics) and clinic-level factors (timely care, treatment by clinicians and staff) were significantly associated with patient experience. Study findings demonstrated that clinic environment, treatment by clinical staff, and managed pain levels contributed to a patient's experience of abortion care, whereas clinician type was not significantly associated.

Positive correlation of employment and psychological well-being for veterans with major abdominal surgery.

PubMed

Horner, David J; Wendel, Christopher S; Skeps, Raymond; Rawl, Susan M; Grant, Marcia; Schmidt, C Max; Ko, Clifford Y; Krouse, Robert S

2010-11-01

Intestinal stomas (ostomies) have been associated negatively with multiple aspects of health-related quality of life. This article examines the relationship between employment status and psychological well-being (PWB) in veterans who underwent major bowel procedures with or without ostomy. Veterans from 3 Veterans Affairs (VA) medical centers were surveyed using the City of Hope ostomy-specific questionnaire and the Short Form 36 item Veteran's version (SF-36V). Response rate was 48% (511 of 1,063). Employment and PWB relationship was assessed using multiple regression with age, income, SF-36V physical component summary (PCS), and employment status as independent variables. Employed veterans reported higher PWB compared with unemployed veterans (P = .003). Full-time workers also reported higher PWB than part-time or unemployed workers (P = .001). Ostomy was not an independent predictor of PWB. Employment among veterans after major abdominal surgery may have intrinsic value for PWB. Patients should be encouraged to return to work, or do volunteer work after recovery. Published by Elsevier Inc.

Modifying effect of cigarette smoking on the association of organizational justice with serious psychological distress in Japanese employees: a cross-sectional study.

PubMed

Inoue, Akiomi; Kawakami, Norito; Eguchi, Hisashi; Tsutsumi, Akizumi

2016-08-01

We examined the modifying effect of cigarette smoking (i.e., smokers vs. non-smokers) on the association of organizational justice (i.e., procedural justice and interactional justice) with serious psychological distress (SPD) in Japanese employees. Overall, 2838 participants from two factories of a manufacturing company in Japan completed a self-administered questionnaire comprising the scales on organizational justice (Organizational Justice Questionnaire), smoking status, psychological distress (K6 scale), demographic and occupational characteristics (i.e., gender, age, education, family size, history of depression, chronic physical conditions, occupation, and work form), and other health-related behaviors (i.e., drinking habit and physical activity). Multiple logistic regression analyses were conducted. In a series of analyses, interaction term of procedural justice or interactional justice with smoking status was included in the model. After adjusting for demographic and occupational characteristics as well as other health-related behaviors, low procedural justice and low interactional justice were significantly associated with SPD (defined as K6 ≥ 13). Furthermore, marginally significant interaction effect of procedural justice with smoking status was observed. Specifically, the association of low procedural justice with SPD was greater among smokers [prevalence odds ratio 7.13 (95 % confidence interval 3.25-15.7) for low vs. high procedural justice subgroup] than among non-smokers [prevalence odds ratio 2.34 (95 % confidence interval 1.52-3.60) for low vs. high procedural justice subgroup]. On the other hand, interaction effect of interactional justice with smoking status was not significant. Cigarette smoking seems to have a harmful effect on the association of the lack of procedural justice with SPD in Japanese employees.

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

PubMed

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

Estimating design-flood discharges for streams in Iowa using drainage-basin and channel-geometry characteristics

USGS Publications Warehouse

Eash, D.A.

1993-01-01

Procedures provided for applying the drainage-basin and channel-geometry regression equations depend on whether the design-flood discharge estimate is for a site on an ungaged stream, an ungaged site on a gaged stream, or a gaged site. When both a drainage-basin and a channel-geometry regression-equation estimate are available for a stream site, a procedure is presented for determining a weighted average of the two flood estimates. The drainage-basin regression equations are applicable to unregulated rural drainage areas less than 1,060 square miles, and the channel-geometry regression equations are applicable to unregulated rural streams in Iowa with stabilized channels.

Regression methods for spatially correlated data: an example using beetle attacks in a seed orchard

Treesearch

Preisler Haiganoush; Nancy G. Rappaport; David L. Wood

1997-01-01

We present a statistical procedure for studying the simultaneous effects of observed covariates and unmeasured spatial variables on responses of interest. The procedure uses regression type analyses that can be used with existing statistical software packages. An example using the rate of twig beetle attacks on Douglas-fir trees in a seed orchard illustrates the...

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

Kwon, Deukwoo; Little, Mark P.; Miller, Donald L.

Purpose: To determine more accurate regression formulas for estimating peak skin dose (PSD) from reference air kerma (RAK) or kerma-area product (KAP). Methods: After grouping of the data from 21 procedures into 13 clinically similar groups, assessments were made of optimal clustering using the Bayesian information criterion to obtain the optimal linear regressions of (log-transformed) PSD vs RAK, PSD vs KAP, and PSD vs RAK and KAP. Results: Three clusters of clinical groups were optimal in regression of PSD vs RAK, seven clusters of clinical groups were optimal in regression of PSD vs KAP, and six clusters of clinical groupsmore » were optimal in regression of PSD vs RAK and KAP. Prediction of PSD using both RAK and KAP is significantly better than prediction of PSD with either RAK or KAP alone. The regression of PSD vs RAK provided better predictions of PSD than the regression of PSD vs KAP. The partial-pooling (clustered) method yields smaller mean squared errors compared with the complete-pooling method.Conclusion: PSD distributions for interventional radiology procedures are log-normal. Estimates of PSD derived from RAK and KAP jointly are most accurate, followed closely by estimates derived from RAK alone. Estimates of PSD derived from KAP alone are the least accurate. Using a stochastic search approach, it is possible to cluster together certain dissimilar types of procedures to minimize the total error sum of squares.« less

MOST: most-similar ligand based approach to target prediction.

PubMed

Huang, Tao; Mi, Hong; Lin, Cheng-Yuan; Zhao, Ling; Zhong, Linda L D; Liu, Feng-Bin; Zhang, Ge; Lu, Ai-Ping; Bian, Zhao-Xiang

2017-03-11

Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction. Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

MULTIPLE REGRESSION MODELS FOR HINDCASTING AND FORECASTING MIDSUMMER HYPOXIA IN THE GULF OF MEXICO

EPA Science Inventory

A new suite of multiple regression models were developed that describe the relationship between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Variabil...

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

Logistic Regression Likelihood Ratio Test Analysis for Detecting Signals of Adverse Events in Post-market Safety Surveillance.

PubMed

Nam, Kijoeng; Henderson, Nicholas C; Rohan, Patricia; Woo, Emily Jane; Russek-Cohen, Estelle

2017-01-01

The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics. Though approaches such as these have proven to be useful, in this article we propose a more flexible logistic regression approach which allows for covariates of all types rather than relying solely on stratification. Indeed, a main advantage of our approach is that the general regression framework provides flexibility to incorporate additional information such as demographic factors and concomitant vaccines. As part of our covariate-adjusted method, we outline a procedure for signal detection that accounts for multiple comparisons and controls the overall Type 1 error rate. To demonstrate the effectiveness of our approach, we illustrate our method with an example involving febrile convulsion, and we further evaluate its performance in a series of simulation studies.

29 CFR 1926.761 - Training.

Code of Federal Regulations, 2010 CFR

2010-07-01

... following activities. (1) Multiple lift rigging procedure. The employer shall ensure that each employee who performs multiple lift rigging has been provided training in the following areas: (i) The nature of the hazards associated with multiple lifts; and (ii) The proper procedures and equipment to perform multiple...

29 CFR 1926.761 - Training.

Code of Federal Regulations, 2011 CFR

2011-07-01

... following activities. (1) Multiple lift rigging procedure. The employer shall ensure that each employee who performs multiple lift rigging has been provided training in the following areas: (i) The nature of the hazards associated with multiple lifts; and (ii) The proper procedures and equipment to perform multiple...

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

PubMed

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Undergraduate Student Motivation in Modularized Developmental Mathematics Courses

ERIC Educational Resources Information Center

Pachlhofer, Keith A.

2017-01-01

This study used the Motivated Strategies for Learning Questionnaire in modularized courses at three institutions across the nation (N = 189), and multiple regression was completed to investigate five categories of student motivation that predicted academic success and course completion. The overall multiple regression analysis was significant and…

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

Categorical Variables in Multiple Regression: Some Cautions.

ERIC Educational Resources Information Center

O'Grady, Kevin E.; Medoff, Deborah R.

1988-01-01

Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)

The moderator-mediator role of social support in early adolescents.

PubMed

Yarcheski, A; Mahon, N E

1999-10-01

The purpose of this study was to examine social support as both a mediator and a moderator of the relationship between perceived stress and symptom patterns in early adolescents. Data were collected from 148 early adolescent boys and girls, ages 12 to 14, who responded to the Perceived Stress Scale, the Personal Resource Questionnaire 85-Part II, and the Symptom Pattern Scale. Using multiple regression analysis procedures specified for the testing of moderation and mediation, results indicated that social support did not play a moderating role in the relationship between perceived stress and symptom patterns, but social support did play a mediating role in this relationship. The findings are interpreted within the two major theoretical orientations that guided the study.

Memory for self-generated narration in the elderly.

PubMed

Drevenstedt, J; Bellezza, F S

1993-06-01

The story mnemonic technique, an effective encoding and retrieval strategy for young adults, was used as a procedure to study encoding and recall in elderly women. Experiment 1 (15 undergraduate and 14 elderly women) showed the technique to be reliable over 3 weeks and without practice effects in both age groups. In Experiment 2, 67 elderly women (mean age = 72 years) were found to make up 3 distinctive subgroupings in patterns of narration cohesiveness and recall accuracy, consistent with pilot data on the technique. A stepwise multiple regression equation found narration cohesiveness, an adaptation of the Daneman-Carpenter (1980) working-memory measure and vocabulary to predict word recall. Results suggested that a general memory factor differentiated the 3 elderly subgroups.

Learning the facts in medical school is not enough: which factors predict successful application of procedural knowledge in a laboratory setting?

PubMed Central

2013-01-01

Background Medical knowledge encompasses both conceptual (facts or “what” information) and procedural knowledge (“how” and “why” information). Conceptual knowledge is known to be an essential prerequisite for clinical problem solving. Primarily, medical students learn from textbooks and often struggle with the process of applying their conceptual knowledge to clinical problems. Recent studies address the question of how to foster the acquisition of procedural knowledge and its application in medical education. However, little is known about the factors which predict performance in procedural knowledge tasks. Which additional factors of the learner predict performance in procedural knowledge? Methods Domain specific conceptual knowledge (facts) in clinical nephrology was provided to 80 medical students (3rd to 5th year) using electronic flashcards in a laboratory setting. Learner characteristics were obtained by questionnaires. Procedural knowledge in clinical nephrology was assessed by key feature problems (KFP) and problem solving tasks (PST) reflecting strategic and conditional knowledge, respectively. Results Results in procedural knowledge tests (KFP and PST) correlated significantly with each other. In univariate analysis, performance in procedural knowledge (sum of KFP+PST) was significantly correlated with the results in (1) the conceptual knowledge test (CKT), (2) the intended future career as hospital based doctor, (3) the duration of clinical clerkships, and (4) the results in the written German National Medical Examination Part I on preclinical subjects (NME-I). After multiple regression analysis only clinical clerkship experience and NME-I performance remained independent influencing factors. Conclusions Performance in procedural knowledge tests seems independent from the degree of domain specific conceptual knowledge above a certain level. Procedural knowledge may be fostered by clinical experience. More attention should be paid to the interplay of individual clinical clerkship experiences and structured teaching of procedural knowledge and its assessment in medical education curricula. PMID:23433202

Radiation exposure of the anesthesiologist in the neurointerventional suite.

PubMed

Anastasian, Zirka H; Strozyk, Dorothea; Meyers, Philip M; Wang, Shuang; Berman, Mitchell F

2011-03-01

Scatter radiation during interventional radiology procedures can produce cataracts in participating medical personnel. Standard safety equipment for the radiologist includes eye protection. The typical configuration of fluoroscopy equipment directs radiation scatter away from the radiologist and toward the anesthesiologist. This study analyzed facial radiation exposure of the anesthesiologist during interventional neuroradiology procedures. Radiation exposure to the forehead of the anesthesiologist and radiologist was measured during 31 adult neuroradiologic procedures involving the head or neck. Variables hypothesized to affect anesthesiologist exposure were recorded for each procedure. These included total radiation emitted by fluoroscopic equipment, radiologist exposure, number of pharmacologic interventions performed by the anesthesiologist, and other variables. Radiation exposure to the anesthesiologist's face averaged 6.5 ± 5.4 μSv per interventional procedure. This exposure was more than 6-fold greater (P < 0.0005) than for noninterventional angiographic procedures (1.0 ± 1.0) and averaged more than 3-fold the exposure of the radiologist (ratio, 3.2; 95% CI, 1.8-4.5). Multiple linear regression analysis showed that the exposure of the anesthesiologist was correlated with the number of pharmacologic interventions performed by the anesthesiologist and the total exposure of the radiologist. Current guidelines for occupational radiation exposure to the eye are undergoing review and are likely to be lowered below the current 100-150 mSv/yr limit. Anesthesiologists who spend significant time in neurointerventional radiology suites may have ocular radiation exposure approaching that of a radiologist. To ensure parity with safety standards adopted by radiologists, these anesthesiologists should wear protective eyewear.

The effects of normal aging on multiple aspects of financial decision-making

PubMed Central

Bangma, Dorien F.; Fuermaier, Anselm B. M.; Tucha, Lara; Tucha, Oliver; Koerts, Janneke

2017-01-01

Objectives Financial decision-making (FDM) is crucial for independent living. Due to cognitive decline that accompanies normal aging, older adults might have difficulties in some aspects of FDM. However, an improved knowledge, personal experience and affective decision-making, which are also related to normal aging, may lead to a stable or even improved age-related performance in some other aspects of FDM. Therefore, the present explorative study examines the effects of normal aging on multiple aspects of FDM. Methods One-hundred and eighty participants (range 18–87 years) were assessed with eight FDM tests and several standard neuropsychological tests. Age effects were evaluated using hierarchical multiple regression analyses. The validity of the prediction models was examined by internal validation (i.e. bootstrap resampling procedure) as well as external validation on another, independent, sample of participants (n = 124). Multiple regression and correlation analyses were applied to investigate the mediation effect of standard measures of cognition on the observed effects of age on FDM. Results On a relatively basic level of FDM (e.g., paying bills or using FDM styles) no significant effects of aging were found. However more complex FDM, such as making decisions in accordance with specific rules, becomes more difficult with advancing age. Furthermore, an older age was found to be related to a decreased sensitivity for impulsive buying. These results were confirmed by the internal and external validation analyses. Mediation effects of numeracy and planning were found to explain parts of the association between one aspect of FDM (i.e. Competence in decision rules) and age; however, these cognitive domains were not able to completely explain the relation between age and FDM. Conclusion Normal aging has a negative influence on a complex aspect of FDM, however, other aspects appear to be unaffected by normal aging or improve. PMID:28792973

Advanced Statistics for Exotic Animal Practitioners.

PubMed

Hodsoll, John; Hellier, Jennifer M; Ryan, Elizabeth G

2017-09-01

Correlation and regression assess the association between 2 or more variables. This article reviews the core knowledge needed to understand these analyses, moving from visual analysis in scatter plots through correlation, simple and multiple linear regression, and logistic regression. Correlation estimates the strength and direction of a relationship between 2 variables. Regression can be considered more general and quantifies the numerical relationships between an outcome and 1 or multiple variables in terms of a best-fit line, allowing predictions to be made. Each technique is discussed with examples and the statistical assumptions underlying their correct application. Copyright © 2017 Elsevier Inc. All rights reserved.

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

Horn, E. M.; Morrissey, L. A.

1984-01-01

The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.

Predictors of surgical site infection in laparoscopic and open ventral incisional herniorrhaphy.

PubMed

Kaafarani, Haytham M A; Kaufman, Derrick; Reda, Domenic; Itani, Kamal M F

2010-10-01

Surgical site infection (SSI) after ventral incisional hernia repair (VIH) can result in serious consequences. We sought to identify patient, procedure, and/or hernia characteristics that are associated with SSI in VIH. Between 2004 and 2006, patients were randomized in four Veteran Affairs (VA) hospitals to undergo laparoscopic or open VIH. Patients who developed SSI within eight weeks postoperatively were compared to those who did not. A bivariate analysis for each factor and a multiple logistic regression analysis were performed to determine factors associated with SSI. The variables studied included patient characteristics and co-morbidities (e.g., age, gender, race, ethnicity, body mass index, ASA classification, diabetes, steroid use), hernia characteristics (e.g., size, duration, number of previous incisions), procedure characteristics (e.g., open versus laparoscopic, blood loss, use of postoperative drains, operating room temperature) and surgeons' experience (resident training level, number of open VIH previously performed by the attending surgeon). Antibiotic prophylaxis, anticoagulation protocols, preparation of the skin, draping of the wound, body temperature control, and closure of the surgical site were all standardized and monitored throughout the study period. Out of 145 patients who underwent VIH, 21 developed a SSI (14.5%). Patients who underwent open VIH had significantly more SSIs than those who underwent laparoscopic VIH (22.1% versus 3.4%; P = 0.002). Among patients who underwent open VIH, those who developed SSI had a recorded intraoperative blood loss greater than 25 mL (68.4% versus 40.3%; P = 0.030), were more likely to have a drain placed (79.0% versus 49.3%; P = 0.021) and were more likey to be operated on by surgeons with less than 75 open VIH case experience (52.6% versus 28.4%; P = 0.048). Patient and hernia characteristics were similar between the two groups. In a multiple logistic regression analysis, the open surgical technique was associated with SSI (OR 8.03, 95% CI 2.03, 31.72; P = 0.003) while controlling for the VA medical center where the procedure was performed (P = 0.041). Open surgical technique and the medical center rather than patient co-morbidities or hernia characteristics are associated with the formation of postoperative SSI in VIH. Published by Elsevier Inc.

Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling

PubMed Central

Sinnott, Jennifer A.; Cai, Tianxi

2013-01-01

Summary Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai et al., 2011). In this paper, we derive testing and prediction methods for KM regression under the accelerated failure time model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. PMID:24328713

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

EPA Science Inventory

Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

Analysis and Interpretation of Findings Using Multiple Regression Techniques

ERIC Educational Resources Information Center

Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

2006-01-01

Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

Tracking the Gender Pay Gap: A Case Study

ERIC Educational Resources Information Center

Travis, Cheryl B.; Gross, Louis J.; Johnson, Bruce A.

2009-01-01

This article provides a short introduction to standard considerations in the formal study of wages and illustrates the use of multiple regression and resampling simulation approaches in a case study of faculty salaries at one university. Multiple regression is especially beneficial where it provides information on strength of association, specific…

Estimating air drying times of lumber with multiple regression

Treesearch

William T. Simpson

2004-01-01

In this study, the applicability of a multiple regression equation for estimating air drying times of red oak, sugar maple, and ponderosa pine lumber was evaluated. The equation allows prediction of estimated air drying times from historic weather records of temperature and relative humidity at any desired location.

Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

Multiple Regression: A Leisurely Primer.

ERIC Educational Resources Information Center

Daniel, Larry G.; Onwuegbuzie, Anthony J.

Multiple regression is a useful statistical technique when the researcher is considering situations in which variables of interest are theorized to be multiply caused. It may also be useful in those situations in which the researchers is interested in studies of predictability of phenomena of interest. This paper provides an introduction to…

Using Monte Carlo Techniques to Demonstrate the Meaning and Implications of Multicollinearity

ERIC Educational Resources Information Center

Vaughan, Timothy S.; Berry, Kelly E.

2005-01-01

This article presents an in-class Monte Carlo demonstration, designed to demonstrate to students the implications of multicollinearity in a multiple regression study. In the demonstration, students already familiar with multiple regression concepts are presented with a scenario in which the "true" relationship between the response and…

Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

ERIC Educational Resources Information Center

Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

1999-01-01

A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

Racial and gender variation in use of diagnostic colonic procedures in the Michigan Medicare population.

PubMed

McMahon, L F; Wolfe, R A; Huang, S; Tedeschi, P; Manning, W; Edlund, M J

1999-07-01

There is accumulating evidence that screening programs can alter the natural history of colorectal cancer, a significant cause of mortality and morbidity in the US. Understanding how the technology to diagnose colonic diseases is utilized in the population provides insight into both the access and processes of care. Using Medicare Part B billing files from the state of Michigan from 1986 to 1989 we identified all procedures used to diagnose colorectal disease. We utilized the Medicare Beneficiary File and the Area Resource File to identify beneficiary-specific and community-sociodemographic characteristics. The beneficiary and sociodemographic characteristics were, then, used in multiple regression analyses to identify their association with procedure utilization. Sigmoidoscopic use declined dramatically with the increasing age cohorts of Medicare beneficiaries. Urban areas and communities with higher education levels had more sigmoidoscopic use. Among procedures used to examine the entire colon, isolated barium enema was used more frequently in African Americans, the elderly, and females. The combination of barium enema and sigmoidoscopy was used more frequently among females and the newest technology, colonoscopy, was used most frequently among White males. The existence of race, gender, and socioeconomic disparities in the use of colorectal technologies in a group of patients with near-universal insurance coverage demonstrates the necessity of understanding the reason(s) for these observed differences to improve access to appropriate technologies to all segments in our society.

Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

ERIC Educational Resources Information Center

Bulcock, J. W.

The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

A Heckman selection model for the safety analysis of signalized intersections

PubMed Central

Wong, S. C.; Zhu, Feng; Pei, Xin; Huang, Helai; Liu, Youjun

2017-01-01

Purpose The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. PMID:28732050

Calculating stage duration statistics in multistage diseases.

PubMed

Komarova, Natalia L; Thalhauser, Craig J

2011-01-01

Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed.

Actual and estimated costs of disposable materials used during surgical procedures.

PubMed

Toyabe, Shin-Ichi; Cao, Pengyu; Kurashima, Sachiko; Nakayama, Yukiko; Ishii, Yuko; Hosoyama, Noriko; Akazawa, Kouhei

2005-07-01

It is difficult to estimate precisely the costs of disposable materials used during surgical operations. To evaluate the actual costs of disposable materials, we calculated the actual costs of disposable materials used in 59 operations by taking account of costs of all disposable materials used for each operation. The costs of the disposable materials varied significantly from operation to operation (US$ 38-4230 per operation), and the median [25-percentile and 75-percentile] of the sum total of disposable material costs of a single operation was found to be US$ 686 [205 and 993]. Multiple regression analysis with a stepwise regression method showed that costs of disposable materials significantly correlated only with operation time (p<0.001). Based on the results, we propose a simple method for estimating costs of disposable materials by measuring operation time, and we found that the method gives reliable results. Since costs of disposable materials used during surgical operations are considerable, precise estimation of the costs is essential for hospital cost accounting. Our method should be useful for planning hospital administration strategies.

Effectiveness of percutaneous vertebroplasty in patients with multiple myeloma having vertebral pain

PubMed Central

Nas, Ömer Fatih; İnecikli, Mehmet Fatih; Hacıkurt, Kadir; Büyükkaya, Ramazan; Özkaya, Güven; Özkalemkaş, Fahir; Ali, Rıdvan; Erdoğan, Cüneyt; Hakyemez, Bahattin

2016-01-01

PURPOSE We aimed to assess the effectiveness, benefits, and reliability of percutaneous vertebroplasty (PV) in patients with vertebral involvement of multiple myeloma. METHODS PV procedures performed on 166 vertebrae of 41 patients with multiple myeloma were retrospectively evaluated. Most of our patients were using level 3 (moderate to severe pain) analgesics. Magnetic resonance imaging was performed before the procedure to assess vertebral involvement of multiple myeloma. The following variables were evaluated: affected vertebral levels, loss of vertebral body height, polymethylmethacrylate (PMMA) cement amount applied to the vertebral body during PV, PMMA cement leakages, and pain before and after PV as assessed by a visual analogue scale (VAS). RESULTS Median VAS scores of patients decreased from 9 one day before PV, to 6 one day after the procedure, to 3 one week after the procedure, and eventually to 1 three months after the procedure (P < 0.001). During the PV procedure, cement leakage was observed at 68 vertebral levels (41%). The median value of PMMA applied to the vertebral body was 6 mL. CONCLUSION Being a minimally invasive and easily performed procedure with low complication rates, PV should be preferred for serious back pain of multiple myeloma patients. PMID:26912107

34 CFR 75.224 - What are the procedures for using a multiple tier review process to evaluate applications?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 34 Education 1 2010-07-01 2010-07-01 false What are the procedures for using a multiple tier... applications received. (d) The Secretary may, in any tier— (1) Use more than one group of experts to gain... procedures for using a multiple tier review process to evaluate applications? (a) The Secretary may use a...

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Preoperative immobility significantly impacts the risk of postoperative complications in bariatric surgery patients.

PubMed

Higgins, Rana M; Helm, Melissa; Gould, Jon C; Kindel, Tammy L

2018-06-01

Preoperative immobility in general surgery patients has been associated with an increased risk of postoperative complications. It is unknown if immobility affects bariatric surgery outcomes. The aim of this study was to determine the impact of immobility on 30-day postoperative bariatric surgery outcomes. This study took place at a university hospital in the United States. The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program 2015 data set was queried for primary minimally invasive bariatric procedures. Preoperative immobility was defined as limited ambulation most or all the time. Logistic regression analysis was performed to determine if immobile patients are at increased risk (odds ratio [OR]) for 30-day complications. There were 148,710 primary minimally invasive bariatric procedures in 2015. Immobile patients had an increased risk of mortality (OR 4.59, P<.001) and greater operative times, length of stay, reoperation rates, and readmissions. Immobile patients had a greater risk of multiple complications, including acute renal failure (OR 6.42, P<.001), pulmonary embolism (OR 2.44, P = .01), cardiac arrest (OR 2.81, P = .05), and septic shock (OR 2.78, P = .02). Regardless of procedure type, immobile patients had a higher incidence of perioperative morbidity compared with ambulatory patients. This study is the first to specifically assess the impact of immobility on 30-day bariatric surgery outcomes. Immobile patients have a significantly increased risk of morbidity and mortality. This study provides an opportunity for the development of multiple quality initiatives to improve the safety and perioperative complication profile for immobile patients undergoing bariatric surgery. Copyright © 2018 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Factors affecting healing and survival after finger amputations in patients with digital artery occlusive disease.

PubMed

Landry, Gregory J; McClary, Ashley; Liem, Timothy K; Mitchell, Erica L; Azarbal, Amir F; Moneta, Gregory L

2013-05-01

Finger amputations are typically performed as distal as possible to preserve maximum finger length. Failure of primary amputation leads to additional procedures, which could potentially be avoided if a more proximal amputation was initially performed. The effect of single versus multiple procedures on morbidity and mortality is not known. We evaluated factors that predicted primary healing and the effects of secondary procedures on survival. Patients undergoing finger amputations from 1995 to 2011 were evaluated for survival with uni- and multivariate analysis of demographic data and preoperative vascular laboratory studies to assess factors influencing primary healing. Seventy-six patients underwent 175 finger amputations (range 1 to 6 fingers per patient). Forty-one percent had diabetes, 33% had nonatherosclerotic digital artery disease, and 29% were on dialysis. Sex distribution was equal. Primary healing occurred in 78.9%, with the remainder requiring revisions. By logistic regression analysis, nonatherosclerotic digital artery disease was associated with failure of primary healing (odds ratio = 7.5; 95% confidence interval, 1.03 to 54; P = .047). Digital photoplethysmography did not predict primary healing. The overall healing of primary and secondary finger amputations was 96.0%. The mean survival after the initial finger amputation was 34.3 months and did not differ between patients undergoing single (35.6 months) versus multiple procedures (33.6 months). Dialysis dependence was associated with decreased survival (hazard ratio = 2.9; 95% confidence interval, 1.13 to 7.25; P = .026). Failure of primary healing is associated with the presence of nonatherosclerotic digital artery disease and is not predicted by digital photoplethysmographic studies. Dialysis dependence is associated with decreased survival in patients with finger amputations, but failure of primary healing does not adversely affect survival. A strategy of aggressive preservation of finger length is appropriate for most patients. Copyright © 2013. Published by Elsevier Inc.

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

Iguchi, Toshihiro, E-mail: iguchi@ba2.so-net.ne.jp; Hiraki, Takao, E-mail: takaoh@tc4.so-net.ne.jp; Gobara, Hideo, E-mail: gobara@cc.okayama-u.ac.jp

PurposeThe aim of the study was to retrospectively evaluate simultaneous multiple hook wire placement outcomes before video-assisted thoracoscopic surgery (VATS).Materials and MethodsThirty-eight procedures were performed on 35 patients (13 men and 22 women; mean age, 59.9 years) with 80 lung lesions (mean diameter 7.9 mm) who underwent simultaneous multiple hook wire placements for preoperative localizations. The primary endpoints were technical success, complications, procedure duration, and VATS outcome; secondary endpoints included comparisons between technical success rates, complication rates, and procedure durations of the 238 single-placement procedures performed. Complications were also evaluated.ResultsIn 35 procedures including 74 lesions, multiple hook wire placements were technically successful;more » in the remaining three procedures, the second target placement was aborted because of massive pneumothorax after the first placement. Although complications occurred in 34 procedures, no grade 3 or above adverse event was observed. The mean procedure duration was 36.4 ± 11.8 min. Three hook wires dislodged during patient transport to the surgical suite. Seventy-four successfully marked lesions were resected. Six lesions without hook wires were successfully resected after detection by palpation with an additional mini-thoracotomy or using subtle pleural changes as a guide. The complication rates and procedure durations of multiple-placement procedures were significantly higher (P = 0.04) and longer (P < 0.001) than those in the single-placement group, respectively, while the technical success rate was not significantly different (P = 0.051).ConclusionsSimultaneous multiple hook wire placements before VATS were clinically feasible, but increased the complication rate and lengthened the procedure time.« less

Influence of Temporal Context on Value in the Multiple-Chains and Successive-Encounters Procedures

ERIC Educational Resources Information Center

O'Daly, Matthew; Angulo, Samuel; Gipson, Cassandra; Fantino, Edmund

2006-01-01

This set of studies explored the influence of temporal context across multiple-chain and multiple-successive-encounters procedures. Following training with different temporal contexts, the value of stimuli sharing similar reinforcement schedules was assessed by presenting these stimuli in concurrent probes. The results for the multiple-chain…

A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

NASA Astrophysics Data System (ADS)

Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

2017-08-01

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

The Moderating Role of Power Distance on the Relationship between Employee Participation and Outcome Variables.

PubMed

Rafiei, Sima; Pourreza, Abolghasem

2013-06-01

Many organisations have realised the importance of human resource for their competitive advantage. Empowering employees is therefore essential for organisational effectiveness. This study aimed to investigate the relationship between employee participation with outcome variables such as organisational commitment, job satisfaction, perception of justice in an organisation and readiness to accept job responsibilities. It further examined the impact of power distance on the relationship between participation and four outcome variables. This was a cross sectional study with a descriptive research design conducted among employees and managers of hospitals affiliated with Tehran University of Medical Sciences, Tehran, Iran. A questionnaire as a main procedure to gather data was developed, distributed and collected. Descriptive statistics, Pearson correlation coefficient and moderated multiple regression were used to analyse the study data. Findings of the study showed that the level of power distance perceived by employees had a significant relationship with employee participation, organisational commitment, job satisfaction, perception of justice and readiness to accept job responsibilities. There was also a significant relationship between employee participation and four outcome variables. The moderated multiple regression results supported the hypothesis that power distance had a significant effect on the relationship between employee participation and four outcome variables. Organisations in which employee empowerment is practiced through diverse means such as participating them in decision making related to their field of work, appear to have more committed and satisfied employees with positive perception toward justice in the organisational interactions and readiness to accept job responsibilities.

Development of efficiency indicators of operating room management for multi-institutional comparisons.

PubMed

Tanaka, Masayuki; Lee, Jason; Ikai, Hiroshi; Imanaka, Yuichi

2013-04-01

The efficiency of a hospital's operating room (OR) management can affect its overall profitability. However, existing indicators that assess OR management efficiency do not take into account differences in hospital size, manpower and functional characteristics, thereby rendering them unsuitable for multi-institutional comparisons. The aim of this study was to develop indicators of OR management efficiency that would take into account differences in hospital size and manpower, which may then be applied to multi-institutional comparisons. Using administrative data from 224 hospitals in Japan from 2008 to 2010, we performed four multiple linear regression analyses at the hospital level, in which the dependent variables were the number of operations per OR per month, procedural fees per OR per month, total utilization times per OR per month and total fees per OR per month for each of the models. The expected values of these four indicators were produced using multiple regression analysis results, adjusting for differences in hospital size and manpower, which are beyond the control of process owners' management. However, more than half of the variations in three of these four indicators were shown to be explained by differences in hospital size and manpower. Using the ratio of observed to expected values (OE ratio), as well as the difference between the two values (OE difference) allows hospitals to identify weaknesses in efficiency with more validity when compared to unadjusted indicators. The new indicators may support the improvement and sustainment of a high-quality health care system. © 2012 Blackwell Publishing Ltd.

Relationship of early-life stress and resilience to military adjustment in a young adulthood population.

PubMed

Choi, Kang; Im, Hyoungjune; Kim, Joohan; Choi, Kwang H; Jon, Duk-In; Hong, Hyunju; Hong, Narei; Lee, Eunjung; Seok, Jeong-Ho

2013-11-01

Early-life stress (ELS) may mediate adjustment problems while resilience may protect individuals against adjustment problems during military service. We investigated the relationship of ELS and resilience with adjustment problem factor scores in the Korea Military Personality Test (KMPT) in candidates for the military service. Four hundred and sixty-one candidates participated in this study. Vulnerability traits for military adjustment, ELS, and resilience were assessed using the KMPT, the Korean Early-Life Abuse Experience Questionnaire, and the Resilience Quotient Test, respectively. Data were analyzed using multiple linear regression analyses. The final model of the multiple linear regression analyses explained 30.2 % of the total variances of the sum of the adjustment problem factor scores of the KMPT. Neglect and exposure to domestic violence had a positive association with the total adjustment problem factor scores of the KMPT, but emotion control, impulse control, and optimism factor scores as well as education and occupational status were inversely associated with the total military adjustment problem score. ELS and resilience are important modulating factors in adjusting to military service. We suggest that neglect and exposure to domestic violence during early life may increase problem with adjustment, but capacity to control emotion and impulse as well as optimistic attitude may play protective roles in adjustment to military life. The screening procedures for ELS and the development of psychological interventions may be helpful for young adults to adjust to military service.

Factors Predicting a Good Symptomatic Outcome After Prostate Artery Embolisation (PAE).

PubMed

Maclean, D; Harris, M; Drake, T; Maher, B; Modi, S; Dyer, J; Somani, B; Hacking, N; Bryant, T

2018-02-26

As prostate artery embolisation (PAE) becomes an established treatment for benign prostatic obstruction, factors predicting good symptomatic outcome remain unclear. Pre-embolisation prostate size as a predictor is controversial with a handful of papers coming to conflicting conclusions. We aimed to investigate if an association existed in our patient cohort between prostate size and clinical benefit, in addition to evaluating percentage volume reduction as a predictor of symptomatic outcome following PAE. Prospective follow-up of 86 PAE patients at a single institution between June 2012 and January 2016 was conducted (mean age 64.9 years, range 54-80 years). Multiple linear regression analysis was performed to assess strength of association between clinical improvement (change in IPSS) and other variables, of any statistical correlation, through Pearson's bivariate analysis. No major procedural complications were identified and clinical success was achieved in 72.1% (n = 62) at 12 months. Initial prostate size and percentage reduction were found to have a significant association with clinical improvement. Multiple linear regression analysis (r 2  = 0.48) demonstrated that percentage volume reduction at 3 months (r = 0.68, p < 0.001) had the strongest correlation with good symptomatic improvement at 12 months after adjusting for confounding factors. Both the initial prostate size and percentage volume reduction at 3 months predict good symptomatic outcome at 12 months. These findings therefore aid patient selection and counselling to achieve optimal outcomes for men undergoing prostate artery embolisation.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

ERIC Educational Resources Information Center

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

An Exploratory Study of Face-to-Face and Cyberbullying in Sixth Grade Students

ERIC Educational Resources Information Center

Accordino, Denise B.; Accordino, Michael P.

2011-01-01

In a pilot study, sixth grade students (N = 124) completed a questionnaire assessing students' experience with bullying and cyberbullying, demographic information, quality of parent-child relationship, and ways they have dealt with bullying/cyberbullying in the past. Two multiple regression analyses were conducted. The multiple regression analysis…

The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

The Use of Multiple Regression Models to Determine if Conjoint Analysis Should Be Conducted on Aggregate Data.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

1996-01-01

In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…

An appraisal of statistical procedures used in derivation of reference intervals.

PubMed

Ichihara, Kiyoshi; Boyd, James C

2010-11-01

When conducting studies to derive reference intervals (RIs), various statistical procedures are commonly applied at each step, from the planning stages to final computation of RIs. Determination of the necessary sample size is an important consideration, and evaluation of at least 400 individuals in each subgroup has been recommended to establish reliable common RIs in multicenter studies. Multiple regression analysis allows identification of the most important factors contributing to variation in test results, while accounting for possible confounding relationships among these factors. Of the various approaches proposed for judging the necessity of partitioning reference values, nested analysis of variance (ANOVA) is the likely method of choice owing to its ability to handle multiple groups and being able to adjust for multiple factors. Box-Cox power transformation often has been used to transform data to a Gaussian distribution for parametric computation of RIs. However, this transformation occasionally fails. Therefore, the non-parametric method based on determination of the 2.5 and 97.5 percentiles following sorting of the data, has been recommended for general use. The performance of the Box-Cox transformation can be improved by introducing an additional parameter representing the origin of transformation. In simulations, the confidence intervals (CIs) of reference limits (RLs) calculated by the parametric method were narrower than those calculated by the non-parametric approach. However, the margin of difference was rather small owing to additional variability in parametrically-determined RLs introduced by estimation of parameters for the Box-Cox transformation. The parametric calculation method may have an advantage over the non-parametric method in allowing identification and exclusion of extreme values during RI computation.

Bronchoscopy with endobronchial ultrasound guided transbronchial needle aspiration vs. transthoracic needle aspiration in lung cancer diagnosis and staging.

PubMed

Munoz, Mark L; Lechtzin, Noah; Li, Qing Kay; Wang, KoPen; Yarmus, Lonny B; Lee, Hans J; Feller-Kopman, David J

2017-07-01

In evaluating patients with suspected lung cancer, it is important to not only obtain a tissue diagnosis, but also to obtain enough tissue for both histologic and molecular analysis in order to appropriately stage the patient with a safe and efficient strategy. The diagnostic approach may often be dependent on local resources and practice patterns rather than current guidelines. We Describe lung cancer staging at two large academic medical centers to identify the impact different procedural approaches have on patient outcomes. We conducted a retrospective cohort study of all patients undergoing a lung cancer diagnostic evaluation at two multidisciplinary centers during a 1-year period. Identifying complication rates and the need for multiple biopsies as our primary outcomes, we developed a multivariate regression model to determine features associated with complications and need for multiple biopsies. Of 830 patients, 285 patients were diagnosed with lung cancers during the study period. Those staged at the institution without an endobronchial ultrasound (EBUS) program were more likely to require multiple biopsies (OR 3.62, 95% CI: 1.71-7.67, P=0.001) and suffer complications associated with the diagnostic procedure (OR 10.2, 95% CI: 3.08-33.58, P<0.001). Initial staging with transthoracic needle aspiration (TTNA) and conventional bronchoscopy were associated with greater need for subsequent biopsies (OR 8.05 and 14.00, 95% CI: 3.43-18.87 and 5.17-37.86, respectively) and higher complication rates (OR 37.75 and 7.20, 95% CI: 10.33-137.96 and 1.36-37.98, respectively). Lung cancer evaluation at centers with a dedicated EBUS program results in fewer biopsies and complications than at multidisciplinary counterparts without an EBUS program.

Adaptive graph-based multiple testing procedures

PubMed Central

Klinglmueller, Florian; Posch, Martin; Koenig, Franz

2016-01-01

Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations. PMID:25319733

Multiple Small Diameter Drillings Increase Femoral Neck Stability Compared with Single Large Diameter Femoral Head Core Decompression Technique for Avascular Necrosis of the Femoral Head.

PubMed

Brown, Philip J; Mannava, Sandeep; Seyler, Thorsten M; Plate, Johannes F; Van Sikes, Charles; Stitzel, Joel D; Lang, Jason E

2016-10-26

Femoral head core decompression is an efficacious joint-preserving procedure for treatment of early stage avascular necrosis. However, postoperative fractures have been described which may be related to the decompression technique used. Femoral head decompressions were performed on 12 matched human cadaveric femora comparing large 8mm single bore versus multiple 3mm small drilling techniques. Ultimate failure strength of the femora was tested using a servo-hydraulic material testing system. Ultimate load to failure was compared between the different decompression techniques using two paired ANCOVA linear regression models. Prior to biomechanical testing and after the intervention, volumetric bone mineral density was determined using quantitative computed tomography to account for variation between cadaveric samples and to assess the amount of bone disruption by the core decompression. Core decompression, using the small diameter bore and multiple drilling technique, withstood significantly greater load prior to failure compared with the single large bore technique after adjustment for bone mineral density (p< 0.05). The 8mm single bore technique removed a significantly larger volume of bone compared to the 3mm multiple drilling technique (p< 0.001). However, total fracture energy was similar between the two core decompression techniques. When considering core decompression for the treatment of early stage avascular necrosis, the multiple small bore technique removed less bone volume, thereby potentially leading to higher load to failure.

Organizational climate determinants of resident safety culture in nursing homes.

PubMed

Arnetz, Judith E; Zhdanova, Ludmila S; Elsouhag, Dalia; Lichtenberg, Peter; Luborsky, Mark R; Arnetz, Bengt B

2011-12-01

In recent years, there has been an increasing focus on the role of safety culture in preventing costly adverse events, such as medication errors and falls, among nursing home residents. However, little is known regarding critical organizational determinants of a positive safety culture in nursing homes. The aim of this study was to identify organizational climate predictors of specific aspects of the staff-rated resident safety culture (RSC) in a sample of nursing homes. Staff at 4 Michigan nursing homes responded to a self-administered questionnaire measuring organizational climate and RSC. Multiple regression analyses were used to identify organizational climate factors that predicted the safety culture dimensions nonpunitive response to mistakes, communication about incidents, and compliance with procedures.  The organizational climate factors efficiency and work climate predicted nonpunitive response to mistakes (p < .001 for both scales) and compliance with procedures (p < .05 and p < .001 respectively). Work stress was an inverse predictor of compliance with procedures (p < .05). Goal clarity was the only significant predictor of communication about incidents (p < .05). Efficiency, work climate, work stress, and goal clarity are all malleable organizational factors that could feasibly be the focus of interventions to improve RSC. Future studies will examine whether these results can be replicated with larger samples.

Influence of hospital-level practice patterns on variation in the application of minimally invasive surgery in United States pediatric patients.

PubMed

Train, Arianne T; Harmon, Carroll M; Rothstein, David H

2017-10-01

Although disparities in access to minimally invasive surgery are thought to exist in pediatric surgical patients in the United States, hospital-level practice patterns have not been evaluated as a possible contributing factor. Retrospective cohort study using the Kids' Inpatient Database, 2012. Odds ratios of undergoing a minimally invasive compared to open operation were calculated for six typical pediatric surgical operations after adjustment for multiple patient demographic and hospital-level variables. Further adjustment to the regression model was made by incorporating hospital practice patterns, defined as operation-specific minimally invasive frequency and volume. Age was the most significant patient demographic factor affecting application of minimally invasive surgery for all procedures. For several procedures, adjusting for individual hospital practice patterns removed race- and income-based disparities seen in performance of minimally invasive operations. Disparities related to insurance status were not affected by the same adjustment. Variation in the application of minimally invasive surgery in pediatric surgical patients is primarily influenced by patient age and the type of procedure performed. Perceived disparities in access related to some socioeconomic factors are decreased but not eliminated by accounting for individual hospital practice patterns, suggesting that complex underlying factors influence application of advanced surgical techniques. II. Copyright © 2017 Elsevier Inc. All rights reserved.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

New insights into old methods for identifying causal rare variants.

PubMed

Wang, Haitian; Huang, Chien-Hsun; Lo, Shaw-Hwa; Zheng, Tian; Hu, Inchi

2011-11-29

The advance of high-throughput next-generation sequencing technology makes possible the analysis of rare variants. However, the investigation of rare variants in unrelated-individuals data sets faces the challenge of low power, and most methods circumvent the difficulty by using various collapsing procedures based on genes, pathways, or gene clusters. We suggest a new way to identify causal rare variants using the F-statistic and sliced inverse regression. The procedure is tested on the data set provided by the Genetic Analysis Workshop 17 (GAW17). After preliminary data reduction, we ranked markers according to their F-statistic values. Top-ranked markers were then subjected to sliced inverse regression, and those with higher absolute coefficients in the most significant sliced inverse regression direction were selected. The procedure yields good false discovery rates for the GAW17 data and thus is a promising method for future study on rare variants.

Quantification of the effects of environmental leaching factors on emissions from bottom ash in road construction.

PubMed

Ecke, Holger; Aberg, Annika

2006-06-01

The re-use of bottom ash in road construction necessitates a tool to predict the impact of trace metals on the surroundings over the lifetime of the road. The aim of this work was to quantify the effect of environmental factors that are supposed to influence leaching, so as to suggest guidelines in developing a leaching procedure for the testing of incineration residues re-used in road constructions. The effects of pH, L/S (liquid-to-solid ratio), leaching time, and leaching atmosphere on the leachate concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were studied using a two-level full factorial design. The most significant factor for all responses was the pH, followed by L/S, though the importance of pH and L/S is often ignored in leaching tests. Multiple linear regression models describing the variation in leaching data had R(2) values ranging from 61-97%. A two-step pH-stat leaching procedure that considers pH as well as L/S and leaching time was suggested.

Multiple hypotheses testing based on ordered p values--a historical survey with applications to medical research.

PubMed

Hommel, Gerhard; Bretz, Frank; Maurer, Willi

2011-07-01

Global tests and multiple test procedures are often based on ordered p values. Such procedures are available for arbitrary dependence structures as well as for specific dependence assumptions of the test statistics. Most of these procedures have been considered as global tests. Multiple test procedures can be obtained by applying the closure principle in order to control the familywise error rate, or by using the false discovery rate as a criterion for type I error rate control. We provide an overview and present examples showing the importance of these procedures in medical research. Finally, we discuss modifications when different weights for the hypotheses of interest are chosen.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

USDA-ARS?s Scientific Manuscript database

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly ...

A Comparison of Methods for Nonparametric Estimation of Item Characteristic Curves for Binary Items

ERIC Educational Resources Information Center

Lee, Young-Sun

2007-01-01

This study compares the performance of three nonparametric item characteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict…

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

Resampling probability values for weighted kappa with multiple raters.

PubMed

Mielke, Paul W; Berry, Kenneth J; Johnston, Janis E

2008-04-01

A new procedure to compute weighted kappa with multiple raters is described. A resampling procedure to compute approximate probability values for weighted kappa with multiple raters is presented. Applications of weighted kappa are illustrated with an example analysis of classifications by three independent raters.

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Surgical resident involvement is safe for common elective general surgery procedures.

PubMed

Tseng, Warren H; Jin, Leah; Canter, Robert J; Martinez, Steve R; Khatri, Vijay P; Gauvin, Jeffrey; Bold, Richard J; Wisner, David; Taylor, Sandra; Chen, Steven L

2011-07-01

Outcomes of surgical resident training are under scrutiny with the changing milieu of surgical education. Few have investigated the effect of surgical resident involvement (SRI) on operative parameters. Examining 7 common general surgery procedures, we evaluated the effect of SRI on perioperative morbidity and mortality and operative time (OpT). The American College of Surgeons National Surgical Quality Improvement Program database (2005 to 2007) was used to identify 7 cases of nonemergent operations. Cases with simultaneous procedures were excluded. Logistic regression was performed across all procedures and within each procedure incorporating SRI, OpT, and risk-stratifying American College of Surgery National Surgical Quality Improvement Program morbidity and mortality probability scores, which incorporate multiple prognostic individual patient factors. Procedure-specific, SRI-stratified OpTs were compared using Wilcoxon rank-sum tests. A total of 71.3% of the 37,907 cases had SRI. Absolute 30-day morbidity for all cases with SRI and without SRI were 3.0% and 1.0%, respectively (p < 0.001); absolute 30-day mortality for all cases with SRI and without SRI were 0.1% and 0.08%, respectively (p < 0.001). After multivariate analysis by specific procedure, SRI was not associated with increased morbidity but was associated with decreased mortality during open right colectomy (odds ratio 0.32; p = 0.01). Across all procedures, SRI was associated with increased morbidity (odds ratio 1.14; p = 0.048) but decreased mortality (odds ratio 0.42; p < 0.001). Mean OpT for all procedures was consistently lower for cases without SRI. SRI has a measurable impact on both 30-day morbidity and mortality and OpT. These data have implications to the impact associated with surgical graduate medical education. Further studies to identify causes of patient morbidity and prevention strategies in surgical teaching environments are warranted. Copyright © 2011 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P.

2014-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Development of Multiple Regression Equations To Predict Fourth Graders' Achievement in Reading and Selected Content Areas.

ERIC Educational Resources Information Center

Hafner, Lawrence E.

A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…

Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

ERIC Educational Resources Information Center

Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

2015-01-01

Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

ERIC Educational Resources Information Center

Martin, David

2008-01-01

This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Regression Models for the Analysis of Longitudinal Gaussian Data from Multiple Sources

PubMed Central

O’Brien, Liam M.; Fitzmaurice, Garrett M.

2006-01-01

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. PMID:15726666

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Traditional Admissions Variables as Predictors of Minority Students' Performance in Medical School — A Cause for Concern

PubMed Central

Johnson, Henry C.; Rosevear, G. Craig

1977-01-01

This study explored the relationship between traditional admissions criteria, performance in the first semester of medical school, and performance on the National Board of Medical Examiners' (NBME) Examination, Part 1 for minority medical students, non-minority medical students, and the two groups combined. Correlational analysis and step-wise multiple regression procedures were used as the analysis techniques. A different pattern of admissions variables related to National Board Part 1 performance for the two groups. The General Information section of the Medical College Admission Test (MCAT) contributed the most variance for the minority student group. MCAT-Science contributed the most variance for the non-minority student group. MCATs accounted for a substantial portion of the variance on the National Board examination. PMID:904005

Rigid bronchoscopic management of acute respiratory failure in a 30-year-old woman

PubMed Central

Madan, Karan; Dhungana, Ashesh; Madan, Neha Kawatra; Mohan, Anant; Hadda, Vijay; Garg, Rakesh; Jain, Deepali; Guleria, Randeep

2016-01-01

A 30-year-old woman presented with a history of progressive shortness of breath, cough, and hoarseness. Stridor was audible on examination. Chest X-ray showed normal lung fields and contrast-enhanced computed tomography thorax showed lower tracheal occlusion with endoluminal growth. Diagnostic flexible bronchoscopy demonstrated multiple whitish glistening nodules over both vocal cords and lower tracheal occlusion by whitish nodular growth. In view of critical central airway obstruction, rigid bronchoscopy and excision of the lower tracheal growth were performed. Histopathological examination of the excised specimen demonstrated features of squamous papillomas. A diagnosis of respiratory papillomatosis was established. On follow-up surveillance bronchoscopy, there was a gradual spontaneous regression of the residual lesions, and the patient remains currently asymptomatic 1 year since the procedure. PMID:27891001

Estimating Mixture of Gaussian Processes by Kernel Smoothing

PubMed Central

Huang, Mian; Li, Runze; Wang, Hansheng; Yao, Weixin

2014-01-01

When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from EM algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset. PMID:24976675

The evaluation of the National Long Term Care Demonstration. 2. Estimation methodology.

PubMed Central

Brown, R S

1988-01-01

Channeling effects were estimated by comparing the post-application experience of the treatment and control groups using multiple regression. A variety of potential threats to the validity of the results, including sample composition issues, data issues, and estimation issues, were identified and assessed. Of all the potential problems examined, the only one determined to be likely to cause widespread distortion of program impact estimates was noncomparability of the baseline data. To avoid this distortion, baseline variables judged to be noncomparably measured were excluded from use as control variables in the regression equation. (Where they existed, screen counterparts to these noncomparable baseline variables were used as substitutes.) All of the other potential problems with the sample, data, or regression estimation approach were found to have little or no actual effect on impact estimates or their interpretation. Broad implementation of special procedures, therefore, was not necessary. The study did find that, because of the frequent use of proxy respondents, the estimated effects of channeling on clients' well-being actually may reflect impacts on the well-being of the informal caregiver rather than the client. This and other isolated cases in which there was some evidence of a potential problem for specific outcome variables were identified and examined in detail in technical reports dealing with those outcomes. Where appropriate, alternative estimates were presented. PMID:3130329

Semiparametric temporal process regression of survival-out-of-hospital.

PubMed

Zhan, Tianyu; Schaubel, Douglas E

2018-05-23

The recurrent/terminal event data structure has undergone considerable methodological development in the last 10-15 years. An example of the data structure that has arisen with increasing frequency involves the recurrent event being hospitalization and the terminal event being death. We consider the response Survival-Out-of-Hospital, defined as a temporal process (indicator function) taking the value 1 when the subject is currently alive and not hospitalized, and 0 otherwise. Survival-Out-of-Hospital is a useful alternative strategy for the analysis of hospitalization/survival in the chronic disease setting, with the response variate representing a refinement to survival time through the incorporation of an objective quality-of-life component. The semiparametric model we consider assumes multiplicative covariate effects and leaves unspecified the baseline probability of being alive-and-out-of-hospital. Using zero-mean estimating equations, the proposed regression parameter estimator can be computed without estimating the unspecified baseline probability process, although baseline probabilities can subsequently be estimated for any time point within the support of the censoring distribution. We demonstrate that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulation studies are performed to show that our estimating procedures have satisfactory finite sample performances. The proposed methods are applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS), an international end-stage renal disease study.

Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.

PubMed

Gruber, Susan; Logan, Roger W; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A

2015-01-15

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V-fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. Copyright © 2014 John Wiley & Sons, Ltd.

Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

PubMed Central

Gruber, Susan; Logan, Roger W.; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A.

2014-01-01

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V -fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. PMID:25316152

Implementing Endobronchial Ultrasound-Guided (EBUS) for Staging and Diagnosis of Lung Cancer: A Cost Analysis

PubMed Central

Slavova-Azmanova, Neli S.; Phillips, Martin; Trevenen, Michelle L.; Li, Ian W.; Johnson, Claire E.

2018-01-01

Background Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and guide sheath (EBUS-GS) are gaining popularity for diagnosis and staging of lung cancer compared to CT-guided transthoracic needle aspiration (CT-TTNA), blind fiber-optic bronchoscopy, and mediastinoscopy. This paper aimed to examine predictors of higher costs for diagnosing and staging lung cancer, and to assess the effect of EBUS techniques on hospital cost. Material/Methods Hospital costs for diagnosis and staging of new primary lung cancer patients presenting in 2007–2008 and 2010–2011 were reviewed retrospectively. Multiple linear regression was used to determine relationships with hospital cost. Results We reviewed 560 lung cancer patient records; 100 EBUS procedures were performed on 90 patients. Higher hospital costs were associated with: EBUS-TBNA performed (p<0.0001); increasing inpatient length of stay (p<0.0001); increasing number of other surgical/diagnostic procedures (p<0.0001); whether the date of management decision fell within an inpatient visit (p<0.0001); and if the patient did not have a CT-TTNA, then costs increased as the number of imaging events increased (interaction p<0.0001). Cohort was not significantly related to cost. Location of the procedure (outside vs. inside theater) was a predictor of lower one-day EBUS costs (p<0.0001). Cost modelling revealed potential cost saving of $1506 per EBUS patient if all EBUS procedures were performed outside rather than in the theater ($66,259 per annum). Conclusions EBUS-TBNA only was an independent predictor of higher cost for diagnosis and staging of lung cancer. Performing EBUS outside compared to in the theater may lower costs for one-day procedures; potential future savings are considerable if more EBUS procedures could be performed outside the operating theater. PMID:29377878

Red blood cell transfusion probability and associated costs in neurosurgical procedures.

PubMed

Barth, Martin; Weiss, Christel; Schmieder, Kirsten

2018-03-20

The extent of red blood cell units (RBC) needed for different neurosurgical procedures and the time point of their administration are widely unknown, which results in generously cross-matching prior to surgery. However, RBC are increasingly requested in the aging western populations, and blood donations are significantly reduced. Therefore, the knowledge of the extent and time point of administration of RBC is of major importance. This is a retrospective single center analysis. The incidence of RBC transfusion during surgery or within 48 h after surgery was analyzed for all neurosurgical patients within 3 years. Costs for cross-matched and transfused RBC were calculated and risk factors for RBC transfusion analyzed. The risk of intraoperative RBC administration was low for spinal and intracranial tumor resections (1.87%) and exceeded 10% only in spinal fusion procedures. This was dependent on the number of fused segments with an intraoperative transfusion risk of > 12.5% with fusion of more than three levels. Multiple logistic regression analysis showed a significantly increased risk for RBC transfusion for female gender (p = 0.006; OR 1.655), higher age (N = 4812; p < 0.0001; OR 1.028), and number of fused segments (N = 737; p < 0.0001; OR 1.433). Annual costs for cross-matching were 783,820.88 USD and for intraoperative RBC administration 121,322.13 USD. Neurosurgical procedures are associated with a low number of RBC needed intraoperatively. Only elective spine fusion procedures with ≥ 3 levels involved and AVM resections seem to require cross-matching of RBC. The present data may allow changing the preoperative algorithm of RBC cross-matching in neurosurgical procedures and help to save resources and costs.

Anxiety and styles of coping with occupational stress resulting from work with 'dangerous' prisoners in prison service officers.

PubMed

Tsirigotis, Konstantinos; Gruszczyński, Wojciech; Pęczkowski, Sebastian

2015-10-01

Prisoners categorised as 'dangerous' are a category of prisoners that require and/or force into using special measures of caution, protection and security. The aim of the study was to examine the intensity of anxiety (as a state and as a trait) experienced by officers working with 'dangerous' prisoners and styles of coping with stress they adopt. A total of 40 officers working with 'dangerous' prisoners (the study group, SG) and 60 officers of the security department not working with 'dangerous' prisoners (the reference group, RG) were studied. The intensity of anxiety was assessed applying the Polish version of 'State-Trait Anxiety Inventory' (STAI); styles of coping with stress were explored employing the Polish version of 'Coping Inventory for Stressful Situations' (CISS) and the author's own questionnaire. Data were analysed using the mean, standard deviation, difference testing (the Mann-Whitney U test), correlation-regression procedure (Kendall's tau, τ correlation coefficient and forward stepwise multiple regression). Officers in the SG faced verbal and physical aggression; nevertheless, scores of officers in both the groups were within the interval of mean scores for all the studied STAI and CISS variables. Officers in the SG achieved significantly higher scores on the state-anxiety scale and the Emotion-Oriented Style (EOS), and lower scores on the Task-Oriented Style (TOS) and Social Diversion (SD). The correlation-regression procedure indicated that there were relationships between anxiety and styles of coping with stress but they differed slightly between the groups. Officers in the SG feel state anxiety stronger and display a stronger preference for the EOS than officers in the RG. Officers in the RG more strongly prefer the TOS and SD. State anxiety is a variable negatively explaining the TOS in the SG, whereas anxiety as a trait is a variable explaining the EOS in both the groups. The coping styles of warders dealing with dangerous prisoners are different and may need specific psychological counselling and training programmes.

Paleotemperature reconstruction from mammalian phosphate δ18O records - an alternative view on data processing

NASA Astrophysics Data System (ADS)

Skrzypek, Grzegorz; Sadler, Rohan; Wiśniewski, Andrzej

2017-04-01

The stable oxygen isotope composition of phosphates (δ18O) extracted from mammalian bone and teeth material is commonly used as a proxy for paleotemperature. Historically, several different analytical and statistical procedures for determining air paleotemperatures from the measured δ18O of phosphates have been applied. This inconsistency in both stable isotope data processing and the application of statistical procedures has led to large and unwanted differences between calculated results. This study presents the uncertainty associated with two of the most commonly used regression methods: least squares inverted fit and transposed fit. We assessed the performance of these methods by designing and applying calculation experiments to multiple real-life data sets, calculating in reverse temperatures, and comparing them with true recorded values. Our calculations clearly show that the mean absolute errors are always substantially higher for the inverted fit (a causal model), with the transposed fit (a predictive model) returning mean values closer to the measured values (Skrzypek et al. 2015). The predictive models always performed better than causal models, with 12-65% lower mean absolute errors. Moreover, the least-squares regression (LSM) model is more appropriate than Reduced Major Axis (RMA) regression for calculating the environmental water stable oxygen isotope composition from phosphate signatures, as well as for calculating air temperature from the δ18O value of environmental water. The transposed fit introduces a lower overall error than the inverted fit for both the δ18O of environmental water and Tair calculations; therefore, the predictive models are more statistically efficient than the causal models in this instance. The direct comparison of paleotemperature results from different laboratories and studies may only be achieved if a single method of calculation is applied. Reference Skrzypek G., Sadler R., Wiśniewski A., 2016. Reassessment of recommendations for processing mammal phosphate δ18O data for paleotemperature reconstruction. Palaeogeography, Palaeoclimatology, Palaeoecology 446, 162-167.

Predictive capacity of sperm quality parameters and sperm subpopulations on field fertility after artificial insemination in sheep.

PubMed

Santolaria, P; Vicente-Fiel, S; Palacín, I; Fantova, E; Blasco, M E; Silvestre, M A; Yániz, J L

2015-12-01

This study was designed to evaluate the relevance of several sperm quality parameters and sperm population structure on the reproductive performance after cervical artificial insemination (AI) in sheep. One hundred and thirty-nine ejaculates from 56 adult rams were collected using an artificial vagina, processed for sperm quality assessment and used to perform 1319 AI. Analyses of sperm motility by computer-assisted sperm analysis (CASA), sperm nuclear morphometry by computer-assisted sperm morphometry analysis (CASMA), membrane integrity by acridine orange-propidium iodide combination and sperm DNA fragmentation using the sperm chromatin dispersion test (SCD) were performed. Clustering procedures using the sperm kinematic and morphometric data resulted in the classification of spermatozoa into three kinematic and three morphometric sperm subpopulations. Logistic regression procedures were used, including fertility at AI as the dependent variable (measured by lambing, 0 or 1) and farm, year, month of AI, female parity, female lambing-treatment interval, ram, AI technician and sperm quality parameters (including sperm subpopulations) as independent factors. Sperm quality variables remaining in the logistic regression model were viability and VCL. Fertility increased for each one-unit increase in viability (by a factor of 1.01) and in VCL (by a factor of 1.02). Multiple linear regression analyses were also performed to analyze the factors possibly influencing ejaculate fertility (N=139). The analysis yielded a significant (P<0.05) relationship between sperm viability and ejaculate fertility. The discriminant ability of the different semen variables to predict field fertility was analyzed using receiver operating characteristic (ROC) curve analysis. Sperm viability and VCL showed significant, albeit limited, predictive capacity on field fertility (0.57 and 0.54 Area Under Curve, respectively). The distribution of spermatozoa in the different subpopulations was not related to fertility. Copyright © 2015 Elsevier B.V. All rights reserved.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Behavioral economic analysis of drug preference using multiple choice procedure data.

PubMed

Greenwald, Mark K

2008-01-11

The multiple choice procedure has been used to evaluate preference for psychoactive drugs, relative to money amounts (price), in human subjects. The present re-analysis shows that MCP data are compatible with behavioral economic analysis of drug choices. Demand curves were constructed from studies with intravenous fentanyl, intramuscular hydromorphone and oral methadone in opioid-dependent individuals; oral d-amphetamine, oral MDMA alone and during fluoxetine treatment, and smoked marijuana alone or following naltrexone pretreatment in recreational drug users. For each participant and dose, the MCP crossover point was converted into unit price (UP) by dividing the money value ($) by the drug dose (mg/70kg). At the crossover value, the dose ceases to function as a reinforcer, so "0" was entered for this and higher UPs to reflect lack of drug choice. At lower UPs, the dose functions as a reinforcer and "1" was entered to reflect drug choice. Data for UP vs. average percent choice were plotted in log-log space to generate demand functions. Rank of order of opioid inelasticity (slope of non-linear regression) was: fentanyl>hydromorphone (continuing heroin users)>methadone>hydromorphone (heroin abstainers). Rank order of psychostimulant inelasticity was d-amphetamine>MDMA>MDMA+fluoxetine. Smoked marijuana was more inelastic with high-dose naltrexone. These findings show this method translates individuals' drug preferences into estimates of population demand, which has the potential to yield insights into pharmacotherapy efficacy, abuse liability assessment, and individual differences in susceptibility to drug abuse.

Behavioral Economic Analysis of Drug Preference Using Multiple Choice Procedure Data

PubMed Central

Greenwald, Mark K.

2008-01-01

The Multiple Choice Procedure has been used to evaluate preference for psychoactive drugs, relative to money amounts (price), in human subjects. The present re-analysis shows that MCP data are compatible with behavioral economic analysis of drug choices. Demand curves were constructed from studies with intravenous fentanyl, intramuscular hydromorphone and oral methadone in opioid-dependent individuals; oral d-amphetamine, oral MDMA alone and during fluoxetine treatment, and smoked marijuana alone or following naltrexone pretreatment in recreational drug users. For each participant and dose, the MCP crossover point was converted into unit price (UP) by dividing the money value ($) by the drug dose (mg/70 kg). At the crossover value, the dose ceases to function as a reinforcer, so “0” was entered for this and higher UPs to reflect lack of drug choice. At lower UPs, the dose functions as a reinforcer and “1” was entered to reflect drug choice. Data for UP vs. average percent choice were plotted in log-log space to generate demand functions. Rank of order of opioid inelasticity (slope of non-linear regression) was: fentanyl > hydromorphone (continuing heroin users) > methadone > hydromorphone (heroin abstainers). Rank order of psychostimulant inelasticity was d-amphetamine > MDMA > MDMA + fluoxetine. Smoked marijuana was more inelastic with high-dose naltrexone. These findings show this method translates individuals’ drug preferences into estimates of population demand, which has the potential to yield insights into pharmacotherapy efficacy, abuse liability assessment, and individual differences in susceptibility to drug abuse. PMID:17949924

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

PubMed

Graffelman, Jan; van Eeuwijk, Fred

2005-12-01

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

Statistical Methods for Magnetic Resonance Image Analysis with Applications to Multiple Sclerosis

NASA Astrophysics Data System (ADS)

Pomann, Gina-Maria

Multiple sclerosis (MS) is an immune-mediated neurological disease that causes disability and morbidity. In patients with MS, the accumulation of lesions in the white matter of the brain is associated with disease progression and worse clinical outcomes. In the first part of the dissertation, we present methodology to study to compare the brain anatomy between patients with MS and controls. A nonparametric testing procedure is proposed for testing the null hypothesis that two samples of curves observed at discrete grids and with noise have the same underlying distribution. We propose to decompose the curves using functional principal component analysis of an appropriate mixture process, which we refer to as marginal functional principal component analysis. This approach reduces the dimension of the testing problem in a way that enables the use of traditional nonparametric univariate testing procedures. The procedure is computationally efficient and accommodates different sampling designs. Numerical studies are presented to validate the size and power properties of the test in many realistic scenarios. In these cases, the proposed test is more powerful than its primary competitor. The proposed methodology is illustrated on a state-of-the art diffusion tensor imaging study, where the objective is to compare white matter tract profiles in healthy individuals and MS patients. In the second part of the thesis, we present methods to study the behavior of MS in the white matter of the brain. Breakdown of the blood-brain barrier in newer lesions is indicative of more active disease-related processes and is a primary outcome considered in clinical trials of treatments for MS. Such abnormalities in active MS lesions are evaluated in vivo using contrast-enhanced structural magnetic resonance imaging (MRI), during which patients receive an intravenous infusion of a costly magnetic contrast agent. In some instances, the contrast agents can have toxic effects. Recently, local image regression techniques have been shown to have modest performance for assessing the integrity of the blood-brain barrier based on imaging without contrast agents. These models have centered on the problem of cross-sectional classification in which patients are imaged at a single study visit and pre-contrast images are used to predict post-contrast imaging. In this paper, we extend these methods to incorporate historical imaging information, and we find the proposed model to exhibit improved performance. We further develop scan-stratified case-control sampling techniques that reduce the computational burden of local image regression models while respecting the low proportion of the brain that exhibits abnormal vascular permeability. In the third part of this thesis, we present methods to evaluate tissue damage in patients with MS. We propose a lag functional linear model to predict a functional response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions; 1) a semi-local smoothing approach using generalized cross-validation; and 2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios. We find that the global smoothing approach results in higher predictive accuracy than the semi-local approach. The methods are employed to estimate a measure of tissue damage in patients with MS. In patients with MS, the myelin sheaths around the axons of the neurons in the brain and spinal cord are damaged. The model facilitates the use of commonly acquired imaging modalities to estimate a measure of tissue damage within lesions. The proposed model outperforms the cross-sectional models that do not account for temporal patterns of lesional development and repair.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

Principles of Quantile Regression and an Application

ERIC Educational Resources Information Center

Chen, Fang; Chalhoub-Deville, Micheline

2014-01-01

Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study and an Application

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Cimpian, Joseph R.

2017-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

PubMed

McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

2017-02-01

Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis.

PubMed

Cho, Seoae; Kim, Haseong; Oh, Sohee; Kim, Kyunga; Park, Taesung

2009-12-15

The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.

Organizational justice and psychological distress among permanent and non-permanent employees in Japan: a prospective cohort study.

PubMed

Inoue, Akiomi; Kawakami, Norito; Tsuno, Kanami; Tomioka, Kimiko; Nakanishi, Mayuko

2013-06-01

Organizational justice has recently been introduced as a new concept as psychosocial determinants of employee health, and an increase in precarious employment is a challenging issue in occupational health. However, no study investigated the association of organizational justice with mental health among employees while taking into account employment contract. The purpose of the present study was to investigate the prospective association of organizational justice (procedural justice and interactional justice) with psychological distress by employment contract among Japanese employees. A total of 373 males and 644 females from five branches of a manufacturing company in Japan were surveyed. At baseline (August 2009), self-administered questionnaires, including the Organizational Justice Questionnaire (OJQ), the K6 scale (psychological distress scale), the Eysenck Personality Questionnaire-Revised (EPQ-R), and other covariates, were used. After one-year follow-up (August 2010), the K6 scale was used again to assess psychological distress. Multiple logistic regression analyses were conducted by sex and employment contract. After adjusting for demographic characteristics, psychological distress, and neuroticism at baseline, low procedural justice was significantly associated with a higher risk of psychological distress at follow-up among non-permanent female employees, while no significant association of procedural justice or interactional justice with psychological distress at follow-up was observed among permanent male or female employees. The results of non-permanent male employees could not be calculated because of small sample size. Low procedural justice may be an important predictor of psychological distress among non-permanent female employees.

Age- and sex-specific reference values of a test of neck muscle endurance.

PubMed

Peolsson, Anneli; Almkvist, Cecilia; Dahlberg, Camilla; Lindqvist, Sara; Pettersson, Susanne

2007-01-01

This study evaluates age- and sex-specific reference values for neck muscle endurance (NME). In this cross-sectional study, 116 randomly selected, healthy volunteers (ages 25-64 years) stratified according to age and gender participated. Dorsal and ventral NME was measured in seconds until exhaustion in a laying-down position. A weight of 4 kg for men or 2 kg for women was used in the dorsal procedure. The ventral procedure was performed without external load. Background and physical activity data were obtained and used in the analysis of NME performance. Mean values for dorsal and ventral NME were about 7 and 2.5 minutes for men and 8.5 and 0.5 minutes for women, respectively. The cutoff values for subnormal dorsal and ventral NME were 157 and 56 seconds for men and 173 and 23 seconds for women, respectively. Women's NME was 122% of men's NME in the dorsal (P = .17) and 24% of men's NME in the ventral (P < .0001) procedure. There were no significant differences among age groups. In multiple regression analysis, physical activity explained 4% of variability in the performance of the dorsal NME; and sex explained 37% of the variability in the performance of ventral NME. The reference values and the cutoff points obtained could be used in clinical practice to identify patients with a subnormal NME. Sex is an important consideration when using both the test procedure and the reference values.

Determining the in-hospital cost of bleeding in patients undergoing percutaneous coronary intervention.

PubMed

Ewen, Edward F; Zhao, Liping; Kolm, Paul; Jurkovitz, Claudine; Fidan, Dogan; White, Harvey D; Gallo, Richard; Weintraub, William S

2009-06-01

The economic impact of bleeding in the setting of nonemergent percutaneous coronary intervention (PCI) is poorly understood and complicated by the variety of bleeding definitions currently employed. This retrospective analysis examines and contrasts the in-hospital cost of bleeding associated with this procedure using six bleeding definitions employed in recent clinical trials. All nonemergent PCI cases at Christiana Care Health System not requiring a subsequent coronary artery bypass were identified between January 2003 and March 2006. Bleeding events were identified by chart review, registry, laboratory, and administrative data. A microcosting strategy was applied utilizing hospital charges converted to costs using departmental level direct cost-to-charge ratios. The independent contributions of bleeding, both major and minor, to cost were determined by multiple regression. Bootstrap methods were employed to obtain estimates of regression parameters and their standard errors. A total of 6,008 cases were evaluated. By GUSTO definitions there were 65 (1.1%) severe, 52 (0.9%) moderate, and 321 (5.3%) mild bleeding episodes with estimated bleeding costs of $14,006; $6,980; and $4,037, respectively. When applying TIMI definitions there were 91 (1.5%) major and 178 (3.0%) minor bleeding episodes with estimated costs of $8,794 and $4,310, respectively. In general, the four additional trial-specific definitions identified more bleeding events, provided lower estimates of major bleeding cost, and similar estimates of minor bleeding costs. Bleeding is associated with considerable cost over and above interventional procedures; however, the choice of bleeding definition impacts significantly on both the incidence and economic consequences of these events.

Omnibus risk assessment via accelerated failure time kernel machine modeling.

PubMed

Sinnott, Jennifer A; Cai, Tianxi

2013-12-01

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

National Trends of Simple Prostatectomy for Benign Prostatic Hyperplasia With an Analysis of Risk Factors for Adverse Perioperative Outcomes.

PubMed

Pariser, Joseph J; Pearce, Shane M; Patel, Sanjay G; Bales, Gregory T

2015-10-01

To examine the national trends of simple prostatectomy (SP) for benign prostatic hyperplasia (BPH) focusing on perioperative outcomes and risk factors for complications. The National Inpatient Sample (2002-2012) was utilized to identify patients with BPH undergoing SP. Analysis included demographics, hospital details, associated procedures, and operative approach (open, robotic, or laparoscopic). Outcomes included complications, length of stay, charges, and mortality. Multivariate logistic regression was used to determine the risk factors for perioperative complications. Linear regression was used to assess the trends in the national annual utilization of SP. The study population included 35,171 patients. Median length of stay was 4 days (interquartile range 3-6). Cystolithotomy was performed concurrently in 6041 patients (17%). The overall complication rate was 28%, with bleeding occurring most commonly. In total, 148 (0.4%) patients experienced in-hospital mortality. On multivariate analysis, older age, black race, and overall comorbidity were associated with greater risk of complications while the use of a minimally invasive approach and concurrent cystolithotomy had a decreased risk. Over the study period, the national use of simple prostatectomy decreased, on average, by 145 cases per year (P = .002). By 2012, 135/2580 procedures (5%) were performed using a minimally invasive approach. The nationwide utilization of SP for BPH has decreased. Bleeding complications are common, but perioperative mortality is low. Patients who are older, black race, or have multiple comorbidities are at higher risk of complications. Minimally invasive approaches, which are becoming increasingly utilized, may reduce perioperative morbidity. Copyright © 2015 Elsevier Inc. All rights reserved.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

[Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

PubMed

Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

2011-12-01

To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Endurance of Multiplication Fact Fluency for Students with Attention Deficit Hyperactivity Disorder

ERIC Educational Resources Information Center

Brady, Kelly K.; Kubina, Richard M., Jr.

2010-01-01

This study examines the relationship between a critical learning outcome of behavioral fluency and endurance, by comparing the effects of two practice procedures on multiplication facts two through nine. The first procedure, called whole time practice trial, consisted of an uninterrupted 1 minute practice time. The second procedure, endurance…

Comparing least-squares and quantile regression approaches to analyzing median hospital charges.

PubMed

Olsen, Cody S; Clark, Amy E; Thomas, Andrea M; Cook, Lawrence J

2012-07-01

Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased estimates even when lognormal and equal variance assumptions are violated. These methods may be particularly useful in describing and analyzing hospital charges from administrative data sets. © 2012 by the Society for Academic Emergency Medicine.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

The effect of tobacco consumption and body mass index on complications and hospital stay after inguinal hernia surgery.

PubMed

Lindström, D; Sadr Azodi, O; Bellocco, R; Wladis, A; Linder, S; Adami, J

2007-04-01

The extent to which lifestyle factors such as tobacco consumption and obesity affect the outcome after inguinal hernia surgery has been poorly studied. This study was undertaken to assess the effect of smoking, smokeless tobacco consumption and obesity on postoperative complications after inguinal hernia surgery. The second aim was to evaluate the effect of tobacco consumption and obesity on the length of hospital stay. A cohort of 12,697 Swedish construction workers with prospectively collected exposure data on tobacco consumption and body mass index (BMI) from 1968 onward were linked to the Swedish inpatient register. Information on inguinal hernia procedures was collected from the inpatient register. Any postoperative complication occurring within 30 days was registered. In addition to this, the length of hospitalization was calculated. The risk of postoperative complications due to tobacco exposure and BMI was estimated using a multiple logistic regression model and the length of hospital stay was estimated in a multiple linear regression model. After adjusting for the other covariates in the multivariate analysis, current smokers had a 34% (OR 1.34, 95% CI 1.04, 1.72) increased risk of postoperative complications compared to never smokers. Use of "Swedish oral moist snuff" (snus) and pack-years of tobacco smoking were not found to be significantly associated with an increased risk of postoperative complications. BMI was found to be significantly associated with an increased risk of postoperative complications (P = 0.04). This effect was mediated by the underweighted group (OR 2.94; 95% CI 1.15, 7.51). In a multivariable model, increased BMI was also found to be significantly associated with an increased mean length of hospital stay (P < 0.001). There was no statistically significant association between smoking or using snus, and the mean length of hospitalization after adjusting for the other covariates in the model. Smoking increases the risk of postoperative complications even in minor surgery such as inguinal hernia procedures. Obesity increases hospitalization after inguinal hernia surgery. The Swedish version of oral moist tobacco, snus, does not seem to affect the complication rate after hernia surgery at all.

Interannual drought index variations in Central Europe related to large-scale atmospheric circulation

NASA Astrophysics Data System (ADS)

Beck, Christoph; Philipp, Andreas; Jacobeit, Jucundus

2014-05-01

This contribution investigates the relationship between large-scale atmospheric circulation and interannual variations of the standardized precipitation index (SPI) in central Europe. To this end occurrence frequencies of circulation types (CT) derived from a variety of circulation type classifications (CTC) applied to daily sea level pressure (SLP) data and mean circulation indices of vorticity (V), zonality (Z) and meridionality (M) have been utilized as predictors within multiple regression models (MRM) for the estimation of gridded 3-month SPI values over central Europe for the period 1950 to 2010. CTC based MRMs used in the analyses comprise variants concerning the basic method for CT classification, the number of CTs, the size and location of the spatial domain used for CTCs and the exclusive use of CT frequencies or the combined use of CT frequencies and mean circulation indices as predictors. Adequate MRM predictor combinations have been identified by applying stepwise multiple regression analyses within a resampling framework. The performance (robustness) of the resulting MRMs has been quantified based on a leave-one out cross-validation procedure applying several skill scores. Furthermore the relative importance of individual predictors has been estimated for each MRM. From these analyses it can be stated that i.) the consideration of vorticity characteristics within CTCs, ii.) a relatively small size of the spatial domain to which CTCs are applied and iii.) the inclusion of mean circulation indices appear to improve model skill. However model skill exhibits distinct variations between seasons and regions. Whereas promising skill can be stated for the western and northwestern parts of the central European domain only unsatisfactorily skill is reached in the more continental regions and particularly during summer. Thus it can be concluded that the here presented approaches feature the potential for the downscaling of central European drought index variations from large-scale circulation at least for some regions. Further improvements of CTC based approaches may be expected from the optimization of CTCs for explaining the SPI e.g. via the inclusion of additional variables into the classification procedure.

Interannual drought index variations in Central Europe related to the large-scale atmospheric circulation—application and evaluation of statistical downscaling approaches based on circulation type classifications

NASA Astrophysics Data System (ADS)

Beck, Christoph; Philipp, Andreas; Jacobeit, Jucundus

2015-08-01

This contribution investigates the relationship between the large-scale atmospheric circulation and interannual variations of the standardized precipitation index (SPI) in Central Europe. To this end, circulation types (CT) have been derived from a variety of circulation type classifications (CTC) applied to daily sea level pressure (SLP) data and mean circulation indices of vorticity ( V), zonality ( Z) and meridionality ( M) have been calculated. Occurrence frequencies of CTs and circulation indices have been utilized as predictors within multiple regression models (MRM) for the estimation of gridded 3-month SPI values over Central Europe, for the period 1950 to 2010. CTC-based MRMs used in the analyses comprise variants concerning the basic method for CT classification, the number of CTs, the size and location of the spatial domain used for CTCs and the exclusive use of CT frequencies or the combined use of CT frequencies and mean circulation indices as predictors. Adequate MRM predictor combinations have been identified by applying stepwise multiple regression analyses within a resampling framework. The performance (robustness) of the resulting MRMs has been quantified based on a leave-one-out cross-validation procedure applying several skill scores. Furthermore, the relative importance of individual predictors has been estimated for each MRM. From these analyses, it can be stated that model skill is improved by (i) the consideration of vorticity characteristics within CTCs, (ii) a relatively small size of the spatial domain to which CTCs are applied and (iii) the inclusion of mean circulation indices. However, model skill exhibits distinct variations between seasons and regions. Whereas promising skill can be stated for the western and northwestern parts of the Central European domain, only unsatisfactory skill is reached in the more continental regions and particularly during summer. Thus, it can be concluded that the presented approaches feature the potential for the downscaling of Central European drought index variations from the large-scale circulation, at least for some regions. Further improvements of CTC-based approaches may be expected from the optimization of CTCs for explaining the SPI, e.g. via the inclusion of additional variables in the classification procedure.

Prevalence of female genital mutilation and its effect on women's health in Bale zone, Ethiopia: a cross-sectional study.

PubMed

Bogale, Daniel; Markos, Desalegn; Kaso, Muhammedawel

2014-10-16

Females' genital mutilation (FGM) is one of the harmful traditional practices affecting the health of women and children. It has a long-term physiological, sexual and psychological effect on women. It remains still a serious problem for large proportion of women in most sub-Saharan Africa countries including Ethiopia. A community based cross sectional study design which is supplemented by qualitative method was conducted in 2014. A total of 634 reproductive age women were involved in the quantitative part of the study. The respondents were drawn from five randomly selected districts of Bale zone. The total sample was allocated proportionally to each district based on the number of reproductive age women it has. Purposive sampling method was used for qualitative study. Then, data were collected using pre-tested and structured questionnaire. The collected data were analyzed by SPSS for windows version 16.0. Multiple logistic regressions were carried out to examine the existence of relationship between FGM and selected determinant factors. Variables significant in the bivariate analysis were then entered into a multiple logistic regression analysis. In this study, 486 (78.5%) of women had undergone some form of FGM with 75% lower and 82% upper confidence interval. To get married, to get social acceptance, to safeguard virginity, to suppress sexual desire and religious recommendations were the main reasons of FGM. The reported immediate complications were excessive bleeding at the time of the procedure, infection, urine retention and swelling of genital organ. Muslim women and women from rural areas were significantly more likely to have undergone the procedure. In addition to these, compared to women 15-20 years old older women were more likely to report themselves having undergone FGM. Although younger women, those from urban residence and some religions are less likely to have had FGM it is still extremely common in this zone. Deep cultural issues and strongly personally held beliefs which are not simple to predict or quantify are likely to be involved in the perpetuation of FGM. Efforts to eradicate the practice should incorporate a human rights approach rather than rely solely on the damaging health consequences.

Single-center experience using three shockwave lithotripters with different generator designs in management of urinary calculi.

PubMed

Ng, C F; Thompson, T J; McLornan, L; Tolley, D A

2006-01-01

We retrospectively reviewed the treatment outcomes of extracorporeal shockwave lithotripsy (SWL) in a single center using either the Wolf Piezolith 2300 (a piezoelectric lithotripter), the Dornier MPL9000 (an electrohydraulic lithotripter), or the Dornier Compact Delta (an electromagnetic lithotripter) from January 1992 to June 2002. A series of 3123 (1449 Piezolith 2300, 780 MPL9000, and 894 Compact Delta) solitary radiopaque urinary stones of < or =15 mm receiving primary SWL were identified. "Stone free" was defined as the absence of evidence of stone on plain radiography. Treatment outcomes were assessed by the stone-free rate 3 months after one treatment session, the retreatment rate, the auxiliary procedure rate, the complication rate, and the effectiveness quotient (EQ). In order to have a better assessment of the efficacy of individual lithotripters, multiple logistic regression was performed to control various factors affecting treatment outcomes, including lithotripter-type, patients' sex and age, history of previous SWL, the stone characteristics (side, site, and size), and the presence of a stent or nephrostomy tube. There were significant differences in the stone site distribution and mean stone size among the three groups. The overall EQ for the Piezolith 2300, MPL9000, and Compact Delta were 0.345, 0.303, and 0.257, respectively. However, using the multiple logistic regression model, the adjusted odds ratio (AOR) of a patient being stone-free after 3 month for the Piezolith 2300 and MPL9000 (using the Compact Delta as the referent category) were 1.38 (95% CI 1.15, 1.65) and 1.72 (95% CI 1.39, 2.11), respectively. Patients treated using the MPL9000 had significantly less re-treatment (AOR = 0.57; 95% CI 0.48, 0.69) than the other groups. No significant difference in the auxiliary procedure rate and complication rate for the three machines was observed. Based on multivariate analysis results, the Dornier MPL9000 had the best treatment outcomes in terms of stone-free rate and re-treatment rate among the three lithotripters.

29 CFR 1926.753 - Hoisting and rigging.

Code of Federal Regulations, 2010 CFR

2010-07-01

... lift rigging procedure. (1) A multiple lift shall only be performed if the following criteria are met: (i) A multiple lift rigging assembly is used; (ii) A maximum of five members are hoisted per lift... multiple lift have been trained in these procedures in accordance with § 1926.761(c)(1). (v) No crane is...

13 CFR 121.407 - What are the size procedures for multiple item procurements?

Code of Federal Regulations, 2010 CFR

2010-01-01

... Requirements for Government Procurement § 121.407 What are the size procedures for multiple item procurements? If a procurement calls for two or more specific end items or types of services with different size... multiple item procurements? 121.407 Section 121.407 Business Credit and Assistance SMALL BUSINESS...

Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis

NASA Astrophysics Data System (ADS)

Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei

2016-10-01

Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high degree of multicollinearity, possibly illustrating the co-evolution of climatic and physiographic conditions. Given the ineffectiveness of many variables used here, future work should develop new variables that target specific processes associated with percentile flows.

Evaluation of the CATSIB DIF Procedure in a Pretest Setting

ERIC Educational Resources Information Center

Nandakumar, Ratna; Roussos, Louis

2004-01-01

A new procedure, CATSIB, for assessing differential item functioning (DIF) on computerized adaptive tests (CATs) is proposed. CATSIB, a modified SIBTEST procedure, matches test takers on estimated ability and controls for impact-induced Type 1 error inflation by employing a CAT version of the IBTEST "regression correction." The…

Forecasting USAF JP-8 Fuel Needs

DTIC Science & Technology

2009-03-01

versus complex ones. When we consider long -term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified...with more simple and easy-to-implement methods, versus complex ones. When we consider long -term 5-year forecasts, our multiple regression model...effort. The insight and experience was certainly appreciated. Special thanks to my Turkish peers for their continuous support and help during this long

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

USDA-ARS?s Scientific Manuscript database

A technique of using multiple calibration sets in partial least squares regression (PLS) was proposed to improve the quantitative determination of ammonia from open-path Fourier transform infrared spectra. The spectra were measured near animal farms, and the path-integrated concentration of ammonia...

Procedures for adjusting regional regression models of urban-runoff quality using local data

USGS Publications Warehouse

Hoos, A.B.; Sisolak, J.K.

1993-01-01

Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

PubMed

Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

2017-08-01

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

Estimation of magnitude and frequency of floods for streams in Puerto Rico : new empirical models

USGS Publications Warehouse

Ramos-Gines, Orlando

1999-01-01

Flood-peak discharges and frequencies are presented for 57 gaged sites in Puerto Rico for recurrence intervals ranging from 2 to 500 years. The log-Pearson Type III distribution, the methodology recommended by the United States Interagency Committee on Water Data, was used to determine the magnitude and frequency of floods at the gaged sites having 10 to 43 years of record. A technique is presented for estimating flood-peak discharges at recurrence intervals ranging from 2 to 500 years for unregulated streams in Puerto Rico with contributing drainage areas ranging from 0.83 to 208 square miles. Loglinear multiple regression analyses, using climatic and basin characteristics and peak-discharge data from the 57 gaged sites, were used to construct regression equations to transfer the magnitude and frequency information from gaged to ungaged sites. The equations have contributing drainage area, depth-to-rock, and mean annual rainfall as the basin and climatic characteristics in estimating flood peak discharges. Examples are given to show a step-by-step procedure in calculating a 100-year flood at a gaged site, an ungaged site, a site near a gaged location, and a site between two gaged sites.

Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia.

PubMed

Ertefaie, Ashkan; Shortreed, Susan; Chakraborty, Bibhas

2016-06-15

Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to optimally individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been largely overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple assignment randomized trial of patients with schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The Moderating Role of Power Distance on the Relationship between Employee Participation and Outcome Variables

PubMed Central

Rafiei, Sima; Pourreza, Abolghasem

2013-01-01

Background: Many organisations have realised the importance of human resource for their competitive advantage. Empowering employees is therefore essential for organisational effectiveness. This study aimed to investigate the relationship between employee participation with outcome variables such as organisational commitment, job satisfaction, perception of justice in an organisation and readiness to accept job responsibilities. It further examined the impact of power distance on the relationship between participation and four outcome variables. Methods: This was a cross sectional study with a descriptive research design conducted among employees and managers of hospitals affiliated with Tehran University of Medical Sciences, Tehran, Iran. A questionnaire as a main procedure to gather data was developed, distributed and collected. Descriptive statistics, Pearson correlation coefficient and moderated multiple regression were used to analyse the study data. Results: Findings of the study showed that the level of power distance perceived by employees had a significant relationship with employee participation, organisational commitment, job satisfaction, perception of justice and readiness to accept job responsibilities. There was also a significant relationship between employee participation and four outcome variables. The moderated multiple regression results supported the hypothesis that power distance had a significant effect on the relationship between employee participation and four outcome variables. Conclusion: Organisations in which employee empowerment is practiced through diverse means such as participating them in decision making related to their field of work, appear to have more committed and satisfied employees with positive perception toward justice in the organisational interactions and readiness to accept job responsibilities. PMID:24596840

Risk Factors and Stroke Characteristic in Patients with Postoperative Strokes.

PubMed

Dong, Yi; Cao, Wenjie; Cheng, Xin; Fang, Kun; Zhang, Xiaolong; Gu, Yuxiang; Leng, Bing; Dong, Qiang

2017-07-01

Intravenous thrombolysis and intra-arterial thrombectomy are now the standard therapies for patients with acute ischemic stroke. In-house strokes have often been overlooked even at stroke centers and there is no consensus on how they should be managed. Perioperative stroke happens rather frequently but treatment protocol is lacking, In China, the issue of in-house strokes has not been explored. The aim of this study is to explore the current management of in-house stroke and identify the common risk factors associated with perioperative strokes. Altogether, 51,841 patients were admitted to a tertiary hospital in Shanghai and the records of those who had a neurological consult for stroke were reviewed. Their demographics, clinical characteristics, in-hospital complications and operations, and management plans were prospectively studied. Routine laboratory test results and risk factors of these patients were analyzed by multiple logistic regression model. From January 1, 2015, to December 31, 2015, over 1800 patients had neurological consultations. Among these patients, 37 had an in-house stroke and 20 had more severe stroke during the postoperative period. Compared to in-house stroke patients without a procedure or operation, leukocytosis and elevated fasting glucose levels were more common in perioperative strokes. In multiple logistic regression model, perioperative strokes were more likely related to large vessel occlusion. Patients with perioperative strokes had different risk factors and severity from other in-house strokes. For these patients, obtaining a neurological consultation prior to surgery may be appropriate in order to evaluate the risk of perioperative stroke. Copyright © 2017. Published by Elsevier Inc.

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

PubMed Central

Zainudin, Suhaila; Arif, Shereena M.

2017-01-01

Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767

Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

PubMed

Sun, Jianguo; Feng, Yanqin; Zhao, Hui

2015-01-01

Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

Selection of the NIR region for a regression model of the ethanol concentration in fermentation process by an online NIR and mid-IR dual-region spectrometer and 2D heterospectral correlation spectroscopy.

PubMed

Nishii, Takashi; Genkawa, Takuma; Watari, Masahiro; Ozaki, Yukihiro

2012-01-01

A new selection procedure of an informative near-infrared (NIR) region for regression model building is proposed that uses an online NIR/mid-infrared (mid-IR) dual-region spectrometer in conjunction with two-dimensional (2D) NIR/mid-IR heterospectral correlation spectroscopy. In this procedure, both NIR and mid-IR spectra of a liquid sample are acquired sequentially during a reaction process using the NIR/mid-IR dual-region spectrometer; the 2D NIR/mid-IR heterospectral correlation spectrum is subsequently calculated from the obtained spectral data set. From the calculated 2D spectrum, a NIR region is selected that includes bands of high positive correlation intensity with mid-IR bands assigned to the analyte, and used for the construction of a regression model. To evaluate the performance of this procedure, a partial least-squares (PLS) regression model of the ethanol concentration in a fermentation process was constructed. During fermentation, NIR/mid-IR spectra in the 10000 - 1200 cm(-1) region were acquired every 3 min, and a 2D NIR/mid-IR heterospectral correlation spectrum was calculated to investigate the correlation intensity between the NIR and mid-IR bands. NIR regions that include bands at 4343, 4416, 5778, 5904, and 5955 cm(-1), which result from the combinations and overtones of the C-H group of ethanol, were selected for use in the PLS regression models, by taking the correlation intensity of a mid-IR band at 2985 cm(-1) arising from the CH(3) asymmetric stretching vibration mode of ethanol as a reference. The predicted results indicate that the ethanol concentrations calculated from the PLS regression models fit well to those obtained by high-performance liquid chromatography. Thus, it can be concluded that the selection procedure using the NIR/mid-IR dual-region spectrometer combined with 2D NIR/mid-IR heterospectral correlation spectroscopy is a powerful method for the construction of a reliable regression model.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Step by Step: Biology Undergraduates' Problem-Solving Procedures during Multiple-Choice Assessment

ERIC Educational Resources Information Center

Prevost, Luanna B.; Lemons, Paula P.

2016-01-01

This study uses the theoretical framework of domain-specific problem solving to explore the procedures students use to solve multiple-choice problems about biology concepts. We designed several multiple-choice problems and administered them on four exams. We trained students to produce written descriptions of how they solved the problem, and this…

Examining Measurement Invariance and Differential Item Functioning with Discrete Latent Construct Indicators: A Note on a Multiple Testing Procedure

ERIC Educational Resources Information Center

Raykov, Tenko; Dimitrov, Dimiter M.; Marcoulides, George A.; Li, Tatyana; Menold, Natalja

2018-01-01

A latent variable modeling method for studying measurement invariance when evaluating latent constructs with multiple binary or binary scored items with no guessing is outlined. The approach extends the continuous indicator procedure described by Raykov and colleagues, utilizes similarly the false discovery rate approach to multiple testing, and…

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Weather Impact on Airport Arrival Meter Fix Throughput

NASA Technical Reports Server (NTRS)

Wang, Yao

2017-01-01

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Practical Guidance for Conducting Mediation Analysis With Multiple Mediators Using Inverse Odds Ratio Weighting

PubMed Central

Nguyen, Quynh C.; Osypuk, Theresa L.; Schmidt, Nicole M.; Glymour, M. Maria; Tchetgen Tchetgen, Eric J.

2015-01-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994–2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. PMID:25693776

Toward a standard line for use in multibeam echo sounder calibration

NASA Astrophysics Data System (ADS)

Weber, Thomas C.; Rice, Glen; Smith, Michael

2018-06-01

A procedure is suggested in which a relative calibration for the intensity output of a multibeam echo sounder (MBES) can be performed. This procedure identifies a common survey line (i.e., a standard line), over which acoustic backscatter from the seafloor is collected with multiple MBES systems or by the same system multiple times. A location on the standard line which exhibits temporal stability in its seafloor backscatter response is used to bring the intensity output of the multiple MBES systems to a common reference. This relative calibration procedure has utility for MBES users wishing to generate an aggregate seafloor backscatter mosaic using multiple systems, revisiting an area to detect changes in substrate type, and comparing substrate types in the same general area but with different systems or different system settings. The calibration procedure is demonstrated using three different MBES systems over 3 different years in New Castle, NH, USA.

A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

DTIC Science & Technology

2012-03-01

Impact of global warming on monsoon variability in Pakistan. J. Anim. Pl. Sci., 21, no. 1, 107–110. Gillies, S., T. Murphree, and D. Meyer, 2012...are generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The...generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The predictands are

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

One-step global parameter estimation of kinetic inactivation parameters for Bacillus sporothermodurans spores under static and dynamic thermal processes.

PubMed

Cattani, F; Dolan, K D; Oliveira, S D; Mishra, D K; Ferreira, C A S; Periago, P M; Aznar, A; Fernandez, P S; Valdramidis, V P

2016-11-01

Bacillus sporothermodurans produces highly heat-resistant endospores, that can survive under ultra-high temperature. High heat-resistant sporeforming bacteria are one of the main causes for spoilage and safety of low-acid foods. They can be used as indicators or surrogates to establish the minimum requirements for heat processes, but it is necessary to understand their thermal inactivation kinetics. The aim of the present work was to study the inactivation kinetics under both static and dynamic conditions in a vegetable soup. Ordinary least squares one-step regression and sequential procedures were applied for estimating these parameters. Results showed that multiple dynamic heating profiles, when analyzed simultaneously, can be used to accurately estimate the kinetic parameters while significantly reducing estimation errors and data collection. Copyright © 2016 Elsevier Ltd. All rights reserved.

Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

PubMed

Hou, T J; Wang, J M; Liao, N; Xu, X J

1999-01-01

Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.

Schooling, occupational motivation, and personality as related to success in paramedical training.

PubMed Central

Booth, R F; Webster, E G; McNally, M S

1976-01-01

Measures of prior school experience, motivation for working in a health care job, and personality were evaluated as potential predictors of success in Navy paramedical training. When, by multiple regression procedures, years of school completed, numbers of suspensions or expulsions from school, occupational motivation, and Comrey personality scale scores were combined with an aptitude measure that is used for guiding recruits into paramedical training, the cross-validity for predicting training completion was significantly increased (P less than 0.001) from 0.40 to 0.50. Practical means for applying these measures to the screening of candidates for paramedical training were developed. Results of the evaluation suggest that guiding people into jobs that they neither prefer nor perceive as congruent with their abilities and interests can significantly reduce the chances for occupational success. PMID:825919

Reduction of shading-derived artifacts in skin chromophore imaging without measurements or assumptions about the shape of the subject

NASA Astrophysics Data System (ADS)

Yoshida, Kenichiro; Nishidate, Izumi; Ojima, Nobutoshi; Iwata, Kayoko

2014-01-01

To quantitatively evaluate skin chromophores over a wide region of curved skin surface, we propose an approach that suppresses the effect of the shading-derived error in the reflectance on the estimation of chromophore concentrations, without sacrificing the accuracy of that estimation. In our method, we use multiple regression analysis, assuming the absorbance spectrum as the response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as the predictor variables. The concentrations of melanin and total hemoglobin are determined from the multiple regression coefficients using compensation formulae (CF) based on the diffuse reflectance spectra derived from a Monte Carlo simulation. To suppress the shading-derived error, we investigated three different combinations of multiple regression coefficients for the CF. In vivo measurements with the forearm skin demonstrated that the proposed approach can reduce the estimation errors that are due to shading-derived errors in the reflectance. With the best combination of multiple regression coefficients, we estimated that the ratio of the error to the chromophore concentrations is about 10%. The proposed method does not require any measurements or assumptions about the shape of the subjects; this is an advantage over other studies related to the reduction of shading-derived errors.

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

ERIC Educational Resources Information Center

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

The relationship between health-related fitness and quality of life in postmenopausal women from Southern Taiwan

PubMed Central

Hsu, Wei-Hsiu; Chen, Chi-lung; Kuo, Liang Tseng; Fan, Chun-Hao; Lee, Mel S; Hsu, Robert Wen-Wei

2014-01-01

Background Health-related fitness has been reported to be associated with improved quality of life (QoL) in the elderly. Health-related fitness is comprised of several dimensions that could be enhanced by specific training regimens. It has remained unclear how various dimensions of health-related fitness interact with QoL in postmenopausal women. Objective The purpose of the current study was to investigate the relationship between the dimensions of health-related fitness and QoL in elderly women. Methods A cohort of 408 postmenopausal women in a rural area of Taiwan was prospectively collected. Dimensions of health-related fitness, consisting of muscular strength, balance, cardiorespiratory endurance, flexibility, muscle endurance, and agility, were assessed. QoL was determined using the Short Form Health Survey (SF-36). Differences between age groups (stratified by decades) were calculated using a one-way analysis of variance (ANOVA) and multiple comparisons using a Scheffé test. A Spearman’s correlation analysis was performed to examine differences between QoL and each dimension of fitness. Multiple linear regression with forced-entry procedure was performed to evaluate the effects of health-related fitness. A P-value of <0.05 was considered statistically significant. Results Age-related decreases in health-related fitness were shown for sit-ups, back strength, grip strength, side steps, trunk extension, and agility (P<0.05). An age-related decrease in QoL, specifically in physical functioning, role limitation due to physical problems, and physical component score, was also demonstrated (P<0.05). Multiple linear regression analyses demonstrated that back strength significantly contributed to the physical component of QoL (adjusted beta of 0.268 [P<0.05]). Conclusion Back strength was positively correlated with the physical component of QoL among the examined dimensions of health-related fitness. Health-related fitness, as well as the physical component of QoL, declined with increasing age. PMID:25258526

A Longitudinal Study of the Predictors of Perceived Procedural Justice in Australian University Staff.

PubMed

Pignata, Silvia; Winefield, Anthony H; Provis, Chris; Boyd, Carolyn M

2016-01-01

This study examined the factors that predict employees' perceptions of procedural justice in university settings. The paper also reviews the ethical aspects of justice and psychological contracts within employment relationships. The study examined the predictors of perceived procedural justice in a two-wave longitudinal sample of 945 employees from 13 universities by applying the Job Demands-Resources theoretical model of stress. The proposed predictors were classified into two categories: Job demands of work pressure and work-home conflict; and job resources of job security, autonomy, trust in senior management, and trust in supervisor. The predictor model also examined job satisfaction and affective organizational commitment, demographic (age, gender, tenure, role) and individual characteristics (negative affectivity, job involvement) as well as Time 1 (T1) perceptions of procedural justice to ensure that tests were rigorous. A series of hierarchical multiple regression analyses found that job satisfaction at T1 was the strongest predictor of perceived procedural justice at Time 2. Employees' trust in senior management, and their length of tenure also positively predicted justice perceptions. There were also differences between academic and non-academic staff groups, as non-academic employees' level of job satisfaction, trust in senior management, and their length of organizational tenure predicted procedural justice perceptions, whereas for academics, only job satisfaction predicted perceived justice. For the "all staff" category, job satisfaction was a dominant and enduring predictor of justice, and employees' trust in senior management also predicted justice. Results highlight the importance of workplace factors in enhancing fair procedures to encourage reciprocity from employees. As perceived procedural justice is also conceptually linked to the psychological contract between employees-employers, it is possible that employees' levels of job satisfaction and perceptions of trust in senior management, relative to other work attitudinal outcomes, may be more effective for improving the broader working environment, and promoting staff morale. This study adds to research on applied business ethics as it focuses on the ethical aspects of perceived procedural justice and highlights the importance of workplace factors in enhancing fair procedures in organizational policy to encourage reciprocity and promote healthy organizational environments.

A Longitudinal Study of the Predictors of Perceived Procedural Justice in Australian University Staff

PubMed Central

Pignata, Silvia; Winefield, Anthony H.; Provis, Chris; Boyd, Carolyn M.

2016-01-01

Purpose: This study examined the factors that predict employees' perceptions of procedural justice in university settings. The paper also reviews the ethical aspects of justice and psychological contracts within employment relationships. Design/Methodology/Approach: The study examined the predictors of perceived procedural justice in a two-wave longitudinal sample of 945 employees from 13 universities by applying the Job Demands-Resources theoretical model of stress. The proposed predictors were classified into two categories: Job demands of work pressure and work-home conflict; and job resources of job security, autonomy, trust in senior management, and trust in supervisor. The predictor model also examined job satisfaction and affective organizational commitment, demographic (age, gender, tenure, role) and individual characteristics (negative affectivity, job involvement) as well as Time 1 (T1) perceptions of procedural justice to ensure that tests were rigorous. Findings: A series of hierarchical multiple regression analyses found that job satisfaction at T1 was the strongest predictor of perceived procedural justice at Time 2. Employees' trust in senior management, and their length of tenure also positively predicted justice perceptions. There were also differences between academic and non-academic staff groups, as non-academic employees' level of job satisfaction, trust in senior management, and their length of organizational tenure predicted procedural justice perceptions, whereas for academics, only job satisfaction predicted perceived justice. For the “all staff” category, job satisfaction was a dominant and enduring predictor of justice, and employees' trust in senior management also predicted justice. Research limitations/implications: Results highlight the importance of workplace factors in enhancing fair procedures to encourage reciprocity from employees. As perceived procedural justice is also conceptually linked to the psychological contract between employees-employers, it is possible that employees' levels of job satisfaction and perceptions of trust in senior management, relative to other work attitudinal outcomes, may be more effective for improving the broader working environment, and promoting staff morale. Originality/value: This study adds to research on applied business ethics as it focuses on the ethical aspects of perceived procedural justice and highlights the importance of workplace factors in enhancing fair procedures in organizational policy to encourage reciprocity and promote healthy organizational environments. PMID:27610093

Detection of Differential Item Functioning with Nonlinear Regression: A Non-IRT Approach Accounting for Guessing

ERIC Educational Resources Information Center

Drabinová, Adéla; Martinková, Patrícia

2017-01-01

In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…

Risk factors for infection after endoscopic ultrasonography-guided drainage of specific types of pancreatic and peripancreatic fluid collections (with video).

PubMed

Guo, Jintao; Feng, Linlin; Sun, Siyu; Ge, Nan; Liu, Xiang; Wang, Sheng; Wang, Guoxin; Sun, Beibei

2016-07-01

Endoscopic ultrasonography (EUS)-guided drainage is widely used for the treatment of specific types of peripancreatic fluid collections (PFCs). Infectious complications have been reported. It is recommended that the infection rate should be assessed by measuring risk factors. The objectives of this study were to measure whether the risk of infection after EUS-guided drainage was associated with patient- and procedure-related factors. Eighty-three patients were eligible for inclusion from September 2008 to November 2012. EUS-guided drainage was performed in all patients. Infectious complications were observed, and data on patient- and procedure-related factors were collected. Patient-related factors mainly included age, sex, etiology of PFC, and cyst location and diameter. Procedure-related factors mainly included approach of EUS-guided drainage and stent diameter. Separate multivariate logistic regression models for all EUS-guided drainage were carried out. Complete EUS-guided drainage was achieved in all patients. A definitive diagnosis of infection after EUS-guided drainage was made in seven patients. All seven patients had a history of acute pancreatitis, and the cyst diameters were all >15 cm. Three patients had diabetes mellitus. The cyst diameter was an independent risk factor for infection. Larger cysts with a diameter >15 cm should perhaps be drained initially with multiple pigtail or a larger diameter self-expandable metal stents to try to avoid infection.

Reduction of catheter-associated bloodstream infections through procedures in newborn babies admitted in a university hospital intensive care unit in Brazil.

PubMed

Resende, Daiane Silva; Ó, Jacqueline Moreira do; Brito, Denise von Dolinger de; Abdallah, Vânia Olivetti Steffen; Gontijo Filho, Paulo Pinto

2011-01-01

Catheter-associated bloodstream infection (CA-BSI) is the most common nosocomial infection in neonatal intensive care units. There is evidence that care bundles to reduce CA-BSI are effective in the adult literature. The aim of this study was to reduce CA-BSI in a Brazilian neonatal intensive care unit by means of a care bundle including few strategies or procedures of prevention and control of these infections. An intervention designed to reduce CA-BSI with five evidence-based procedures was conducted. A total of sixty-seven (26.7%) CA-BSIs were observed. There were 46 (32%) episodes of culture-proven sepsis in group preintervention (24.1 per 1,000 catheter days [CVC days]). Neonates in the group after implementation of the intervention had 21 (19.6%) episodes of CA-BSI (14.9 per 1,000 CVC days). The incidence of CA-BSI decreased significantly after the intervention from the group preintervention and postintervention (32% to 19.6%, 24.1 per 1,000 CVC days to 14.9 per 1,000 CVC days, p=0.04). In the multiple logistic regression analysis, the use of more than 3 antibiotics and length of stay >8 days were independent risk factors for BSI. A stepwise introduction of evidence-based intervention and intensive and continuous education of all healthcare workers are effective in reducing CA-BSI.

Virtual reality laparoscopy: which potential trainee starts with a higher proficiency level?

PubMed

Paschold, M; Schröder, M; Kauff, D W; Gorbauch, T; Herzer, M; Lang, H; Kneist, W

2011-09-01

Minimally invasive surgery requires technical skills distinct from those used in conventional surgery. The aim of this prospective study was to identify personal characteristics that may predict the attainable proficiency level of first-time virtual reality laparoscopy (VRL) trainees. Two hundred and seventy-nine consecutive undergraduate medical students without experience attended a standardized VRL training. Performance data of an abstract and a procedural task were correlated with possible predictive factors providing potential competence in VRL. Median global score requirement status was 86.7% (interquartile range (IQR) 75-93) for the abstract task and 74.4% (IQR 67-88) for the procedural task. Unadjusted analysis showed significant increase in the global score in both tasks for trainees who had a gaming console at home and frequently used it as well as for trainees who felt self-confident to assist in a laparoscopic operation. Multiple logistic regression analysis identified frequency of video gaming (often/frequently vs. rarely/not at all, odds ratio: abstract model 2.1 (95% confidence interval 1.2; 3.6), P = 0.009; virtual reality operation procedure 2.4 (95% confidence interval 1.3; 4.2), P = 0.003) as a predictive factor for VRL performance. Frequency of video gaming is associated with quality of first-time VRL performance. Video game experience may be used as trainee selection criteria for tailored concepts of VRL training programs.

Multiple Regression Redshift Calibration for Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Kalinkov, M.; Kuneva, I.; Valtchanov, I.

A new procedure for calibration of distances to ACO (Abell et al.1989) clusters of galaxies has been developed. In the previous version of the Reference Catalog of ACO Clusters of Galaxies (Kalinkov & Kuneva 1992) an attempt has been made to compare various calibration schemes. For the Version 93 we have made some refinements. Many improvements from the early days of the photometric calibration have been made --- from Rowan-Robinson (1972), Corwin (1974), Kalinkov & Kuneva (1975), Mills Hoskins (1977) to more complicated --- Leir & van den Bergh (1977), Postman et al.(1985), Kalinkov Kuneva (1985, 1986, 1990), Scaramella et al.(1991), Zucca et al. (1993). It was shown that it is impossible to use the same calibration relation for northern (A) and southern (ACO) clusters of galaxies. Therefore the calibration have to be made separately for both catalogs. Moreover it is better if one could find relations for the 274 A-clusters, studied by the authors of ACO. We use the luminosity distance for H0=100km/s/Mpc and q0 = 0.5 and we have 1200 clusters with measured redshifts. The first step is to fit log(z) on m10 (magnitude of the tenth rank galaxy) for A-clusters and on m1, m3 and m10 for ACO clusters. The second step is to take into account the K-correction and the Scott effect (Postman et al.1985) with iterative process. To avoid the initial errors of the redshift estimates in A- and ACO catalogs we adopt Hubble's law for the apparent radial distribution of galaxies in clusters. This enable us to calculate a new cluster richness from preliminary redshift estimate. This is the third step. Further continues the study of the correlation matrix between log(z) and prospective predictors --- new richness groups, BM, RS and A types, radio and X-ray fluxes, apparent separations between the first three brightest galaxies, mean population (gal/sq.deg), Multiple linear as well as nonlinear regression estimators are found. Many clusters that deviate by more than 2.5 sigmas are rejected. Each case is examined for observational errors, substructuring, foreground and background. Some of the clusters are doubtful --- most probably they have to be excluded from the catalogs. The multiple regressions allow us to estimate redshift in the range 0.02 to 0.2 with an error of 7 percent.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Retrospective study of the prevalence of postanaesthetic hypothermia in dogs.

PubMed

Redondo, J I; Suesta, P; Serra, I; Soler, C; Soler, G; Gil, L; Gómez-Villamandos, R J

2012-10-13

The anaesthetic records of 1525 dogs were examined to determine the prevalence of postanaesthetic hypothermia, its clinical predictors and consequences. Temperature was recorded throughout the anaesthesia. At the end of the procedure, details coded in were: hyperthermia (>39.50°C), normothermia (38.50°C-39.50°C), slight (38.49°C-36.50°C), moderate (36.49°C-34.00°C) and severe hypothermia (<34.00°C). Statistical analysis consisted of multiple regression to identify the factors that are associated with the temperature at the end of the procedure. Before premedication, the temperature was 38.7 ± 0.6°C (mean ± sd). At 60, 120 and 180 minutes from induction, the temperature was 36.7 ± 1.3°C, 36.1 ± 1.4°C and 35.8 ± 1.5°C, respectively. The prevalence of hypothermia was: slight, 51.5 per cent (95 per cent CI 49.0 to 54.0 per cent); moderate, 29.3 per cent (27.1-31.7 per cent) and severe: 2.8% (2.0-3.7%). The variables that associated with a decrease in the temperature recorded at the end of the anaesthesia were: duration of the preanesthetic time, duration of the anaesthesia, physical condition (ASA III and ASA IV dogs showed lower temperatures than ASA I dogs), the reason for anaesthesia (anaesthesia for diagnostic procedures or thoracic surgery reduce the temperature when compared with minor procedures), and the recumbency during the procedure (sternal and dorsal recumbencies showed lower temperatures than lateral recumbency). The temperature before premedication and the body surface (BS) were associated with a higher temperature at the end of the anaesthesia, and would be considered as protective factors.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

NASA Technical Reports Server (NTRS)

Dawson, Terence P.; Curran, Paul J.; Kupiec, John A.

1995-01-01

A major goal of airborne imaging spectrometry is to estimate the biochemical composition of vegetation canopies from reflectance spectra. Remotely-sensed estimates of foliar biochemical concentrations of forests would provide valuable indicators of ecosystem function at regional and eventually global scales. Empirical research has shown a relationship exists between the amount of radiation reflected from absorption features and the concentration of given biochemicals in leaves and canopies (Matson et al., 1994, Johnson et al., 1994). A technique commonly used to determine which wavelengths have the strongest correlation with the biochemical of interest is unguided (stepwise) multiple regression. Wavelengths are entered into a multivariate regression equation, in their order of importance, each contributing to the reduction of the variance in the measured biochemical concentration. A significant problem with the use of stepwise regression for determining the correlation between biochemical concentration and spectra is that of 'overfitting' as there are significantly more wavebands than biochemical measurements. This could result in the selection of wavebands which may be more accurately attributable to noise or canopy effects. In addition, there is a real problem of collinearity in that the individual biochemical concentrations may covary. A strong correlation between the reflectance at a given wavelength and the concentration of a biochemical of interest, therefore, may be due to the effect of another biochemical which is closely related. Furthermore, it is not always possible to account for potentially suitable waveband omissions in the stepwise selection procedure. This concern about the suitability of stepwise regression has been identified and acknowledged in a number of recent studies (Wessman et al., 1988, Curran, 1989, Curran et al., 1992, Peterson and Hubbard, 1992, Martine and Aber, 1994, Kupiec, 1994). These studies have pointed to the lack of a physical link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.

SYSTEMATIC PROCEDURE FOR DESIGNING PROCESSES WITH MULTIPLE ENVIRONMENTAL OBJECTIVES

EPA Science Inventory

Evaluation of multiple objectives is very important in designing environmentally benign processes. It requires a systematic procedure for solving multiobjective decision-making problems, due to the complex nature of the problems, the need for complex assessments, and complicated ...

A method for nonlinear exponential regression analysis

NASA Technical Reports Server (NTRS)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

PubMed Central

Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

2015-01-01

Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Ovarian stimulation length, number of follicles higher than 17 mm and estradiol on the day of human chorionic gonadotropin administration are risk factors for multiple pregnancy in intrauterine insemination

PubMed Central

MELO, MARCO A.B.; SIMÓN, CARLOS; REMOHÍ, JOSÉ; PELLICER, ANTONIO; MESEGUER, MARCOS

2007-01-01

Aim:  The aim of the present study was to identify the risk factors, their prognostic value on multiple pregnancies (MP) prediction and their thresholds in women undergoing controlled ovarian hyperstimulation (COH) with follicle stimulating hormone (FSH) and intrauterine insemination (IUI). Methods:  A case‐control study was carried out by identifying in our database all the pregnancies reached by donor and conjugal IUI (DIUI and CIUI, respectively), and compared cycle features, patients’ characteristics and sperm analysis results between women achieving single pregnancy (SP) versus MP. The number of gestational sacs, follicular sizes and estradiol levels on the human chorionic gonadotropin (hCG) administration day, COH length and semen parameters were obtained from each cycle and compared. Student's t‐tests for mean comparisons, receiver–operator curve (ROC) analysis to determine the predictive value of each parameter on MP achievement and multiple regression analysis to determine single parameter influence were carried out. Results:  Women with MP in IUI stimulated cycles reached the adequate size of the dominant follicle (17 mm) significantly earlier than those achieving SP. Also, the mean follicles number, and estradiol levels on the hCG day were higher in the CIUI and DIUI MP group. Nevertheless, only ROC curve analysis revealed good prognostic value for estradiol and follicles higher than 17 mm. Multiple regression analysis confirmed these results. No feature of the basic sperm analysis, either in the ejaculate or in the prepared sample, was different or predictive of MP. When using donor sperm, different thresholds of follicle number, stimulation length and estradiol in the prediction of MP were noted, in comparison with CIUI. Conclusions:  MP in stimulated IUI cycles are closely associated to stimulation length, number of developed follicles higher than 17 mm on the day of hCG administration and estradiol levels. Also, estradiol has a good predictive value over MP in IUI stimulated cycles. The establishment of clinical thresholds will certainly help in the management of these couples to avoid undesired multiple pregnancies by canceling cycles or converting them into in vitro fertilization procedures. (Reprod Med Biol 2007; 6: 19–26) PMID:29699262

Genome-wide regression and prediction with the BGLR statistical package.

PubMed

Pérez, Paulino; de los Campos, Gustavo

2014-10-01

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis. Copyright © 2014 by the Genetics Society of America.

A two-step hierarchical hypothesis set testing framework, with applications to gene expression data on ordered categories

PubMed Central

2014-01-01

Background In complex large-scale experiments, in addition to simultaneously considering a large number of features, multiple hypotheses are often being tested for each feature. This leads to a problem of multi-dimensional multiple testing. For example, in gene expression studies over ordered categories (such as time-course or dose-response experiments), interest is often in testing differential expression across several categories for each gene. In this paper, we consider a framework for testing multiple sets of hypothesis, which can be applied to a wide range of problems. Results We adopt the concept of the overall false discovery rate (OFDR) for controlling false discoveries on the hypothesis set level. Based on an existing procedure for identifying differentially expressed gene sets, we discuss a general two-step hierarchical hypothesis set testing procedure, which controls the overall false discovery rate under independence across hypothesis sets. In addition, we discuss the concept of the mixed-directional false discovery rate (mdFDR), and extend the general procedure to enable directional decisions for two-sided alternatives. We applied the framework to the case of microarray time-course/dose-response experiments, and proposed three procedures for testing differential expression and making multiple directional decisions for each gene. Simulation studies confirm the control of the OFDR and mdFDR by the proposed procedures under independence and positive correlations across genes. Simulation results also show that two of our new procedures achieve higher power than previous methods. Finally, the proposed methodology is applied to a microarray dose-response study, to identify 17 β-estradiol sensitive genes in breast cancer cells that are induced at low concentrations. Conclusions The framework we discuss provides a platform for multiple testing procedures covering situations involving two (or potentially more) sources of multiplicity. The framework is easy to use and adaptable to various practical settings that frequently occur in large-scale experiments. Procedures generated from the framework are shown to maintain control of the OFDR and mdFDR, quantities that are especially relevant in the case of multiple hypothesis set testing. The procedures work well in both simulations and real datasets, and are shown to have better power than existing methods. PMID:24731138

Proceedings of the Conference on the Design of Experiments in Army Research Development and Testing (26th) Held at New Mexico State University, Las Cruces, New Mexico on 22-24 October 1980.

DTIC Science & Technology

1981-06-01

normality and several types of nonnormality. Overall the rank transformation procedure seems to be the best. The Fisher’s LSD multiple comparisons procedure...the rank transformation procedure appears to maintain power better than Fisher’s LSD or the randomization proce- dures. The conclusion of this study...best. The Fisher’s LSD multiple comparisons procedure in the one way and two way layouts iv compared with a randomization procedure and with the same

Prediction of sickness absence: development of a screening instrument

PubMed Central

Duijts, S F A; Kant, IJ; Landeweerd, J A; Swaen, G M H

2006-01-01

Objectives To develop a concise screening instrument for early identification of employees at risk for sickness absence due to psychosocial health complaints. Methods Data from the Maastricht Cohort Study on “Fatigue at Work” were used to identify items to be associated with an increased risk of sickness absence. The analytical procedures univariate logistic regression, backward stepwise linear regression, and multiple logistic regression were successively applied. For both men and women, sum scores were calculated, and sensitivity and specificity rates of different cut‐off points on the screening instrument were defined. Results In women, results suggested that feeling depressed, having a burnout, being tired, being less interested in work, experiencing obligatory change in working days, and living alone, were strong predictors of sickness absence due to psychosocial health complaints. In men, statistically significant predictors were having a history of sickness absence, compulsive thinking, being mentally fatigued, finding it hard to relax, lack of supervisor support, and having no hobbies. A potential cut‐off point of 10 on the screening instrument resulted in a sensitivity score of 41.7% for women and 38.9% for men, and a specificity score of 91.3% for women and 90.6% for men. Conclusions This study shows that it is possible to identify predictive factors for sickness absence and to develop an instrument for early identification of employees at risk for sickness absence. The results of this study increase the possibility for both employers and policymakers to implement interventions directed at the prevention of sickness absence. PMID:16698807

Downward trends in surgical site and urinary tract infections after cesarean delivery in a French surveillance network, 1997-2003.

PubMed

Vincent, Agnès; Ayzac, Louis; Girard, Raphaële; Caillat-Vallet, Emmanuelle; Chapuis, Catherine; Depaix, Florence; Dumas, Anne-Marie; Gignoux, Chantal; Haond, Catherine; Lafarge-Leboucher, Joëlle; Launay, Carine; Tissot-Guerraz, Françoise; Fabry, Jacques

2008-03-01

To evaluate whether the adjusted rates of surgical site infection (SSI) and urinary tract infection (UTI) after cesarean delivery decrease in maternity units that perform active healthcare-associated infection surveillance. Trend analysis by means of multiple logistic regression. A total of 80 maternity units participating in the Mater Sud-Est surveillance network. A total of 37,074 cesarean deliveries were included in the surveillance from January 1, 1997, through December 31, 2003. We used a logistic regression model to estimate risk-adjusted post-cesarean delivery infection odds ratios. The variables included were the maternity units' annual rate of operative procedures, the level of dispensed neonatal care, the year of delivery, maternal risk factors, and the characteristics of cesarean delivery. The trend of risk-adjusted odds ratios for SSI and UTI during the study period was studied by linear regression. The crude rates of SSI and UTI after cesarean delivery were 1.5% (571 of 37,074 patients) and 1.8% (685 of 37,074 patients), respectively. During the study period, the decrease in SSI and UTI adjusted odds ratios was statistically significant (R=-0.823 [P=.023] and R=-0.906 [P=.005], respectively). Reductions of 48% in the SSI rate and 52% in the UTI rate were observed in the maternity units. These unbiased trends could be related to progress in preventive practices as a result of the increased dissemination of national standards and a collaborative surveillance with benchmarking of rates.

System dynamic modeling: an alternative method for budgeting.

PubMed

Srijariya, Witsanuchai; Riewpaiboon, Arthorn; Chaikledkaew, Usa

2008-03-01

To construct, validate, and simulate a system dynamic financial model and compare it against the conventional method. The study was a cross-sectional analysis of secondary data retrieved from the National Health Security Office (NHSO) in the fiscal year 2004. The sample consisted of all emergency patients who received emergency services outside their registered hospital-catchments area. The dependent variable used was the amount of reimbursed money. Two types of model were constructed, namely, the system dynamic model using the STELLA software and the multiple linear regression model. The outputs of both methods were compared. The study covered 284,716 patients from various levels of providers. The system dynamic model had the capability of producing various types of outputs, for example, financial and graphical analyses. For the regression analysis, statistically significant predictors were composed of service types (outpatient or inpatient), operating procedures, length of stay, illness types (accident or not), hospital characteristics, age, and hospital location (adjusted R(2) = 0.74). The total budget arrived at from using the system dynamic model and regression model was US$12,159,614.38 and US$7,301,217.18, respectively, whereas the actual NHSO reimbursement cost was US$12,840,805.69. The study illustrated that the system dynamic model is a useful financial management tool, although it is not easy to construct. The model is not only more accurate in prediction but is also more capable of analyzing large and complex real-world situations than the conventional method.

Retrieval of total suspended matter concentrations from high resolution WorldView-2 imagery: a case study of inland rivers

NASA Astrophysics Data System (ADS)

Shi, Liangliang; Mao, Zhihua; Wang, Zheng

2018-02-01

Satellite imagery has played an important role in monitoring water quality of lakes or coastal waters presently, but scarcely been applied in inland rivers. This paper presents an attempt of feasibility to apply regression model to quantify and map the concentrations of total suspended matter (CTSM) in inland rivers which have a large scale of spatial and a high CTSM dynamic range by using high resolution satellite remote sensing data, WorldView-2. An empirical approach to quantify CTSM by integrated use of high resolution WorldView-2 multispectral data and 21 in situ CTSM measurements. Radiometric correction, geometric and atmospheric correction involved in image processing procedure is carried out for deriving the surface reflectance to correlate the CTSM and satellite data by using single-variable and multivariable regression technique. Results of regression model show that the single near-infrared (NIR) band 8 of WorldView-2 have a relative strong relationship (R2=0.93) with CTSM. Different prediction models were developed on various combinations of WorldView-2 bands, the Akaike Information Criteria approach was used to choose the best model. The model involving band 1, 3, 5, and 8 of WorldView-2 had a best performance, whose R2 reach to 0.92, with SEE of 53.30 g/m3. The spatial distribution maps were produced by using the best multiple regression model. The results of this paper indicated that it is feasible to apply the empirical model by using high resolution satellite imagery to retrieve CTSM of inland rivers in routine monitoring of water quality.

Step by Step: Biology Undergraduates’ Problem-Solving Procedures during Multiple-Choice Assessment

PubMed Central

Prevost, Luanna B.; Lemons, Paula P.

2016-01-01

This study uses the theoretical framework of domain-specific problem solving to explore the procedures students use to solve multiple-choice problems about biology concepts. We designed several multiple-choice problems and administered them on four exams. We trained students to produce written descriptions of how they solved the problem, and this allowed us to systematically investigate their problem-solving procedures. We identified a range of procedures and organized them as domain general, domain specific, or hybrid. We also identified domain-general and domain-specific errors made by students during problem solving. We found that students use domain-general and hybrid procedures more frequently when solving lower-order problems than higher-order problems, while they use domain-specific procedures more frequently when solving higher-order problems. Additionally, the more domain-specific procedures students used, the higher the likelihood that they would answer the problem correctly, up to five procedures. However, if students used just one domain-general procedure, they were as likely to answer the problem correctly as if they had used two to five domain-general procedures. Our findings provide a categorization scheme and framework for additional research on biology problem solving and suggest several important implications for researchers and instructors. PMID:27909021

The prediction of intelligence in preschool children using alternative models to regression.

PubMed

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Respiratory system mechanics during laparoscopic cholecystectomy.

PubMed

Rizzotti, L; Vassiliou, M; Amygdalou, A; Psarakis, Ch; Rasmussen, T R; Laopodis, V; Behrakis, P

2002-04-01

The influence of laparoscopic cholecystectomy (LC) on the mechanical properties of the respiratory system (RS) was examined using multiple regression analysis (MRA). Measurements of airway pressure (PaO) and flow (V') were obtained from 32 patients at four distinct stages of the LC procedure: 1) Immediately before the application of pneumoperitoneum (PP) at supine position, 2) 5 min after the induction of PP at Trendelenburg position, 3) 5 min after the patients position at reverse Trendelenburg, and 4) 5 min after the end ofthe surgical procedure with the patient again in supine position. Evaluated parameters were the RS elastance (Ers), resistance (Rrs), impedance (Zrs), the angle theta indicating the balance between the elastic and resistive components of the impedance, as well as the end-expiratory elastic recoil pressure (EEP). Ers and Zrs increased considerably during PP and remained elevated immediately after abolishing PP Rrs, on the contrary, returned to pre-operative levels right after the operation. Change of body position from Trendelenburg (T) to reverseTrendelenburg (rT) mainly induced a significant change in theta, thus indicating an increased dominance of the elastic component of Zrs on changing fromT to rT. There was no evidence of increased End-Expiratory Pressure during PP

Preoperative psychological adjustment and surgical outcome are determinants of psychosocial status after anterior temporal lobectomy.

PubMed Central

Hermann, B P; Wyler, A R; Somes, G

1992-01-01

This investigation evaluated the role of preoperative psychological adjustment, degree of postoperative seizure reduction, and other relevant variables (age, education, IQ, age at onset of epilepsy, laterality of resection) in determining emotional/psychosocial outcome following anterior temporal lobectomy. Ninety seven patients with complex partial seizures of temporal lobe origin were administered the Minnesota Multiphasic Personality Inventory (MMPI), Washington Psychosocial Seizure Inventory (WPSI), and the General Health Questionnaire (GHQ) both before and six to eight months after anterior temporal lobectomy. The data were subjected to a nonparametric rank sum technique (O'Brien's procedure) which combined the test scores to form a single outcome index (TOTAL PSYCHOSOCIAL OUTCOME) that was analysed by multiple regression procedures. Results indicated that the most powerful predictors of patients' overall postoperative psychosocial outcome were: 1) The adequacy of their preoperative psychosocial adjustment, and 2) A totally seizure-free outcome. Additional analyses were carried out separately on the MMPI, WPSI, and GHQ to determine whether findings varied as a function of the specific outcome measure. These results were related to the larger literature concerned with the psychological outcome of anterior temporal lobectomy. PMID:1619418

Optimization of fixture layouts of glass laser optics using multiple kernel regression.

PubMed

Su, Jianhua; Cao, Enhua; Qiao, Hong

2014-05-10

We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

Stem cell collection in unmanipulated HLA-haploidentical/mismatched related transplantation with combined granulocyte-colony stimulating factor-mobilised blood and bone marrow for patients with haematologic malignancies: the impact of donor characteristics and procedural settings.

PubMed

Zhang, C; Chen, X-H; Zhang, X; Gao, L; Gao, L; Kong, P-Y; Peng, X-G; Sun, A-H; Gong, Y; Zeng, D-F; Wang, Q-Y

2010-06-01

Unmanipulated haploidentical/mismatched related transplantation with combined granulocyte-colony stimulating factor-mobilised peripheral blood stem cells (G-PBSCs) and granulocyte-colony stimulating factor-mobilised bone marrow (G-BM) has been developed as an alternative transplantation strategy for patients with haematologic malignancies. However, little information is available about the factors predicting the outcome of peripheral blood stem cell (PBSC) collection and bone marrow (BM) harvest in this transplantation. The effects of donor characteristics and procedure factors on CD34(+) cell yield were investigated. A total of 104 related healthy donors received granulocyte-colony stimulating factor (G-CSF) followed by PBSC collection and BM harvest. Male donors had significantly higher yields compared with female donors. In multiple regression analysis for peripheral blood collection, age and flow rate were negatively correlated with cell yield, whereas body mass index, pre-aphaeresis white blood cell (WBC) and circulating immature cell (CIC) counts were positively correlated with cell yields. For BM harvest, age was negatively correlated with cell yields, whereas pre-BM collection CIC counts were positively correlated with cell yield. All donors achieved the final product of >or=6 x10(6) kg(-1) recipient body weight. This transplantation strategy has been shown to be a feasible approach with acceptable outcomes in stem cell collection for patients who received HLA-haploidentical/mismatched transplantation with combined G-PBSCs and G-BM. In donors with multiple high-risk characteristics for poor aphaeresis CD34(+) cell yield, BM was an alternative source.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

NASA Astrophysics Data System (ADS)

Sirenko, M. A.; Tarasenko, P. F.; Pushkarev, M. I.

2017-01-01

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Thalamic volume deficit contributes to procedural and explicit memory impairment in HIV infection with primary alcoholism comorbidity.

PubMed

Fama, Rosemary; Rosenbloom, Margaret J; Sassoon, Stephanie A; Rohlfing, Torsten; Pfefferbaum, Adolf; Sullivan, Edith V

2014-12-01

Component cognitive and motor processes contributing to diminished visuomotor procedural learning in HIV infection with comorbid chronic alcoholism (HIV+ALC) include problems with attention and explicit memory processes. The neural correlates associated with this constellation of cognitive and motor processes in HIV infection and alcoholism have yet to be delineated. Frontostriatal regions are affected in HIV infection, frontothalamocerebellar regions are affected in chronic alcoholism, and frontolimbic regions are likely affected in both; all three of these systems have the potential of contributing to both visuomotor procedural learning and explicit memory processes. Here, we examined the neural correlates of implicit memory, explicit memory, attention, and motor tests in 26 HIV+ALC (5 with comorbidity for nonalcohol drug abuse/dependence) and 19 age-range matched healthy control men. Parcellated brain volumes, including cortical, subcortical, and allocortical regions, as well as cortical sulci and ventricles, were derived using the SRI24 brain atlas. Results indicated that smaller thalamic volumes were associated with poorer performance on tests of explicit (immediate and delayed) and implicit (visuomotor procedural) memory in HIV+ALC. By contrast, smaller hippocampal volumes were associated with lower scores on explicit, but not implicit memory. Multiple regression analyses revealed that volumes of both the thalamus and the hippocampus were each unique independent predictors of explicit memory scores. This study provides evidence of a dissociation between implicit and explicit memory tasks in HIV+ALC, with selective relationships observed between hippocampal volume and explicit but not implicit memory, and highlights the relevance of the thalamus to mnemonic processes.

CT fluoroscopy-guided renal tumour cutting needle biopsy: retrospective evaluation of diagnostic yield, safety, and risk factors for diagnostic failure.

PubMed

Iguchi, Toshihiro; Hiraki, Takao; Matsui, Yusuke; Fujiwara, Hiroyasu; Sakurai, Jun; Masaoka, Yoshihisa; Gobara, Hideo; Kanazawa, Susumu

2018-01-01

To evaluate retrospectively the diagnostic yield, safety, and risk factors for diagnostic failure of computed tomography (CT) fluoroscopy-guided renal tumour biopsy. Biopsies were performed for 208 tumours (mean diameter 2.3 cm; median diameter 2.1 cm; range 0.9-8.5 cm) in 199 patients. One hundred and ninety-nine tumours were ≤4 cm. All 208 initial procedures were divided into diagnostic success and failure groups. Multiple variables related to the patients, lesions, and procedures were assessed to determine the risk factors for diagnostic failure. After performing 208 initial and nine repeat biopsies, 180 malignancies and 15 benign tumours were pathologically diagnosed, whereas 13 were not diagnosed. In 117 procedures, 118 Grade I and one Grade IIIa adverse events (AEs) occurred. Neither Grade ≥IIIb AEs nor tumour seeding were observed within a median follow-up period of 13.7 months. Logistic regression analysis revealed only small tumour size (≤1.5 cm; odds ratio 3.750; 95% confidence interval 1.362-10.326; P = 0.011) to be a significant risk factor for diagnostic failure. CT fluoroscopy-guided renal tumour biopsy is a safe procedure with a high diagnostic yield. A small tumour size (≤1.5 cm) is a significant risk factor for diagnostic failure. • CT fluoroscopy-guided renal tumour biopsy has a high diagnostic yield. • CT fluoroscopy-guided renal tumour biopsy is safe. • Small tumour size (≤1.5 cm) is a risk factor for diagnostic failure.

POWER-ENHANCED MULTIPLE DECISION FUNCTIONS CONTROLLING FAMILY-WISE ERROR AND FALSE DISCOVERY RATES.

PubMed

Peña, Edsel A; Habiger, Joshua D; Wu, Wensong

2011-02-01

Improved procedures, in terms of smaller missed discovery rates (MDR), for performing multiple hypotheses testing with weak and strong control of the family-wise error rate (FWER) or the false discovery rate (FDR) are developed and studied. The improvement over existing procedures such as the Šidák procedure for FWER control and the Benjamini-Hochberg (BH) procedure for FDR control is achieved by exploiting possible differences in the powers of the individual tests. Results signal the need to take into account the powers of the individual tests and to have multiple hypotheses decision functions which are not limited to simply using the individual p -values, as is the case, for example, with the Šidák, Bonferroni, or BH procedures. They also enhance understanding of the role of the powers of individual tests, or more precisely the receiver operating characteristic (ROC) functions of decision processes, in the search for better multiple hypotheses testing procedures. A decision-theoretic framework is utilized, and through auxiliary randomizers the procedures could be used with discrete or mixed-type data or with rank-based nonparametric tests. This is in contrast to existing p -value based procedures whose theoretical validity is contingent on each of these p -value statistics being stochastically equal to or greater than a standard uniform variable under the null hypothesis. Proposed procedures are relevant in the analysis of high-dimensional "large M , small n " data sets arising in the natural, physical, medical, economic and social sciences, whose generation and creation is accelerated by advances in high-throughput technology, notably, but not limited to, microarray technology.

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

Estimation of premorbid general fluid intelligence using traditional Chinese reading performance in Taiwanese samples.

PubMed

Chen, Ying-Jen; Ho, Meng-Yang; Chen, Kwan-Ju; Hsu, Chia-Fen; Ryu, Shan-Jin

2009-08-01

The aims of the present study were to (i) investigate if traditional Chinese word reading ability can be used for estimating premorbid general intelligence; and (ii) to provide multiple regression equations for estimating premorbid performance on Raven's Standard Progressive Matrices (RSPM), using age, years of education and Chinese Graded Word Reading Test (CGWRT) scores as predictor variables. Four hundred and twenty-six healthy volunteers (201 male, 225 female), aged 16-93 years (mean +/- SD, 41.92 +/- 18.19 years) undertook the tests individually under supervised conditions. Seventy percent of subjects were randomly allocated to the derivation group (n = 296), and the rest to the validation group (n = 130). RSPM score was positively correlated with CGWRT score and years of education. RSPM and CGWRT scores and years of education were also inversely correlated with age, but the declining trend for RSPM performance against age was steeper than that for CGWRT performance. Separate multiple regression equations were derived for estimating RSPM scores using different combinations of age, years of education, and CGWRT score for both groups. The multiple regression coefficient of each equation ranged from 0.71 to 0.80 with the standard error of estimate between 7 and 8 RSPM points. When fitting the data of one group to the equations derived from its counterpart group, the cross-validation multiple regression coefficients ranged from 0.71 to 0.79. There were no significant differences in the 'predicted-obtained' RSPM discrepancies between any equations. The regression equations derived in the present study may provide a basis for estimating premorbid RSPM performance.

Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

PubMed

Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

2017-05-01

This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

A population-based study on the association between rheumatoid arthritis and voice problems.

PubMed

Hah, J Hun; An, Soo-Youn; Sim, Songyong; Kim, So Young; Oh, Dong Jun; Park, Bumjung; Kim, Sung-Gyun; Choi, Hyo Geun

2016-07-01

The objective of this study was to investigate whether rheumatoid arthritis increases the frequency of organic laryngeal lesions and the subjective voice complaint rate in those with no organic laryngeal lesion. We performed a cross-sectional study using the data from 19,368 participants (418 rheumatoid arthritis patients and 18,950 controls) of the 2008-2011 Korea National Health and Nutrition Examination Survey. The associations between rheumatoid arthritis and organic laryngeal lesions/subjective voice complaints were analyzed using simple/multiple logistic regression analysis with complex sample adjusting for confounding factors, including age, sex, smoking status, stress level, and body mass index, which could provoke voice problems. Vocal nodules, vocal polyp, and vocal palsy were not associated with rheumatoid arthritis in a multiple regression analysis, and only laryngitis showed a positive association (adjusted odds ratio, 1.59; 95 % confidence interval, 1.01-2.52; P = 0.047). Rheumatoid arthritis was associated with subjective voice discomfort in a simple regression analysis, but not in a multiple regression analysis. Participants with rheumatoid arthritis were older, more often female, and had higher stress levels than those without rheumatoid arthritis. These factors were associated with subjective voice complaints in both simple and multiple regression analyses. Rheumatoid arthritis was not associated with organic laryngeal diseases except laryngitis. Rheumatoid arthritis did not increase the odds ratio for subjective voice complaints. Voice problems in participants with rheumatoid arthritis originated from the characteristics of the rheumatoid arthritis group (higher mean age, female sex, and stress level) rather than rheumatoid arthritis itself.

Predicting MHC-II binding affinity using multiple instance regression

PubMed Central

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2011-01-01

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

The Impact of Metastatic Spinal Tumor Location on 30-Day Perioperative Mortality and Morbidity After Surgical Decompression.

PubMed

Hussain, Awais K; Vig, Khushdeep S; Cheung, Zoe B; Phan, Kevin; Lima, Mauricio C; Kim, Jun S; Kaji, Deepak A; Arvind, Varun; Cho, Samuel Kang-Wook

2018-06-01

A retrospective cohort study from 2011 to 2014 was performed using the American College of Surgeons National Surgical Quality Improvement Program database. The purpose of this study was to assess the impact of tumor location in the cervical, thoracic, or lumbosacral spine on 30-day perioperative mortality and morbidity after surgical decompression of metastatic extradural spinal tumors. Operative treatment of metastatic spinal tumors involves extensive procedures that are associated with significant complication rates and healthcare costs. Past studies have examined various risk factors for poor clinical outcomes after surgical decompression procedures for spinal tumors, but few studies have specifically investigated the impact of tumor location on perioperative mortality and morbidity. We identified 2238 patients in the American College of Surgeons National Surgical Quality Improvement Program database who underwent laminectomy for excision of metastatic extradural tumors in the cervical, thoracic, or lumbosacral spine. Baseline patient characteristics were collected from the database. Univariate and multivariate regression analyses were performed to examine the association between spinal tumor location and 30-day perioperative mortality and morbidity. On univariate analysis, cervical spinal tumors were associated with the highest rate of pulmonary complications. Multivariate regression analysis demonstrated that cervical spinal tumors had the highest odds of multiple perioperative complications. However, thoracic spinal tumors were associated with the highest risk of intra- or postoperative blood transfusion. In contrast, patients with metastatic tumors in the lumbosacral spine had lower odds of perioperative mortality, pulmonary complications, and sepsis. Tumor location is an independent risk factor for perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors. The addition of tumor location to existing prognostic scoring systems may help to improve their predictive accuracy. 3.

Predictive factors for cosmetic surgery: a hospital-based investigation.

PubMed

Li, Jun; Li, Qian; Zhou, Bei; Gao, Yanli; Ma, Jiehua; Li, Jingyun

2016-01-01

Cosmetic surgery is becoming increasingly popular in China. However, reports on the predictive factors for cosmetic surgery in Chinese individuals are scarce in the literature. We retrospectively analyzed 4550 cosmetic surgeries performed from January 2010 to December 2014 at a single center in China. Data collection included patient demographics and type of cosmetic surgery. Predictive factors were age, sex, marital status, occupational status, educational degree, and having had children. Predictive factors for the three major cosmetic surgeries were determined using a logistic regression analysis. Patients aged 19-34 years accounted for the most popular surgical procedures (76.9 %). The most commonly requested procedures were eye surgery, Botox injection, and nevus removal. Logistic regression analysis showed that higher education level (college, P = 0.01, OR 1.21) was predictive for eye surgery. Age (19-34 years, P = 0.00, OR 33.39; 35-50, P = 0.00, OR 31.34; ≥51, P = 0.00, OR 16.42), female sex (P = 0.00, OR 9.19), employment (service occupations, P = 0.00, OR 2.31; non-service occupations, P = 0.00, OR 1.76), and higher education level (college, P = 0.00, OR 1.39) were independent predictive factors for Botox injection. Married status (P = 0.00, OR 1.57), employment (non-service occupations, P = 0.00, OR 1.50), higher education level (masters, P = 0.00, OR 6.61), and having children (P = 0.00, OR 1.45) were independent predictive factors for nevus removal. The principal three cosmetic surgeries (eye surgery, Botox injection, and nevus removal) were associated with multiple variables. Patients employed in non-service occupations were more inclined to undergo Botox injection and nevus removal. Cohort study, Level III.

Learning curve analysis of mitral valve repair using telemanipulative technology.

PubMed

Charland, Patrick J; Robbins, Tom; Rodriguez, Evilio; Nifong, Wiley L; Chitwood, Randolph W

2011-08-01

To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. We found a statistically significant learning curve (P < .01). The institutional learning percentage∗ derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R(2) = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis. Copyright © 2011 The American Association for Thoracic Surgery. All rights reserved.

Associations of Organizational Justice with Tinnitus and the Mediating Role of Depressive Symptoms and Burnout-Findings from a Cross-Sectional Study.

PubMed

Herr, Raphael M; Loerbroks, Adrian; Bosch, Jos A; Seegel, Max; Schneider, Michael; Schmidt, Burkhard

2016-04-01

Tinnitus refers to the perception of a sound while an external source is absent. Research has identified work-related stress and its potential mental health-related sequelaes, i.e., depression and burnout, as risk factors for tinnitus. Perceived unfairness at work (organizational injustice), which is considered a psychosocial occupational stressor, has been shown to predict depression and burnout but its potential associations with tinnitus remains unaddressed. The aim was to determine the relationship of organizational injustice with tinnitus, and to examine depression and burnout as potential mediators. Cross-sectional data from a sample of 1632 employees were used. Tinnitus was assessed by self-report (n = 207; 13.9 %). Organizational justice and its subcomponents (interactional and procedural justice), burnout, and depressive symptoms were measured by validated questionnaires. Associations were assessed by logistic regressions, and mediation was assessed by maximum likelihood logistic regression estimations. Overall organizational justice, interactional and procedural justice were inversely related to tinnitus (z-score for overall justice: OR = 0.754; 95 % CI = 0.649 to 0.876). These associations were independent of demographics, socioeconomic status, job characteristics (including potential noise exposure), and health behaviors. Mediation analyses suggested a potential mediation by burnout (95 % CI indirect effect -0.188 to -0.066) and depressive symptoms (95 % CI indirect effect -0.160 to -0.043). Parallel multiple mediation analysis revealed that mediation through burnout was significantly larger than through depressive symptoms. Organizational justice appeared inversely related to tinnitus and this association was explained by individual differences in burnout symptoms, suggesting mediation. Longitudinal studies may further help to strengthen the evidence base for prevention of tinnitus through promotion of organizational justice and prevention of burnout.

A SYSTEMATIC PROCEDURE FOR DESIGNING PROCESSES WITH MULTIPLE ENVIRONMENTAL OBJECTIVES

EPA Science Inventory

Evaluation and analysis of multiple objectives are very important in designing environmentally benign processes. They require a systematic procedure for solving multi-objective decision-making problems due to the complex nature of the problems and the need for complex assessment....

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…