Survival analysis and Cox regression.
Benítez-Parejo, N; Rodríguez del Águila, M M; Pérez-Vicente, S
2011-01-01
The data provided by clinical trials are often expressed in terms of survival. The analysis of survival comprises a series of statistical analytical techniques in which the measurements analysed represent the time elapsed between a given exposure and the outcome of a certain event. Despite the name of these techniques, the outcome in question does not necessarily have to be either survival or death, and may be healing versus no healing, relief versus pain, complication versus no complication, relapse versus no relapse, etc. The present article describes the analysis of survival from both a descriptive perspective, based on the Kaplan-Meier estimation method, and in terms of bivariate comparisons using the log-rank statistic. Likewise, a description is provided of the Cox regression models for the study of risk factors or covariables associated to the probability of survival. These models are defined in both simple and multiple forms, and a description is provided of how they are calculated and how the postulates for application are checked - accompanied by illustrating examples with the shareware application R.
Survival analysis of cervical cancer using stratified Cox regression
NASA Astrophysics Data System (ADS)
Purnami, S. W.; Inayati, K. D.; Sari, N. W. Wulan; Chosuvivatwong, V.; Sriplung, H.
2016-04-01
Cervical cancer is one of the mostly widely cancer cause of the women death in the world including Indonesia. Most cervical cancer patients come to the hospital already in an advanced stadium. As a result, the treatment of cervical cancer becomes more difficult and even can increase the death's risk. One of parameter that can be used to assess successfully of treatment is the probability of survival. This study raises the issue of cervical cancer survival patients at Dr. Soetomo Hospital using stratified Cox regression based on six factors such as age, stadium, treatment initiation, companion disease, complication, and anemia. Stratified Cox model is used because there is one independent variable that does not satisfy the proportional hazards assumption that is stadium. The results of the stratified Cox model show that the complication variable is significant factor which influent survival probability of cervical cancer patient. The obtained hazard ratio is 7.35. It means that cervical cancer patient who has complication is at risk of dying 7.35 times greater than patient who did not has complication. While the adjusted survival curves showed that stadium IV had the lowest probability of survival.
van Houwelingen, Hans C; Putter, Hein
2015-04-01
By far the most popular model to obtain survival predictions for individual patients is the Cox model. The Cox model does not make any assumptions on the underlying hazard, but it relies heavily on the proportional hazards assumption. The most common ways to circumvent this robustness problem are 1) to categorize patients based on their prognostic risk score and to base predictions on Kaplan-Meier curves for the risk categories, or 2) to include interactions with the covariates and suitable functions of time. Robust estimators of the t(0)-year survival probabilities can also be obtained from a "stopped Cox" regression model, in which all observations are administratively censored at t(0). Other recent approaches to solve this robustness problem, originally proposed in the context of competing risks, are pseudo-values and direct binomial regression, based on unbiased estimating equations. In this paper stopped Cox regression is compared with these direct approaches. This is done by means of a simulation study to assess the biases of the different approaches and an analysis of breast cancer data to get some feeling for the performance in practice. The tentative conclusion is that stopped Cox and direct models agree well if the follow-up is not too long. There are larger differences for long-term follow-up data. There stopped Cox might be more efficient, but less robust.
Objective Bayesian model selection for Cox regression.
Held, Leonhard; Gravestock, Isaac; Sabanés Bové, Daniel
2016-12-20
There is now a large literature on objective Bayesian model selection in the linear model based on the g-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors. In this paper, we show that test-based Bayes factors can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum a posteriori and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis patients and the development of a clinical prediction model for future cardiovascular events based on data from the Second Manifestations of ARTerial disease (SMART) cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally, a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. Copyright © 2016 John Wiley & Sons, Ltd.
Simultaneous confidence bands for Cox regression from semiparametric random censorship.
Mondal, Shoubhik; Subramanian, Sundarraman
2016-01-01
Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined.
Yoshihama, Mieko; Horrocks, Julie
2003-08-01
This study uses Cox regression with time-varying covariates to examine the relationship between intimate partner violence and posttraumatic stress disorder (PTSD) in a random sample of Japanese American women and immigrant women from Japan (N = 211). Because applications of survival analysis in trauma research are scarce, this paper presents the utility of this analytical approach by contrasting it with other common methods of analysis (chi-square tests and Cox regression with covariates that do not change over time).
ERIC Educational Resources Information Center
Chen, Chau-Kuang
2005-01-01
Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…
[Application of spline-based Cox regression on analyzing data from follow-up studies].
Dong, Ying; Yu, Jin-ming; Hu, Da-yi
2012-09-01
With R, this study involved the application of the spline-based Cox regression to analyze data related to follow-up studies when the two basic assumptions of Cox proportional hazards regression were not satisfactory. Results showed that most of the continuous covariates contributed nonlinearly to mortality risk while the effects of three covariates were time-dependent. After considering multiple covariates in spline-based Cox regression, when the ankle brachial index (ABI) decreased by 0.1, the hazard ratio (HR) for all-cause death was 1.071. The spline-based Cox regression method could be applied to analyze the data related to follow-up studies when the assumptions of Cox proportional hazards regression were violated.
Modern Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Bloom, Howard S.
2012-01-01
This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of…
Zhu, Lucheng; Luo, Wenhua; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin
2013-09-01
The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.
Dynamics of HPV vaccination initiation in Flanders (Belgium) 2007-2009: a Cox regression model
2011-01-01
Background We investigated dynamic patterns and predictors of HPV vaccination initiation in Flanders (Belgium) by girls aged 12 to 18, between 2007 and 2009, the period immediately after the introduction of the HPV vaccines on the Belgian market. During this period the initiative for vaccination was taken by the girl, her family or the general practitioner/pediatrician/gynecologist. Methods We used a Cox regression model with time constant and time varying predictors to model hazard rates of HPV vaccination initiation. The sample existed of 117,151 female members of the National Alliance of Christian Mutualities, the largest sickness fund in Flanders. Results The study showed that the hazard of HPV vaccination initiation was higher (1) for older girls, (2) for girls with a more favorable socio-economic background, (3) under more generous reimbursement regimes (with this effect being more pronounced for girls with weak socioeconomic backgrounds), (4) for girls that were informed personally about the reimbursement rules. Conclusions When the initiative for HPV vaccination lies with the girls, their families or the physicians (no organized setting) the uptake of the vaccines is affected by both individual and organizational factors. PMID:21672202
Multiple linear regression analysis
NASA Technical Reports Server (NTRS)
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Relative risk regression analysis of epidemiologic data.
Prentice, R L
1985-11-01
Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical epidemiologic procedures, such as that due to Mantel and Haenszel. In the context of an epidemiologic cohort study, relative risk regression methods extend conventional survival data methods and binary response (e.g., logistic) regression models by taking explicit account of the time to disease occurrence while allowing arbitrary baseline disease rates, general censorship, and time-varying risk factors. This latter feature is particularly relevant to many environmental risk assessment problems wherein one wishes to relate disease rates at a particular point in time to aspects of a preceding risk factor history. Relative risk regression methods also adapt readily to time-matched case-control studies and to certain less standard designs. The uses of relative risk regression methods are illustrated and the state of development of these procedures is discussed. It is argued that asymptotic partial likelihood estimation techniques are now well developed in the important special case in which the disease rates of interest have interpretations as counting process intensity functions. Estimation of relative risks processes corresponding to disease rates falling outside this class has, however, received limited attention. The general area of relative risk regression model criticism has, as yet, not been thoroughly studied, though a number of statistical groups are studying such features as tests of fit, residuals, diagnostics and graphical procedures. Most such studies have been restricted to exponential form relative risks as have simulation studies of relative risk estimation
NASA Technical Reports Server (NTRS)
Kattan, Michael W.; Hess, Kenneth R.; Kattan, Michael W.
1998-01-01
New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.
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…
van Dijk, M R; Steyerberg, E W; Stenning, S P; Dusseldorp, E; Habbema, J D F
2004-03-22
The International Germ Cell Consensus (IGCC) classification identifies good, intermediate and poor prognosis groups among patients with metastatic nonseminomatous germ cell tumours (NSGCT). It uses the risk factors primary site, presence of nonpulmonary visceral metastases and tumour markers alpha-fetoprotein (AFP), human chorionic gonadotrophin (HCG) and lactic dehydrogenase (LDH). The IGCC classification is easy to use and remember, but lacks flexibility. We aimed to examine the extent of any loss in discrimination within the IGCC classification in comparison with alternative modelling by formal weighing of the risk factors. We analysed survival of 3048 NSGCT patients with Cox regression and recursive partitioning for alternative classifications. Good, intermediate and poor prognosis groups were based on predicted 5-year survival. Classifications were further refined by subgrouping within the poor prognosis group. Performance was measured primarily by a bootstrap corrected c-statistic to indicate discriminative ability for future patients. The weights of the risk factors in the alternative classifications differed slightly from the implicit weights in the IGCC classification. Discriminative ability, however, did not increase clearly (IGCC classification, c=0.732; Cox classification, c=0.730; Recursive partitioning classification, c=0.709). Three subgroups could be identified within the poor prognosis groups, resulting in classifications with five prognostic groups and slightly better discriminative ability (c=0.740). In conclusion, the IGCC classification in three prognostic groups is largely supported by Cox regression and recursive partitioning. Cox regression was the most promising tool to define a more refined classification. British Journal of Cancer (2004) 90, 1176-1183. doi:10.1038/sj.bjc.6601665 www.bjcancer.com Published online 24 February 2004
Maximum Likelihood Inference for the Cox Regression Model with Applications to Missing Covariates.
Chen, Ming-Hui; Ibrahim, Joseph G; Shao, Qi-Man
2009-10-01
In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model (Cox, 1972, 1975) both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology.
Covariate analysis of survival data: a small-sample study of Cox's model
Johnson, M.E.; Tolley, H.D.; Bryson, M.C.; Goldman, A.S.
1982-09-01
Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed.
Zemmour, Christophe; Bertucci, François; Finetti, Pascal; Chetrit, Bernard; Birnbaum, Daniel; Filleron, Thomas; Boher, Jean-Marie
2015-01-01
BACKGROUND DNA microarray studies identified gene expression signatures predictive of metastatic relapse in early breast cancer. Standard feature selection procedures applied to reduce the set of predictive genes did not take into account the correlation between genes. In this paper, we studied the performances of three high-dimensional regression methods – CoxBoost, LASSO (Least Absolute Shrinkage and Selection Operator), and Elastic net – to identify prognostic signatures in patients with early breast cancer. METHODS We analyzed three public retrospective datasets, including a total of 384 patients with axillary lymph node-negative breast cancer. The Amsterdam van’t Veer’s training set of 78 patients was used to determine the optimal gene sets and classifiers using sensitivity thresholds resulting in mis-classification of no more than 10% of the poor-prognosis group. To ensure the comparability between different methods, an automatic selection procedure was used to determine the number of genes included in each model. The van de Vijver’s and Desmedt’s datasets were used as validation sets to evaluate separately the prognostic performances of our classifiers. The results were compared to the original Amsterdam 70-gene classifier. RESULTS The automatic selection procedure reduced the number of predictive genes up to a minimum of six genes. In the two validation sets, the three models (Elastic net, LASSO, and CoxBoost) led to the definition of genomic classifiers predicting the 5-year metastatic status with similar performances, with respective 59, 56, and 54% accuracy, 83, 75, and 83% sensitivity, and 53, 52, and 48% specificity in the Desmedt’s dataset. In comparison, the Amsterdam 70-gene signature showed 45% accuracy, 97% sensitivity, and 34% specificity. The gene overlap and the classification concordance between the three classifiers were high. All the classifiers added significant prognostic information to that provided by the traditional
Accounting for covariate measurement error in a Cox model analysis of recurrence of depression.
Liu, K; Mazumdar, S; Stone, R A; Dew, M A; Houck, P R; Reynolds, C F
2001-01-01
When a covariate measured with error is used as a predictor in a survival analysis using the Cox model, the parameter estimate is usually biased. In clinical research, covariates measured without error such as treatment procedure or sex are often used in conjunction with a covariate measured with error. In a randomized clinical trial of two types of treatments, we account for the measurement error in the covariate, log-transformed total rapid eye movement (REM) activity counts, in a Cox model analysis of the time to recurrence of major depression in an elderly population. Regression calibration and two variants of a likelihood-based approach are used to account for measurement error. The likelihood-based approach is extended to account for the correlation between replicate measures of the covariate. Using the replicate data decreases the standard error of the parameter estimate for log(total REM) counts while maintaining the bias reduction of the estimate. We conclude that covariate measurement error and the correlation between replicates can affect results in a Cox model analysis and should be accounted for. In the depression data, these methods render comparable results that have less bias than the results when measurement error is ignored.
Regression Analysis by Example. 5th Edition
ERIC Educational Resources Information Center
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Does Cox analysis of a randomized survival study yield a causal treatment effect?
Aalen, Odd O; Cook, Richard J; Røysland, Kjetil
2015-10-01
Statistical methods for survival analysis play a central role in the assessment of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and many other fields. The most common approach to analysis involves fitting a Cox regression model including a treatment indicator, and basing inference on the large sample properties of the regression coefficient estimator. Despite the fact that treatment assignment is randomized, the hazard ratio is not a quantity which admits a causal interpretation in the case of unmodelled heterogeneity. This problem arises because the risk sets beyond the first event time are comprised of the subset of individuals who have not previously failed. The balance in the distribution of potential confounders between treatment arms is lost by this implicit conditioning, whether or not censoring is present. Thus while the Cox model may be used as a basis for valid tests of the null hypotheses of no treatment effect if robust variance estimates are used, modeling frameworks more compatible with causal reasoning may be preferrable in general for estimation.
Regression analysis of cytopathological data
Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.
1982-12-01
Epithelial cells from the human body are frequently labelled according to one of several ordered levels of abnormality, ranging from normal to malignant. The label of the most abnormal cell in a specimen determines the score for the specimen. This paper presents a model for the regression of specimen scores against continuous and discrete variables, as in host exposure to carcinogens. Application to data and tests for adequacy of model fit are illustrated using sputum specimens obtained from a cohort of former asbestos workers.
Lee, Paul H.
2016-01-01
Healthy adults are advised to perform at least 150 min of moderate-intensity physical activity weekly, but this advice is based on studies using self-reports of questionable validity. This study examined the dose-response relationship of accelerometer-measured physical activity and sedentary behaviors on all-cause mortality using segmented Cox regression to empirically determine the break-points of the dose-response relationship. Data from 7006 adult participants aged 18 or above in the National Health and Nutrition Examination Survey waves 2003–2004 and 2005–2006 were included in the analysis and linked with death certificate data using a probabilistic matching approach in the National Death Index through December 31, 2011. Physical activity and sedentary behavior were measured using ActiGraph model 7164 accelerometer over the right hip for 7 consecutive days. Each minute with accelerometer count <100; 1952–5724; and ≥5725 were classified as sedentary, moderate-intensity physical activity, and vigorous-intensity physical activity, respectively. Segmented Cox regression was used to estimate the hazard ratio (HR) of time spent in sedentary behaviors, moderate-intensity physical activity, and vigorous-intensity physical activity and all-cause mortality, adjusted for demographic characteristics, health behaviors, and health conditions. Data were analyzed in 2016. During 47,119 person-year of follow-up, 608 deaths occurred. Each additional hour per day of sedentary behaviors was associated with a HR of 1.15 (95% CI 1.01, 1.31) among participants who spend at least 10.9 h per day on sedentary behaviors, and each additional minute per day spent on moderate-intensity physical activity was associated with a HR of 0.94 (95% CI 0.91, 0.96) among participants with daily moderate-intensity physical activity ≤14.1 min. Associations of moderate physical activity and sedentary behaviors on all-cause mortality were independent of each other. To conclude, evidence from
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…
Regression Analysis and the Sociological Imagination
ERIC Educational Resources Information Center
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Use of Multiple Correlation Analysis and Multiple Regression Analysis.
ERIC Educational Resources Information Center
Huberty, Carl J.; Petoskey, Martha D.
1999-01-01
Distinguishes between multiple correlation and multiple regression analysis. Illustrates suggested information reporting methods and reviews the use of regression methods when dealing with problems of missing data. (SK)
Box-Cox Mixed Logit Model for Travel Behavior Analysis
NASA Astrophysics Data System (ADS)
Orro, Alfonso; Novales, Margarita; Benitez, Francisco G.
2010-09-01
To represent the behavior of travelers when they are deciding how they are going to get to their destination, discrete choice models, based on the random utility theory, have become one of the most widely used tools. The field in which these models were developed was halfway between econometrics and transport engineering, although the latter now constitutes one of their principal areas of application. In the transport field, they have mainly been applied to mode choice, but also to the selection of destination, route, and other important decisions such as the vehicle ownership. In usual practice, the most frequently employed discrete choice models implement a fixed coefficient utility function that is linear in the parameters. The principal aim of this paper is to present the viability of specifying utility functions with random coefficients that are nonlinear in the parameters, in applications of discrete choice models to transport. Nonlinear specifications in the parameters were present in discrete choice theory at its outset, although they have seldom been used in practice until recently. The specification of random coefficients, however, began with the probit and the hedonic models in the 1970s, and, after a period of apparent little practical interest, has burgeoned into a field of intense activity in recent years with the new generation of mixed logit models. In this communication, we present a Box-Cox mixed logit model, original of the authors. It includes the estimation of the Box-Cox exponents in addition to the parameters of the random coefficients distribution. Probability of choose an alternative is an integral that will be calculated by simulation. The estimation of the model is carried out by maximizing the simulated log-likelihood of a sample of observed individual choices between alternatives. The differences between the predictions yielded by models that are inconsistent with real behavior have been studied with simulation experiments.
Liu, Ke; Chen, Kewei; Yao, Li; Guo, Xiaojuan
2017-01-01
Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction
Docking studies on NSAID/COX-2 isozyme complexes using Contact Statistics analysis
NASA Astrophysics Data System (ADS)
Ermondi, Giuseppe; Caron, Giulia; Lawrence, Raelene; Longo, Dario
2004-11-01
The selective inhibition of COX-2 isozymes should lead to a new generation of NSAIDs with significantly reduced side effects; e.g. celecoxib (Celebrex®) and rofecoxib (Vioxx®). To obtain inhibitors with higher selectivity it has become essential to gain additional insight into the details of the interactions between COX isozymes and NSAIDs. Although X-ray structures of COX-2 complexed with a small number of ligands are available, experimental data are missing for two well-known selective COX-2 inhibitors (rofecoxib and nimesulide) and docking results reported are controversial. We use a combination of a traditional docking procedure with a new computational tool (Contact Statistics analysis) that identifies the best orientation among a number of solutions to shed some light on this topic.
Association between COX-2 -1195G>A polymorphism and gastrointestinal cancer risk: A meta-analysis
Zhang, Xiao-Wei; Li, Jun; Jiang, Yu-Xing; Chen, Yu-Xiang
2017-01-01
AIM To perform a meta-analysis to investigate the association between cyclooxygenase-2 (COX-2) -1195G>A gene polymorphism and gastrointestinal cancers. METHODS Publications related to the COX-2 -1195G>A gene polymorphism and gastrointestinal cancers published before July 2016 were retrieved from PubMed, EMBASE, Web of Science, China Biological Medicine Database, China National Knowledge Infrastructure, and CQVIP Database. Meta-analysis was performed using Stata11.0 software. The strength of the association was evaluated by calculating the combined odds ratios (ORs) and the corresponding 95%CIs. The retrieved publications were excluded or included one by one for sensitivity analysis. In addition, the funnel plot, Begg’s rank correlation test, and Egger’s linear regression method were applied to analyse whether the included publications had publication bias. RESULTS A total of 24 publications related to the COX-2 -1195G>A gene polymorphism were included, including 28 studies involving 11043 cases and 18008 controls. The meta-analysis results showed that the COX-2 -1195G>A gene polymorphism significantly correlated with an increased risk of gastrointestinal cancers, particularly gastric cancer (A vs G: OR = 1.35; AA/AG vs GG: OR = 1.54; AA vs GG/AG: OR = 1.43; AA vs GG: OR = 1.80; AG vs GG: OR = 1.35). Compared to the Caucasian population in America and Europe, the COX-2 -1195G>A gene polymorphism in the Asian population (A vs G: OR = 1.30; AA/AG vs GG: OR = 1.50; AA vs GG/AG: OR = 1.35; AA vs GG: OR = 1.71; AG vs GG: OR = 1.37) significantly increased gastrointestinal cancer risk. The sensitivity analysis (P < 0.05) and the false positive report probability (P < 0.2) confirmed the reliability of the results. CONCLUSION The results showed that the COX-2 -1195G>A gene polymorphism might be a potential risk factor for gastrointestinal cancers. Further validation by a large homogeneous study is warranted.
Commonality Analysis for the Regression Case.
ERIC Educational Resources Information Center
Murthy, Kavita
Commonality analysis is a procedure for decomposing the coefficient of determination (R superscript 2) in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors in various…
Beretta, C; Garavaglia, G; Cavalli, M
2005-10-01
We report on the inhibitory activity of the NSAIDs meloxicam, carprofen, phenylbutazone and flunixin, on blood cyclooxygenases in the horse using in vitro enzyme-linked assays. As expected, comparison of IC50 indicated that meloxicam and carprofen are more selective inhibitors of COX-2 than phenylbutazone and flunixin; meloxicam was the most advantageous for horses of four NSAIDs examined. However at IC80, phenylbutazone (+134.4%) and flunixin (+29.7%) had greater COX-2 selectivity than at IC50, and meloxicam (-41.2%) and carprofen (-12.9%) had lower COX-2 selectivity than at IC50. We therefore propose that the selectivity of NSAIDs should be assessed at the 80% as well as 50% inhibition level.
Xu, Feng; Li, Mengxin; Zhang, Chao; Cui, Jianxiu; Liu, Jun; Li, Jie; Jiang, Hongchuan
2017-01-01
The prognostic significance of COX-2 in patients with breast cancer remains controversial. The aims of our meta-analysis are to evaluate its association with clinicopathological characteristics and prognostic value in patients with breast cancer. PubMed, EMBASE, Web of Science, the Ovid Database and Grey literature were systematically searched up to May 2016. Twenty-one studies including 6739 patients with breast cancer were analyzed. The meta-analysis indicated that the incidence difference of COX-2 expression was significant when comparing the lymph node positive group to negative group (OR = 1.76, 95% CI [1.30, 2.39]) and the tumor size ≥ 2cm group to the tumor size < 2cm group (OR = 1.71, 95% CI [1.22, 2.39]). None of other clinicopathological parameters such as the ER status, PR status, HER2 status and the vascular invasion status were associated with COX-2 overexpression. The detection of COX-2 was significantly correlated with the disease-free survival (DFS) of patients (HR = 1.58, 95% CI [1.23, 2.03]) and the overall survival (OS) of patients (HR = 1.51, 95% CI [1.31, 1.72]). Our meta-analysis demonstrates that the presence of high levels of COX-2 is associated with poor prognosis for breast cancer patients and predicts bigger tumor size and lymph node metastasis. PMID:27999206
Analysis of the cytochrome c oxidase subunit II (COX2) gene in giant panda, Ailuropoda melanoleuca.
Ling, S S; Zhu, Y; Lan, D; Li, D S; Pang, H Z; Wang, Y; Li, D Y; Wei, R P; Zhang, H M; Wang, C D; Hu, Y D
2017-01-23
The giant panda, Ailuropoda melanoleuca (Ursidae), has a unique bamboo-based diet; however, this low-energy intake has been sufficient to maintain the metabolic processes of this species since the fourth ice age. As mitochondria are the main sites for energy metabolism in animals, the protein-coding genes involved in mitochondrial respiratory chains, particularly cytochrome c oxidase subunit II (COX2), which is the rate-limiting enzyme in electron transfer, could play an important role in giant panda metabolism. Therefore, the present study aimed to isolate, sequence, and analyze the COX2 DNA from individuals kept at the Giant Panda Protection and Research Center, China, and compare these sequences with those of the other Ursidae family members. Multiple sequence alignment showed that the COX2 gene had three point mutations that defined three haplotypes, with 60% of the sequences corresponding to haplotype I. The neutrality tests revealed that the COX2 gene was conserved throughout evolution, and the maximum likelihood phylogenetic analysis, using homologous sequences from other Ursidae species, showed clustering of the COX2 sequences of giant pandas, suggesting that this gene evolved differently in them.
Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin
2013-10-15
In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology.
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.
Canonical Analysis as a Generalized Regression Technique for Multivariate Analysis.
ERIC Educational Resources Information Center
Williams, John D.
The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…
Park, Sang Hyun; Eom, Keeseon S.; Park, Min Sun; Kwon, Oh Kyoung; Kim, Hyo Sun
2013-01-01
Diphyllobothrium nihonkaiense has been reported in Korea as Diphyllobothrium latum because of their close morphologic resemblance. We have identified a human case of D. nihonkaiense infection using the mitochondrial cytochrome c oxidase subunit I (cox1) gene sequence analysis. On 18 February 2012, a patient who had consumed raw fish a month earlier visited our outpatient clinic with a long tapeworm parasite excreted in the feces. The body of the segmented worm was 2 m long and divided into the scolex (head) and proglottids. It was morphologically close to D. nihonkaiense and D. latum. The cox1 gene analysis showed 99.4% (340/342 bp) homology with D. nihonkaiense but only 91.8% (314/342 bp) homology with D. latum. The present study suggested that the Diphyllobothrium spp. infection in Korea should be analyzed with specific DNA sequence for an accurate species identification. PMID:24039292
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.
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
ERIC Educational Resources Information Center
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Giganti, Mark J.; Luz, Paula M.; Caro-Vega, Yanink; Cesar, Carina; Padgett, Denis; Koenig, Serena; Echevarria, Juan; McGowan, Catherine C.; Shepherd, Bryan E.
2015-01-01
Abstract Many studies of HIV/AIDS aggregate data from multiple cohorts to improve power and generalizability. There are several analysis approaches to account for cross-cohort heterogeneity; we assessed how different approaches can impact results from an HIV/AIDS study investigating predictors of mortality. Using data from 13,658 HIV-infected patients starting antiretroviral therapy from seven Latin American and Caribbean cohorts, we illustrate the assumptions of seven readily implementable approaches to account for across cohort heterogeneity with Cox proportional hazards models, and we compare hazard ratio estimates across approaches. As a sensitivity analysis, we modify cohort membership to generate specific heterogeneity conditions. Hazard ratio estimates varied slightly between the seven analysis approaches, but differences were not clinically meaningful. Adjusted hazard ratio estimates for the association between AIDS at treatment initiation and death varied from 2.00 to 2.20 across approaches that accounted for heterogeneity; the adjusted hazard ratio was estimated as 1.73 in analyses that ignored across cohort heterogeneity. In sensitivity analyses with more extreme heterogeneity, we noted a slightly greater distinction between approaches. Despite substantial heterogeneity between cohorts, the impact of the specific approach to account for heterogeneity was minimal in our case study. Our results suggest that it is important to account for across cohort heterogeneity in analyses, but that the specific technique for addressing heterogeneity may be less important. Because of their flexibility in accounting for cohort heterogeneity, we prefer stratification or meta-analysis methods, but we encourage investigators to consider their specific study conditions and objectives. PMID:25647087
Choi, In-Wook; Kim, Hwang-Yong; Quan, Juan-Hua; Ryu, Jae-Gee; Sun, Rubing; Lee, Young-Ha
2015-10-01
Fascioliasis, a food-borne trematode zoonosis, is a disease primarily in cattle and sheep and occasionally in humans. Water dropwort (Oenanthe javanica), an aquatic perennial herb, is a common second intermediate host of Fasciola, and the fresh stems and leaves are widely used as a seasoning in the Korean diet. However, no information regarding Fasciola species contamination in water dropwort is available. Here, we collected 500 samples of water dropwort in 3 areas in Korea during February and March 2015, and the water dropwort contamination of Fasciola species was monitored by DNA sequencing analysis of the Fasciola hepatica and Fasciola gigantica specific mitochondrial cytochrome c oxidase subunit 1 (cox1) and nuclear ribosomal internal transcribed spacer 2 (ITS-2). Among the 500 samples assessed, the presence of F. hepatica cox1 and 1TS-2 markers were detected in 2 samples, and F. hepatica contamination was confirmed by sequencing analysis. The nucleotide sequences of cox1 PCR products from the 2 F. hepatica-contaminated samples were 96.5% identical to the F. hepatica cox1 sequences in GenBank, whereas F. gigantica cox1 sequences were 46.8% similar with the sequence detected from the cox1 positive samples. However, F. gigantica cox1 and ITS-2 markers were not detected by PCR in the 500 samples of water dropwort. Collectively, in this survey of the water dropwort contamination with Fasciola species, very low prevalence of F. hepatica contamination was detected in the samples.
Crager, Michael R; Tang, Gong
We propose a method for assessing an individual patient's risk of a future clinical event using clinical trial or cohort data and Cox proportional hazards regression, combining the information from several studies using meta-analysis techniques. The method combines patient-specific estimates of the log cumulative hazard across studies, weighting by the relative precision of the estimates, using either fixed- or random-effects meta-analysis calculations. Risk assessment can be done for any future patient using a few key summary statistics determined once and for all from each study. Generalizations of the method to logistic regression and linear models are immediate. We evaluate the methods using simulation studies and illustrate their application using real data.
Strategies for Detecting Outliers in Regression Analysis: An Introductory Primer.
ERIC Educational Resources Information Center
Evans, Victoria P.
Outliers are extreme data points that have the potential to influence statistical analyses. Outlier identification is important to researchers using regression analysis because outliers can influence the model used to such an extent that they seriously distort the conclusions drawn from the data. The effects of outliers on regression analysis are…
Park, Sang Hyun; Jeon, Hyeong Kyu; Kim, Jin Bong
2015-01-01
Most of the diphyllobothriid tapeworms isolated from human samples in the Republic of Korea (= Korea) have been identified as Diphyllobothrium nihonkaiense by genetic analysis. This paper reports confirmation of D. nihonkaiense infections in 4 additional human samples obtained between 1995 and 2014, which were analyzed at the Department of Parasitology, Hallym University College of Medicine, Korea. Analysis of the mitochondrial cytochrome c oxidase 1 (cox1) gene revealed a 98.5-99.5% similarity with a reference D. nihonkaiense sequence in GenBank. The present report adds 4 cases of D. nihonkaiense infections to the literature, indicating that the dominant diphyllobothriid tapeworm species in Korea is D. nihonkaiense but not D. latum. PMID:25748716
ERIC Educational Resources Information Center
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan.
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…
The Precision Efficacy Analysis for Regression Sample Size Method.
ERIC Educational Resources Information Center
Brooks, Gordon P.; Barcikowski, Robert S.
The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…
Laubender, Ruediger P; Bender, Ralf
2014-02-28
Recently, Laubender and Bender (Stat. Med. 2010; 29: 851-859) applied the average risk difference (RD) approach to estimate adjusted RD and corresponding number needed to treat measures in the Cox proportional hazards model. We calculated standard errors and confidence intervals by using bootstrap techniques. In this paper, we develop asymptotic variance estimates of the adjusted RD measures and corresponding asymptotic confidence intervals within the counting process theory and evaluated them in a simulation study. We illustrate the use of the asymptotic confidence intervals by means of data of the Düsseldorf Obesity Mortality Study.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A
2016-01-01
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
3D Regression Heat Map Analysis of Population Study Data.
Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard
2016-01-01
Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.
NASA Astrophysics Data System (ADS)
Ahn, Kuk-Hyun; Palmer, Richard
2016-09-01
Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes 'spatial proximity' based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. 'Spatial proximity' based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.
Linear regression analysis of survival data with missing censoring indicators
Wang, Qihua
2010-01-01
Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial. PMID:20559722
Joint regression analysis of correlated data using Gaussian copulas.
Song, Peter X-K; Li, Mingyao; Yuan, Ying
2009-03-01
This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correlated outcomes. This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients. The availability of joint probability models enables us to develop a full maximum likelihood inference. Numerical illustrations are focused on regression models for discrete correlated data, including multidimensional logistic regression models and a joint model for mixed normal and binary outcomes. In the simulation studies, the proposed copula-based joint model is compared to the popular generalized estimating equations, which is a moment-based estimating equation method to join univariate GLMs. Two real-world data examples are used in the illustration.
Regression Model Optimization for the Analysis of Experimental Data
NASA Technical Reports Server (NTRS)
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.
Combined survival analysis of cardiac patients by a Cox PH model and a Markov chain.
Shauly, Michal; Rabinowitz, Gad; Gilutz, Harel; Parmet, Yisrael
2011-10-01
The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.
Time series analysis using semiparametric regression on oil palm production
NASA Astrophysics Data System (ADS)
Yundari, Pasaribu, U. S.; Mukhaiyar, U.
2016-04-01
This paper presents semiparametric kernel regression method which has shown its flexibility and easiness in mathematical calculation, especially in estimating density and regression function. Kernel function is continuous and it produces a smooth estimation. The classical kernel density estimator is constructed by completely nonparametric analysis and it is well reasonable working for all form of function. Here, we discuss about parameter estimation in time series analysis. First, we consider the parameters are exist, then we use nonparametrical estimation which is called semiparametrical. The selection of optimum bandwidth is obtained by considering the approximation of Mean Integrated Square Root Error (MISE).
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Sparse Regression by Projection and Sparse Discriminant Analysis.
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J; Zhao, Hongyu
2015-04-01
Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared to the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplemental materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
Regression analysis for solving diagnosis problem of children's health
NASA Astrophysics Data System (ADS)
Cherkashina, Yu A.; Gerget, O. M.
2016-04-01
The paper includes results of scientific researches. These researches are devoted to the application of statistical techniques, namely, regression analysis, to assess the health status of children in the neonatal period based on medical data (hemostatic parameters, parameters of blood tests, the gestational age, vascular-endothelial growth factor) measured at 3-5 days of children's life. In this paper a detailed description of the studied medical data is given. A binary logistic regression procedure is discussed in the paper. Basic results of the research are presented. A classification table of predicted values and factual observed values is shown, the overall percentage of correct recognition is determined. Regression equation coefficients are calculated, the general regression equation is written based on them. Based on the results of logistic regression, ROC analysis was performed, sensitivity and specificity of the model are calculated and ROC curves are constructed. These mathematical techniques allow carrying out diagnostics of health of children providing a high quality of recognition. The results make a significant contribution to the development of evidence-based medicine and have a high practical importance in the professional activity of the author.
An adequate design for regression analysis of yield trials.
Gusmão, L
1985-12-01
Based on theoretical demonstrations and illustrated with a numerical example from triticale yield trials in Portugal, the Completely Randomized Design is proposed as the one suited for Regression Analysis. When trials are designed in Complete Randomized Blocks the regression of plot production on block mean instead of the regression of cultivar mean on the overall mean of the trial is proposed as the correct procedure for regression analysis. These proposed procedures, in addition to providing a better agreement with the assumptions for regression and the philosophy of the method, induce narrower confidence intervals and attenuation of the hyperbolic effect. The increase in precision is brought about by both a decrease in the t Student values by an increased number of degrees of freedom, and by a decrease in standard error by a non proportional increase of residual variance and non proportional increase of the sum of squares of the assumed independent variable. The new procedures seem to be promising for a better understanding of the mechanism of specific instability.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert; Bader, Jon B.
2009-01-01
Calibration data of a wind tunnel sting balance was processed using a search algorithm that identifies an optimized regression model for the data analysis. The selected sting balance had two moment gages that were mounted forward and aft of the balance moment center. The difference and the sum of the two gage outputs were fitted in the least squares sense using the normal force and the pitching moment at the balance moment center as independent variables. The regression model search algorithm predicted that the difference of the gage outputs should be modeled using the intercept and the normal force. The sum of the two gage outputs, on the other hand, should be modeled using the intercept, the pitching moment, and the square of the pitching moment. Equations of the deflection of a cantilever beam are used to show that the search algorithm s two recommended math models can also be obtained after performing a rigorous theoretical analysis of the deflection of the sting balance under load. The analysis of the sting balance calibration data set is a rare example of a situation when regression models of balance calibration data can directly be derived from first principles of physics and engineering. In addition, it is interesting to see that the search algorithm recommended the same regression models for the data analysis using only a set of statistical quality metrics.
Regression Analysis with Dummy Variables: Use and Interpretation.
ERIC Educational Resources Information Center
Hinkle, Dennis E.; Oliver, J. Dale
1986-01-01
Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)
Regression Analysis: Instructional Resource for Cost/Managerial Accounting
ERIC Educational Resources Information Center
Stout, David E.
2015-01-01
This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…
Quantile Regression with Censored Data
ERIC Educational Resources Information Center
Lin, Guixian
2009-01-01
The Cox proportional hazards model and the accelerated failure time model are frequently used in survival data analysis. They are powerful, yet have limitation due to their model assumptions. Quantile regression offers a semiparametric approach to model data with possible heterogeneity. It is particularly powerful for censored responses, where the…
Tensor Regression with Applications in Neuroimaging Data Analysis
Zhou, Hua; Li, Lexin; Zhu, Hongtu
2013-01-01
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data. PMID:24791032
A framework for longitudinal data analysis via shape regression
NASA Astrophysics Data System (ADS)
Fishbaugh, James; Durrleman, Stanley; Piven, Joseph; Gerig, Guido
2012-02-01
Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.
Kolenda, Rafał; Ugorski, Maciej; Bednarski, Michał
2014-08-01
Sarcocysts from four Polish roe deer were collected and examined by light microscopy, small subunit ribosomal RNA (ssu rRNA), and the subunit I of cytochrome oxidase (cox1) sequence analysis. This resulted in identification of Sarcocystis gracilis, Sarcocystis oviformis, and Sarcocystis silva. However, we were unable to detect Sarcocystis capreolicanis, the fourth Sarcocystis species found previously in Norwegian roe deer. Polish sarcocysts isolated from various tissues differed in terms of their shape and size and were larger than the respective Norwegian isolates. Analysis of ssu rRNA gene revealed the lack of differences between Sarcocystis isolates belonging to one species and a very low degree of genetic diversity between Polish and Norwegian sarcocysts, ranging from 0.1% for Sarcocystis gracilis and Sarcocystis oviformis to 0.44% for Sarcocystis silva. Contrary to the results of the ssu rRNA analysis, small intraspecies differences in cox1 sequences were found among Polish Sarcocystis gracilis and Sarcocystis silva isolates. The comparison of Polish and Norwegian cox1 sequences representing the same Sarcocystis species revealed similar degree of sequence identity, namely 99.72% for Sarcocystis gracilis, 98.76% for Sarcocystis silva, and 99.85% for Sarcocystis oviformis. Phylogenetic reconstruction and genetic population analyses showed an unexpected high degree of identity between Polish and Norwegian isolates. Moreover, cox1 gene sequences turned out to be more accurate than ssu rRNA when used to reveal phylogenetic relationships among closely related species. The results of our study revealed that the same Sarcocystis species isolated from the same hosts living in different geographic regions show a very high level of genetic similarity.
A regression model analysis of longitudinal dental caries data.
Ringelberg, M L; Tonascia, J A
1976-03-01
Longitudinal data on caries experience were derived from the reexamination and interview of a cohort of 306 subjects with an average follow-up period of 33 years after the baseline examination. Analysis of the data was accomplished by the use of contingency tables utilizing enumeration statistics compared with a multiple regression analysis. The analyses indicated a strong association of caries experience at one point in time with the caries experience of that same person earlier in life. The regression model approach offers adjustment of any given independent variable for the effect of all other independent variables, providing a powerful means of bias reduction. The model is also useful in separating out the specific effect of an independent variable over and above the contribution of other variables. The model used explained 35% of the variability in the DMFS scores recorded. Similar models could be useful adjuncts in the analyses of dental epidemiologic data.
Principal regression analysis and the index leverage effect
NASA Astrophysics Data System (ADS)
Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe
2011-09-01
We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.
Robust regression applied to fractal/multifractal analysis.
NASA Astrophysics Data System (ADS)
Portilla, F.; Valencia, J. L.; Tarquis, A. M.; Saa-Requejo, A.
2012-04-01
Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn't be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don't have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: • Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R p-value. In this way we consider the implications of reducing the number of points. • Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology. Acknowledgements Funding provided by CEIGRAM (Research Centre for the Management of Agricultural and Environmental Risks) and by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
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.
Islam, Abul B M M K; Dave, Mandar; Amin, Sonia; Jensen, Roderick V; Amin, Ashok R
2016-04-01
The constitutively-expressed cyclooxygenase 1 (COX-1) and the inducible COX-2 are both involved in the conversion of arachidonic acid (AA) to prostaglandins (PGs). However, the functional roles of COX-1 at the cellular level remain unclear. We hypothesized that by comparing differential gene expression and eicosanoid metabolism in lung fibroblasts from wild-type (WT) mice and COX-2(-/-) or COX-1(-/-) mice may help address the functional roles of COX-1 in inflammation and other cellular functions. Compared to WT, the number of specifically-induced transcripts were altered descendingly as follows: COX-2(-/-)>COX-1(-/-)>WT+IL-1β. COX-1(-/-) or COX-2(-/-) cells shared about 50% of the induced transcripts with WT cells treated with IL-1β, respectively. An interactive "anti-inflammatory, proinflammatory, and redox-activated" signature in the protein-protein interactome map was observed in COX-2(-/-) cells. The augmented COX-1 mRNA (in COX-2(-/-) cells) was associated with the upregulation of mRNAs for glutathione S-transferase (GST), superoxide dismutase (SOD), NAD(P)H dehydrogenase quinone 1 (NQO1), aryl hydrocarbon receptor (AhR), peroxiredoxin, phospholipase, prostacyclin synthase, and prostaglandin E synthase, resulting in a significant increase in the levels of PGE2, PGD2, leukotriene B4 (LTB4), PGF1α, thromboxane B2 (TXB2), and PGF2α. The COX-1 plays a dominant role in shifting AA toward the LTB4 pathway and anti-inflammatory activities. Compared to WT, the upregulated COX-1 mRNA in COX-2(-/-) cells generated an "eicosanoid storm". The genomic characteristics of COX-2(-/-) is similar to that of proinflammatory cells as observed in IL-1β induced WT cells. COX-1(-/-) and COX-2(-/-) cells exhibited compensation of various eicosanoids at the genomic and metabolic levels.
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra
Parra, Edwin Roger; Lin, Flavia; Martins, Vanessa; Rangel, Maristela Peres; Capelozzi, Vera Luiza
2013-01-01
OBJECTIVE: To study the expression of COX-1 and COX-2 in the remodeled lung in systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) patients, correlating that expression with patient survival. METHODS: We examined open lung biopsy specimens from 24 SSc patients and 30 IPF patients, using normal lung tissue as a control. The histological patterns included fibrotic nonspecific interstitial pneumonia (NSIP) in SSc patients and usual interstitial pneumonia (UIP) in IPF patients. We used immunohistochemistry and histomorphometry to evaluate the expression of COX-1 and COX-2 in alveolar septa, vessels, and bronchioles. We then correlated that expression with pulmonary function test results and evaluated its impact on patient survival. RESULTS: The expression of COX-1 and COX-2 in alveolar septa was significantly higher in IPF-UIP and SSc-NSIP lung tissue than in the control tissue. No difference was found between IPF-UIP and SSc-NSIP tissue regarding COX-1 and COX-2 expression. Multivariate analysis based on the Cox regression model showed that the factors associated with a low risk of death were younger age, high DLCO/alveolar volume, IPF, and high COX-1 expression in alveolar septa, whereas those associated with a high risk of death were advanced age, low DLCO/alveolar volume, SSc (with NSIP), and low COX-1 expression in alveolar septa. CONCLUSIONS: Our findings suggest that strategies aimed at preventing low COX-1 synthesis will have a greater impact on SSc, whereas those aimed at preventing high COX-2 synthesis will have a greater impact on IPF. However, prospective randomized clinical trials are needed in order to confirm that. PMID:24473763
Spatial regression analysis on 32 years total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-02-01
Multiple-regressions analysis have been performed on 32 years of total ozone column data that was spatially gridded with a 1° × 1.5° resolution. The total ozone data consists of the MSR (Multi Sensor Reanalysis; 1979-2008) and two years of assimilated SCIAMACHY ozone data (2009-2010). The two-dimensionality in this data-set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on non-seasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Nino (ENSO) and stratospheric alternative halogens (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at high and mid-latitudes, the solar cycle affects ozone positively mostly at the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high Northern latitudes, the effect of QBO is positive and negative at the tropics and mid to high-latitudes respectively and ENSO affects ozone negatively between 30° N and 30° S, particularly at the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid to high
Forecasting urban water demand: A meta-regression analysis.
Sebri, Maamar
2016-12-01
Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike.
Analysis of regression methods for solar activity forecasting
NASA Technical Reports Server (NTRS)
Lundquist, C. A.; Vaughan, W. W.
1979-01-01
The paper deals with the potential use of the most recent solar data to project trends in the next few years. Assuming that a mode of solar influence on weather can be identified, advantageous use of that knowledge presumably depends on estimating future solar activity. A frequently used technique for solar cycle predictions is a linear regression procedure along the lines formulated by McNish and Lincoln (1949). The paper presents a sensitivity analysis of the behavior of such regression methods relative to the following aspects: cycle minimum, time into cycle, composition of historical data base, and unnormalized vs. normalized solar cycle data. Comparative solar cycle forecasts for several past cycles are presented as to these aspects of the input data. Implications for the current cycle, No. 21, are also given.
Tree-augmented Cox proportional hazards models.
Su, Xiaogang; Tsai, Chih-Ling
2005-07-01
We study a hybrid model that combines Cox proportional hazards regression with tree-structured modeling. The main idea is to use step functions, provided by a tree structure, to 'augment' Cox (1972) proportional hazards models. The proposed model not only provides a natural assessment of the adequacy of the Cox proportional hazards model but also improves its model fitting without loss of interpretability. Both simulations and an empirical example are provided to illustrate the use of the proposed method.
Four cases of Taenia saginata infection with an analysis of COX1 gene.
Cho, Jaeeun; Jung, Bong-Kwang; Lim, Hyemi; Kim, Min-Jae; Yooyen, Thanapon; Lee, Dongmin; Eom, Keeseon S; Shin, Eun-Hee; Chai, Jong-Yil
2014-02-01
Human taeniases had been not uncommon in the Republic of Korea (=Korea) until the 1980s. The prevalence decreased and a national survey in 2004 revealed no Taenia egg positive cases. However, a subsequent national survey in 2012 showed 0.04% (10 cases) prevalence of Taenia spp. eggs suggesting its resurgence in Korea. We recently encountered 4 cases of Taenia saginata infection who had symptoms of taeniasis that included discharge of proglottids. We obtained several proglottids from each case. Because the morphological features of T. saginata are almost indistinguishable from those of Taenia asiatica, molecular analyses using the PCR-RFLP and DNA sequencing of the cytochrome c oxidase subunit 1 (cox1) were performed to identify the species. The PCR-RFLP patterns of all of the 4 specimens were consistent with T. saginata, and the cox1 gene sequence showed 99.8-100% identity with that of T. saginata reported previously from Korea, Japan, China, and Cambodia. All of the 4 patients had the history of travel abroad but its relation with contracting taeniasis was unclear. Our findings may suggest resurgence of T. saginata infection among people in Korea.
de Pedro, María; Baeza, Sara; Escudero, María-Teresa; Dierssen-Sotos, Trinidad; Gómez-Acebo, Inés; Pollán, Marina; Llorca, Javier
2015-01-01
Evidence on non-steroidal anti-inflammatory drugs (NSAID) use and breast cancer risk shows a slightly protective effect of these drugs, but previous studies lack randomized clinical trial results and present high heterogeneity in exposure measurement. This systematic review and meta-analysis widens the knowledge about NSAID use and breast cancer risk, updating the information from the last meta-analysis, focusing on evidence on specific effects of COX-2 inhibitors and differential expression patterns of hormonal receptors. A PubMed-database search was conducted to include all entries published with the keywords "BREAST CANCER NSAID ANTI-INFLAMMATORY" until 10/24/2013 providing original results from cohort studies, case-control studies, or randomized clinical trials with at least one reported relative risk (RR) or odds ratio (OR) on the association between any NSAID use and incidence of invasive breast cancer. This resulted in 49 publications, from which the information was retrieved about type of study, exposure characteristics, breast cancer characteristics, and breast cancer-NSAID association. Meta-analyses were performed separately for case-control and cohort studies and for different hormone-receptor status. NSAID use reduced invasive breast cancer risk by about 20 %. A similar effect was found for aspirin, acetaminophen, COX-2 inhibitors and, to a lesser extent, ibuprofen. The effect of aspirin was similar in preventing hormone-receptor-positive breast cancer. This meta-analysis suggests a slightly protective effect of NSAIDs-especially aspirin and COX-2 inhibitors- against breast cancer, which seems to be restricted to ER/PR+tumors.
Bar-Yaacov, Dan; Bouskila, Amos; Mishmar, Dan
2013-01-01
Recently, we found dramatic mitochondrial DNA divergence of Israeli Chamaeleo chamaeleon populations into two geographically distinct groups. We aimed to examine whether the same pattern of divergence could be found in nuclear genes. However, no genomic resource is available for any chameleon species. Here we present the first chameleon transcriptome, obtained using deep sequencing (SOLiD). Our analysis identified 164,000 sequence contigs of which 19,000 yielded unique BlastX hits. To test the efficacy of our sequencing effort, we examined whether the chameleon and other available reptilian transcriptomes harbored complete sets of genes comprising known biochemical pathways, focusing on the nDNA-encoded oxidative phosphorylation (OXPHOS) genes as a model. As a reference for the screen, we used the human 86 (including isoforms) known structural nDNA-encoded OXPHOS subunits. Analysis of 34 publicly available vertebrate transcriptomes revealed orthologs for most human OXPHOS genes. However, OXPHOS subunit COX8 (Cytochrome C oxidase subunit 8), including all its known isoforms, was consistently absent in transcriptomes of iguanian lizards, implying loss of this subunit during the radiation of this suborder. The lack of COX8 in the suborder Iguania is intriguing, since it is important for cellular respiration and ATP production. Our sequencing effort added a new resource for comparative genomic studies, and shed new light on the evolutionary dynamics of the OXPHOS system. PMID:24009133
Bar-Yaacov, Dan; Bouskila, Amos; Mishmar, Dan
2013-01-01
Recently, we found dramatic mitochondrial DNA divergence of Israeli Chamaeleo chamaeleon populations into two geographically distinct groups. We aimed to examine whether the same pattern of divergence could be found in nuclear genes. However, no genomic resource is available for any chameleon species. Here we present the first chameleon transcriptome, obtained using deep sequencing (SOLiD). Our analysis identified 164,000 sequence contigs of which 19,000 yielded unique BlastX hits. To test the efficacy of our sequencing effort, we examined whether the chameleon and other available reptilian transcriptomes harbored complete sets of genes comprising known biochemical pathways, focusing on the nDNA-encoded oxidative phosphorylation (OXPHOS) genes as a model. As a reference for the screen, we used the human 86 (including isoforms) known structural nDNA-encoded OXPHOS subunits. Analysis of 34 publicly available vertebrate transcriptomes revealed orthologs for most human OXPHOS genes. However, OXPHOS subunit COX8 (Cytochrome C oxidase subunit 8), including all its known isoforms, was consistently absent in transcriptomes of iguanian lizards, implying loss of this subunit during the radiation of this suborder. The lack of COX8 in the suborder Iguania is intriguing, since it is important for cellular respiration and ATP production. Our sequencing effort added a new resource for comparative genomic studies, and shed new light on the evolutionary dynamics of the OXPHOS system.
Regression analysis exploring teacher impact on student FCI post scores
NASA Astrophysics Data System (ADS)
Mahadeo, Jonathan V.; Manthey, Seth R.; Brewe, Eric
2013-01-01
High School Modeling Workshops are designed to improve high school physics teachers' understanding of physics and how to teach using the Modeling method. The basic assumption is that the teacher plays a critical role in their students' physics education. This study investigated teacher impacts on students' Force Concept Inventory scores, (FCI), with the hopes of identifying quantitative differences between teachers. This study examined student FCI scores from 18 teachers with at least a year of teaching high school physics. This data was then evaluated using a General Linear Model (GLM), which allowed for a regression equation to be fitted to the data. This regression equation was used to predict student post FCI scores, based on: teacher ID, student pre FCI score, gender, and representation. The results show 12 out of 18 teachers significantly impact their student post FCI scores. The GLM further revealed that of the 12 teachers only five have a positive impact on student post FCI scores. Given these differences among teachers it is our intention to extend our analysis to investigate pedagogical differences between them.
Cardiorespiratory fitness and laboratory stress: a meta-regression analysis.
Jackson, Erica M; Dishman, Rod K
2006-01-01
We performed a meta-regression analysis of 73 studies that examined whether cardiorespiratory fitness mitigates cardiovascular responses during and after acute laboratory stress in humans. The cumulative evidence indicates that fitness is related to slightly greater reactivity, but better recovery. However, effects varied according to several study features and were smallest in the better controlled studies. Fitness did not mitigate integrated stress responses such as heart rate and blood pressure, which were the focus of most of the studies we reviewed. Nonetheless, potentially important areas, particularly hemodynamic and vascular responses, have been understudied. Women, racial/ethnic groups, and cardiovascular patients were underrepresented. Randomized controlled trials, including naturalistic studies of real-life responses, are needed to clarify whether a change in fitness alters putative stress mechanisms linked with cardiovascular health.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.
2013-01-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Estimation of crown closure from AVIRIS data using regression analysis
NASA Technical Reports Server (NTRS)
Staenz, K.; Williams, D. J.; Truchon, M.; Fritz, R.
1993-01-01
Crown closure is one of the input parameters used for forest growth and yield modelling. Preliminary work by Staenz et al. indicates that imaging spectrometer data acquired with sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have some potential for estimating crown closure on a stand level. The objectives of this paper are: (1) to establish a relationship between AVIRIS data and the crown closure derived from aerial photography of a forested test site within the Interior Douglas Fir biogeoclimatic zone in British Columbia, Canada; (2) to investigate the impact of atmospheric effects and the forest background on the correlation between AVIRIS data and crown closure estimates; and (3) to improve this relationship using multiple regression analysis.
Spatial regression analysis of traffic crashes in Seoul.
Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F
2016-06-01
Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands.
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…
Zhao, Qiu-jiong; Bai, Shao-cong; Cheng, Cheng; Tao, Ben-zhang; Wang, Le-kai; Liang, Shuang; Yin, Ling; Hang, Xing-yi; Shang, Ai-jia
2016-01-01
Copy number variations have been found in patients with neural tube abnormalities. In this study, we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents. Of eight copy number variations, four were non-polymorphic. These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes, and microcephaly. Gene function enrichment analysis revealed that COX8C, a gene associated with metabolic disorders of the nervous system, was located in the copy number variation region of Patient 1. Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome. Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease. PMID:27651783
Lofstedt, Ragnar E
2007-01-01
The field of risk communication has its roots in the environmental, chemical, space, and nuclear arenas. As a number of these sectors have now vastly improved their communication strategies, attention is being placed on sectors that have been more problematic as of late. Examples of such sectors, include the food industries and the pharmaceutical/health sector. This article focuses on how large, multinational pharmaceutical companies can better communicate risks by analysis of one specific case, namely, that of the Cox-2 controversy.(1) For purposes of this article, risk communication is best described as "the flow of information and risk evaluations back and forth between academic experts, regulatory practitioners, interest groups and the general public," and "big pharma" refers to the more traditional R & D-based, innovative pharmaceutical companies.
Risk factors for temporomandibular disorder: Binary logistic regression analysis
Magalhães, Bruno G.; de-Sousa, Stéphanie T.; de Mello, Victor V C.; da-Silva-Barbosa, André C.; de-Assis-Morais, Mariana P L.; Barbosa-Vasconcelos, Márcia M V.
2014-01-01
Objectives: To analyze the influence of socioeconomic and demographic factors (gender, economic class, age and marital status) on the occurrence of temporomandibular disorder. Study Design: One hundred individuals from urban areas in the city of Recife (Brazil) registered at Family Health Units was examined using Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) which addresses myofascial pain and joint problems (disc displacement, arthralgia, osteoarthritis and oesteoarthrosis). The Brazilian Economic Classification Criteria (CCEB) was used for the collection of socioeconomic and demographic data. Then, it was categorized as Class A (high social class), Classes B/C (middle class) and Classes D/E (very poor social class). The results were analyzed using Pearson’s chi-square test for proportions, Fisher’s exact test, nonparametric Mann-Whitney test and Binary logistic regression analysis. Results: None of the participants belonged to Class A, 72% belonged to Classes B/C and 28% belonged to Classes D/E. The multivariate analysis revealed that participants from Classes D/E had a 4.35-fold greater chance of exhibiting myofascial pain and 11.3-fold greater chance of exhibiting joint problems. Conclusions: Poverty is a important condition to exhibit myofascial pain and joint problems. Key words:Temporomandibular joint disorders, risk factors, prevalence. PMID:24316706
Mixed-effects Poisson regression analysis of adverse event reports
Gibbons, Robert D.; Segawa, Eisuke; Karabatsos, George; Amatya, Anup K.; Bhaumik, Dulal K.; Brown, C. Hendricks; Kapur, Kush; Marcus, Sue M.; Hur, Kwan; Mann, J. John
2008-01-01
SUMMARY A new statistical methodology is developed for the analysis of spontaneous adverse event (AE) reports from post-marketing drug surveillance data. The method involves both empirical Bayes (EB) and fully Bayes estimation of rate multipliers for each drug within a class of drugs, for a particular AE, based on a mixed-effects Poisson regression model. Both parametric and semiparametric models for the random-effect distribution are examined. The method is applied to data from Food and Drug Administration (FDA)’s Adverse Event Reporting System (AERS) on the relationship between antidepressants and suicide. We obtain point estimates and 95 per cent confidence (posterior) intervals for the rate multiplier for each drug (e.g. antidepressants), which can be used to determine whether a particular drug has an increased risk of association with a particular AE (e.g. suicide). Confidence (posterior) intervals that do not include 1.0 provide evidence for either significant protective or harmful associations of the drug and the adverse effect. We also examine EB, parametric Bayes, and semiparametric Bayes estimators of the rate multipliers and associated confidence (posterior) intervals. Results of our analysis of the FDA AERS data revealed that newer antidepressants are associated with lower rates of suicide adverse event reports compared with older antidepressants. We recommend improvements to the existing AERS system, which are likely to improve its public health value as an early warning system. PMID:18404622
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…
Analysis of retirement income adequacy using quantile regression: A case study in Malaysia
NASA Astrophysics Data System (ADS)
Alaudin, Ros Idayuwati; Ismail, Noriszura; Isa, Zaidi
2015-09-01
Quantile regression is a statistical analysis that does not restrict attention to the conditional mean and therefore, permitting the approximation of the whole conditional distribution of a response variable. Quantile regression is a robust regression to outliers compared to mean regression models. In this paper, we demonstrate how quantile regression approach can be used to analyze the ratio of projected wealth to needs (wealth-needs ratio) during retirement.
Power analysis of principal components regression in genetic association studies.
Shen, Yan-feng; Zhu, Jun
2009-10-01
Association analysis provides an opportunity to find genetic variants underlying complex traits. A principal components regression (PCR)-based approach was shown to outperform some competing approaches. However, a limitation of this method is that the principal components (PCs) selected from single nucleotide polymorphisms (SNPs) may be unrelated to the phenotype. In this article, we investigate the theoretical properties of such a method in more detail. We first derive the exact power function of the test based on PCR, and hence clarify the relationship between the test power and the degrees of freedom (DF). Next, we extend the PCR test to a general weighted PCs test, which provides a unified framework for understanding the properties of some related statistics. We then compare the performance of these tests. We also introduce several data-driven adaptive alternatives to overcome difficulties in the PCR approach. Finally, we illustrate our results using simulations based on real genotype data. Simulation study shows the risk of using the unsupervised rule to determine the number of PCs, and demonstrates that there is no single uniformly powerful method for detecting genetic variants.
A flexible count data regression model for risk analysis.
Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P
2008-02-01
In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.
NASA Astrophysics Data System (ADS)
Haddad, Khaled; Rahman, Ataur
2012-04-01
SummaryIn this article, an approach using Bayesian Generalised Least Squares (BGLS) regression in a region-of-influence (ROI) framework is proposed for regional flood frequency analysis (RFFA) for ungauged catchments. Using the data from 399 catchments in eastern Australia, the BGLS-ROI is constructed to regionalise the flood quantiles (Quantile Regression Technique (QRT)) and the first three moments of the log-Pearson type 3 (LP3) distribution (Parameter Regression Technique (PRT)). This scheme firstly develops a fixed region model to select the best set of predictor variables for use in the subsequent regression analyses using an approach that minimises the model error variance while also satisfying a number of statistical selection criteria. The identified optimal regression equation is then used in the ROI experiment where the ROI is chosen for a site in question as the region that minimises the predictive uncertainty. To evaluate the overall performances of the quantiles estimated by the QRT and PRT, a one-at-a-time cross-validation procedure is applied. Results of the proposed method indicate that both the QRT and PRT in a BGLS-ROI framework lead to more accurate and reliable estimates of flood quantiles and moments of the LP3 distribution when compared to a fixed region approach. Also the BGLS-ROI can deal reasonably well with the heterogeneity in Australian catchments as evidenced by the regression diagnostics. Based on the evaluation statistics it was found that both BGLS-QRT and PRT-ROI perform similarly well, which suggests that the PRT is a viable alternative to QRT in RFFA. The RFFA methods developed in this paper is based on the database available in eastern Australia. It is expected that availability of a more comprehensive database (in terms of both quality and quantity) will further improve the predictive performance of both the fixed and ROI based RFFA methods presented in this study, which however needs to be investigated in future when such a
Elghafghuf, Adel; Dufour, Simon; Reyher, Kristen; Dohoo, Ian; Stryhn, Henrik
2014-12-01
Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation
Quantile regression provides a fuller analysis of speed data.
Hewson, Paul
2008-03-01
Considerable interest already exists in terms of assessing percentiles of speed distributions, for example monitoring the 85th percentile speed is a common feature of the investigation of many road safety interventions. However, unlike the mean, where t-tests and ANOVA can be used to provide evidence of a statistically significant change, inference on these percentiles is much less common. This paper examines the potential role of quantile regression for modelling the 85th percentile, or any other quantile. Given that crash risk may increase disproportionately with increasing relative speed, it may be argued these quantiles are of more interest than the conditional mean. In common with the more usual linear regression, quantile regression admits a simple test as to whether the 85th percentile speed has changed following an intervention in an analogous way to using the t-test to determine if the mean speed has changed by considering the significance of parameters fitted to a design matrix. Having briefly outlined the technique and briefly examined an application with a widely published dataset concerning speed measurements taken around the introduction of signs in Cambridgeshire, this paper will demonstrate the potential for quantile regression modelling by examining recent data from Northamptonshire collected in conjunction with a "community speed watch" programme. Freely available software is used to fit these models and it is hoped that the potential benefits of using quantile regression methods when examining and analysing speed data are demonstrated.
Hu, Yao-Dong; Pang, Hui-Zhong; Li, De-Sheng; Ling, Shan-Shan; Lan, Dan; Wang, Ye; Zhu, Yun; Li, Di-Yan; Wei, Rong-Ping; Zhang, He-Min; Wang, Cheng-Dong
2016-11-05
As the rate-limiting enzyme of the mitochondrial respiratory chain, cytochrome c oxidase (COX) plays a crucial role in biological metabolism. "Living fossil" giant panda (Ailuropoda melanoleuca) is well-known for its special bamboo diet. In an effort to explore functional variation of COX1 in the energy metabolism behind giant panda's low-energy bamboo diet, we looked at genetic variation of COX1 gene in giant panda, and tested for its selection effect. In 1545 base pairs of the gene from 15 samples, 9 positions were variable and 1 mutation leaded to an amino acid sequence change. COX1 gene produces six haplotypes, nucleotide (pi), haplotype diversity (Hd). In addition, the average number of nucleotide differences (k) is 0.001629±0.001036, 0.8083±0.0694 and 2.517, respectively. Also, dN/dS ratio is significantly below 1. These results indicated that giant panda had a low population genetic diversity, and an obvious purifying selection of the COX1 gene which reduces synthesis of ATP determines giant panda's low-energy bamboo diet. Phylogenetic trees based on the COX1 gene were constructed to demonstrate that giant panda is the sister group of other Ursidae.
Technology Transfer Automated Retrieval System (TEKTRAN)
Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...
Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models
ERIC Educational Resources Information Center
Shieh, Gwowen
2009-01-01
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…
Measuring Habituation in Infants: An Approach Using Regression Analysis.
ERIC Educational Resources Information Center
Ashmead, Daniel H.; Davis, DeFord L.
1996-01-01
Used computer simulations to examine effectiveness of different criteria for measuring infant visual habituation. Found that a criterion based on fitting a second-order polynomial regression function to looking-time data produced more accurate estimation of looking times and higher power for detecting novelty effects than did the traditional…
Grades, Gender, and Encouragement: A Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Owen, Ann L.
2010-01-01
The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…
NASA Technical Reports Server (NTRS)
Parsons, Vickie s.
2009-01-01
The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.
Liu, Xiang; Saat, M Rapik; Qin, Xiao; Barkan, Christopher P L
2013-10-01
Derailments are the most common type of freight-train accidents in the United States. Derailments cause damage to infrastructure and rolling stock, disrupt services, and may cause casualties and harm the environment. Accordingly, derailment analysis and prevention has long been a high priority in the rail industry and government. Despite the low probability of a train derailment, the potential for severe consequences justify the need to better understand the factors influencing train derailment severity. In this paper, a zero-truncated negative binomial (ZTNB) regression model is developed to estimate the conditional mean of train derailment severity. Recognizing that the mean is not the only statistic describing data distribution, a quantile regression (QR) model is also developed to estimate derailment severity at different quantiles. The two regression models together provide a better understanding of train derailment severity distribution. Results of this work can be used to estimate train derailment severity under various operational conditions and by different accident causes. This research is intended to provide insights regarding development of cost-efficient train safety policies.
Li, Qing; Liu, Liu; Liu, Yanling; Zhou, Huirong; Yang, Zhi; Yuan, Keng; Min, Weiping
2015-01-01
The correlationship between COX-2 gene polymorphisms and breast cancer has been wildly studied, but the results remain controversial. Hence, the present meta-analysis aimed to investigate the association between COX-2 SNPs (rs5275, rs20417, rs689466, rs5277, rs2206593) and risk of breast cancer. Data were collected from PubMed, Embase and China National Knowledge Infrastructure. Summary odds ratio (OR) with 95 % confidence interval (CI) was applied to assess the relationship. Heterogeneity test, sensitivity analysis and publication bias test were also performed. There were 17 articles that contained 19 studies in this research. Fourteen case-control studies with 15,007 breast cancer cases and 20,005 controls were concerning rs5275 polymorphism, and 8 case-control studies with 10,216 cases and 12,839 controls were about rs20417 polymorphism. Other three polymorphisms (rs689466, rs2206593, rs5277) were studied in 5, 3 and 3 studies, respectively. COX-2-rs20417 CC genotype was significantly associated with increased risk of breast cancer when comparing to G allele [ORs were 1.231 (1.050-1.444) for CC vs. GG, P = 0.01, 1.223 (1.045-1.432) for CC vs. G carrier, P = 0.01]. Furthermore, the results of the subgroup analysis by ethnicity suggested that C allele significantly contributed to the risk of breast cancer for Asians [1.459 (1.182-1.802) for GC vs. GG, 1.472 (1.201-1.805) for C carrier vs. GG]. However, no association was found for rs5275, rs689466, rs5277 and rs2206593 in all comparison modes. This meta-analysis indicated that the COX-2 rs20417 polymorphism contributed to genetic susceptibility of breast cancer. In contrast, COX-2 rs5275, rs689466, rs2206593 and rs5277 polymorphisms might be not associated with the risk of breast cancer.
DFT analysis and spectral characteristics of Celecoxib a potent COX-2 inhibitor
NASA Astrophysics Data System (ADS)
Vijayakumar, B.; Kannappan, V.; Sathyanarayanamoorthi, V.
2016-10-01
Extensive quantum mechanical studies are carried out on Celecoxib (CXB), a new generation drug to understand the vibrational and electronic spectral characteristics of the molecule. The vibrational frequencies of CXB are computed by HF and B3LYP methods with 6-311++G (d, p) basis set. The theoretical scaled vibrational frequencies have been assigned and they agreed satisfactorily with experimental FT-IR and Raman frequencies. The theoretical maximum wavelength of absorption of CXB are calculated in water and ethanol by TD-DFT method and these values are compared with experimentally determined λmax values. The spectral and Natural bonds orbital (NBO) analysis in conjunction with spectral data established the presence of intra molecular interactions such as mesomeric, hyperconjugative and steric effects in CXB. The electron density at various positions and reactivity descriptors of CXB indicate that the compound functions as a nucleophile and establish that aromatic ring system present in the molecule is the site of drug action. Electronic distribution and HOMO - LUMO energy values of CXB are discussed in terms of intra-molecular interactions. Computed values of Mulliken charges and thermodynamic properties of CXB are reported.
Sharbatkhori, Mitra; Fasihi Harandi, Majid; Mirhendi, Hossein; Hajialilo, Elham; Kia, Eshrat Beigom
2011-03-01
Nineteen hydatid cyst isolates collected from camels in central Iran were subjected to sequences analysis of mitochondrial cytochrome c oxidase subunit 1 (cox1) and NADH dehydrogenase subunit 1 (nad1) genes. A consensus sequence obtained containing 366 nucleotides for cox1 and 471 nucleotides for nad1 genes. Overall, the camel isolates indicated five different sequences in cox1 and nine in nad1 genes. The sequences analysis indicated that 26.3%, 42.1%, and 31.6% of isolates belonging to G1, G3, and G6 genotypes of Echinococcus granulosus, respectively. The isolates with G3 genotype indicated one cox1 sequence having 100% homology with reference G3 sequence (AN: M84663) and two different nad1 sequences, one having 100% homology with reference G3 sequence (AN: AJ237634) and the other with a silent mutation (G to A) in position 279. The presence of G3 genotype (buffalo strain) of E. granulosus as dominant genotype in camels is emphasized. As G3 genotype has formerly been reported in human, the epidemiological role of camels is warranted in future surveys.
Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis
NASA Astrophysics Data System (ADS)
Michel, Vincent; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand
The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding regions of the brain whose functional signal reliably predicts some behavioral information makes it possible to better understand how this information is encoded or processed in the brain. However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. A commonly used solution is to regularize the weights of the parametric prediction function. However, model specification needs a careful design to balance adaptiveness and sparsity. In this paper, we introduce a novel method, Multi - Class Sparse Bayesian Regression(MCBR), that generalizes classical approaches such as Ridge regression and Automatic Relevance Determination. Our approach is based on a grouping of the features into several classes, where each class is regularized with specific parameters. We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies than reference methods, and yields more interpretable feature loadings.
A new approach in regression analysis for modeling adsorption isotherms.
Marković, Dana D; Lekić, Branislava M; Rajaković-Ognjanović, Vladana N; Onjia, Antonije E; Rajaković, Ljubinka V
2014-01-01
Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart's percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method.
Heteroscedastic regression analysis of factors affecting BMD monitoring.
Sadatsafavi, Mohsen; Moayyeri, Alireza; Wang, Liqun; Leslie, William D
2008-11-01
Identifying factors affecting BMD precision and interindividual heterogeneity in BMD change can help optimize BMD monitoring. BMD change for the lumbar spine and total hip for short-term reproducibility (n = 328) and long-term clinical monitoring (n = 2720) populations were analyzed with heteroscedastic regression using linear prediction for mean (monitoring population only) and log-linear prediction for SD (both populations). For clinical monitoring, male sex, baseline body mass index (BMI), and systemic corticosteroid use were associated with greater SD of BMD change. Weight gain was negatively associated with SD for the hip, whereas height change was positively associated with SD for the spine. Each additional year of monitoring increased the SD by 6.5-9.2%. Osteoporosis treatment affected mean change but did not increase dispersion. For short-term reproducibility, performing scans on a different day increased the SD of measurement error by 38-44%. Baseline BMD, difference in bone area, and a repeat scan performed by different technologists were associated with higher measurement error only for the hip. For both samples, heteroscedastic regression outperformed models that assumed homogeneous variance. Heteroscedastic regression techniques are powerful yet underused tools in analyzing longitudinal BMD data and can be used to generate individualized predictions of BMD change and measurement error.
A New Approach in Regression Analysis for Modeling Adsorption Isotherms
Onjia, Antonije E.
2014-01-01
Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart's percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method. PMID:24672394
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…
Yang, Man; Wang, Hong-Tao; Zhao, Miao; Meng, Wen-Bo; Ou, Jin-Qing; He, Jun-Hui; Zou, Bing; Lei, Ping-Guang
2015-01-01
Abstract Currently 2 difference classes of cyclooxygenase (COX)-2 inhibitors, coxibs and relatively selective COX-2 inhibitors, are available for patients requiring nonsteroidal anti-inflammatory drug (NSAID) therapy; their gastroprotective effect is hardly directly compared. The aim of this study was to compare the gastroprotective effect of relatively selective COX-2 inhibitors with coxibs. MEDLINE, EMBASE, and the Cochrane Library (from their inception to March 2015) were searched for potential eligible studies. We included randomized controlled trials comparing coxibs (celecoxib, etoricoxib, parecoxib, and lumiracoxib), relatively selective COX-2 inhibitors (nabumetone, meloxicam, and etodolac), and nonselective NSAIDs with a study duration ≥4 weeks. Comparative effectiveness and safety data were pooled by Bayesian network meta-analysis. The primary outcomes were ulcer complications and symptomatic ulcer. Summary effect-size was calculated as risk ratio (RR), together with the 95% confidence interval (CI). This study included 36 trials with a total of 112,351 participants. Network meta-analyses indicated no significant difference between relatively selective COX-2 inhibitors and coxibs regarding ulcer complications (RR, 1.38; 95% CI, 0.47–3.27), symptomatic ulcer (RR, 1.02; 95% CI, 0.09–3.92), and endoscopic ulcer (RR, 1.18; 95% CI, 0.37–2.96). Network meta-analyses adjusting potential influential factors (age, sex, previous ulcer disease, and follow-up time), and sensitivity analyses did not reveal any major change to the main results. Network meta-analyses suggested that relatively selective COX-2 inhibitors and coxibs were associated with comparable incidences of total adverse events (AEs) (RR, 1.09; 95% CI, 0.93–1.31), gastrointestinal AEs (RR, 1.04; 95% CI, 0.87–1.25), total withdrawals (RR, 1.00; 95% CI, 0.74–1.33), and gastrointestinal AE-related withdrawals (RR, 1.02; 95% CI, 0.57–1.74). Relatively selective COX-2 inhibitors appear to be
Striker, Lora K.; Medalie, Laura
1997-01-01
This report provides the results of a detailed Level II analysis of scour potential at structure MORETH00010021 on Town Highway 1 crossing Cox Brook, Moretown, Vermont (figures 1–8). A Level II study is a basic engineering analysis of the site, including a quantitative analysis of stream stability and scour (U.S. Department of Transportation, 1993). Results of a Level I scour investigation also are included in Appendix E of this report. A Level I investigation provides a qualitative geomorphic characterization of the study site. Information on the bridge, gleaned from Vermont Agency of Transportation (VTAOT) files, was compiled prior to conducting Level I and Level II analyses and is found in Appendix D. The site is in the Green Mountain section of the New England physiographic province in north-central Vermont. The 2.85-mi2 drainage area is in a predominantly rural and forested basin. In the vicinity of the study site, the surface cover is predominantly forested. In the study area, Cox Brook has an incised, sinuous channel with a slope of approximately 0.02 ft/ft, an average channel top width of 23 ft and an average bank height of 4 ft. The channel bed material ranges from gravel to cobble with a median grain size (D50) of 47.5 mm (0.156 ft). The geomorphic assessment at the time of the Level I and Level II site visit on July 18, 1996, indicated that the reach was stable. The Town Highway 1 crossing of Cox Brook is a 29-ft-long, two-lane bridge consisting of one 27-foot steel-beam span (Vermont Agency of Transportation, written communication, October 13, 1995). The opening length of the structure parallel to the bridge face is 24.8 ft. The bridge is supported by vertical, concrete abutments with wingwalls. The channel is skewed approximately 60 degrees to the opening while the measured opening-skew-to-roadway is 40 degrees. A scour hole 1.0 ft deeper than the mean thalweg depth was observed along the left abutment downstream during the Level I assessment. The
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.
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.
Ferrari, Alberto
2017-02-16
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
Zarei, Zabiholah; Kia, Eshrat Beigom; Heidari, Zahra; Mikaeili, Fattaneh; Mohebali, Mehdi; Sharifdini, Meysam
2016-01-01
Dirofilaria immitis is an important filarial nematode in dogs. In this study, age and sex distribution of this zoonotic nematode among dogs were investigated in northwest of Iran in Meshkin-Shahr city. Molecular characteristics of the isolates, based on cytochrome oxidase subunit 1 (COX1) gene were compared to the isolates from other areas of the world.Blood samples were collected from 91 dogs which were selected by simple classified accidental sampling. Thin and thick blood smear examinations were used to find out infectivity with D. immitis. DNA extraction was performed from adult D. immitis recovered from heart of infected dogs. The COX1 gene was amplified and sequenced. Phylogenetic analysis was carried out using sequences obtained in this study along with relevant sequences deposited in the GenBank. Phylogenetic analysis and sequence variation was performed using MEGA software in comparison with those COX1 sequences deposited in GenBank. Out of 91 dogs, 19 (20.87%) were found positive for infection with D. immitis. There was no statistically significant difference between males and females of dogs in terms of D. immitis infection. However, the rate of infection in dogs more than 2 years old was significantly higher than those with lower age. Both sequences analyzed in this study showed 100% homology to each other. Intra-species variation of these isolates with those from other areas of the world amounted to 0 to 0.50%. Phylogenetic analysis of the COX1 gene suggested that it is conserved, and can be used for study on genetic diversity and classification of filarial nematodes. PMID:28144425
Gogoi, Dhrubajyoti; Bezbaruah, Rajib Lochan; Bordoloi, Manabjyoti; Sarmah, Rajeev; Bora, Tarun Chandra
2012-01-01
Litsea spp of Laural family are traditionally used as herbal medicine for treating inflammation including gastroenterologia, oedema and rheumatic arthritis. Therefore, it is of interest to investigate and understand the molecular principles for such actions. Here, we have illustrated the binding of thirteen Litsea derived biologically active compounds against the inflammation associated target COX (cyclo-oxygenase) -2 enzymes. We compared the binding information of these compounds with a selected number of already known COX-2 inhibitors. The comparison reflected that some of these compounds such as linderol, catechin, 6'-hydroxy-2',3',4' - trimethoxy-chalcone and litseaone have better or equivalent binding features compared to already known inhibitory compounds namely celecoxib, acetylsalicylic acid, rofecoxib. Therefore, all these small compounds reported from plant Litsea spp were found to possess potential medicinal values with anti-inflammatory properties. PMID:23139590
Gogoi, Dhrubajyoti; Bezbaruah, Rajib Lochan; Bordoloi, Manabjyoti; Sarmah, Rajeev; Bora, Tarun Chandra
2012-01-01
Litsea spp of Laural family are traditionally used as herbal medicine for treating inflammation including gastroenterologia, oedema and rheumatic arthritis. Therefore, it is of interest to investigate and understand the molecular principles for such actions. Here, we have illustrated the binding of thirteen Litsea derived biologically active compounds against the inflammation associated target COX (cyclo-oxygenase) -2 enzymes. We compared the binding information of these compounds with a selected number of already known COX-2 inhibitors. The comparison reflected that some of these compounds such as linderol, catechin, 6'-hydroxy-2',3',4' - trimethoxy-chalcone and litseaone have better or equivalent binding features compared to already known inhibitory compounds namely celecoxib, acetylsalicylic acid, rofecoxib. Therefore, all these small compounds reported from plant Litsea spp were found to possess potential medicinal values with anti-inflammatory properties.
Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking
2015-10-20
412TW-PA-15238 Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking DANIEL T. LAIRD AIR...COVERED (From - To) 20 OCT 15 – 23 OCT 15 4. TITLE AND SUBTITLE Analysis of Covariance with Linear Regression Error Model on Antenna Control Tracking...analysis of variance (ANOVA) to decide for the null- or alternative-hypotheses of a telemetry antenna control unit’s (ACU) ability to track on C-band
Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis
ERIC Educational Resources Information Center
Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.
2006-01-01
Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…
A Noncentral "t" Regression Model for Meta-Analysis
ERIC Educational Resources Information Center
Camilli, Gregory; de la Torre, Jimmy; Chiu, Chia-Yi
2010-01-01
In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral "t" distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this…
Development of a User Interface for a Regression Analysis Software Tool
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.
Multivariate Alternatives to Regression Analysis in the Evaluation of Salary Equity-Parity.
ERIC Educational Resources Information Center
Carter, Richard D.; And Others
1984-01-01
The analysis of salary equity-parity typically involves the use of multiple regression analysis to determine predicted salary and the residual differences between predicted and actual salary. Two alternatives are presented, canonical analysis and multiple discriminant analysis. (Author/MLW)
Clinical significance of Cox-2, Survivin and Bcl-2 expression in hepatocellular carcinoma (HCC).
Yang, Yu; Zhu, Jiang; Gou, Hongfeng; Cao, Dan; Jiang, Ming; Hou, Mei
2011-09-01
Cox-2, Survivin and Bcl-2 are frequently overexpressed in numerous types of cancers. They are known to be the important regulators of apoptosis. This study was designed to investigate the correlation between the clinical characteristics and the expression of Cox-2, Survivin and Bcl-2 in hepatocellular carcinoma. A total of 63 postoperative hepatocellular carcinoma (HCC) samples, 10 adjacent non-tumor samples and 10 normal liver samples were immunochemically detected for the expression of Cox-2, Survivin and Bcl-2. A median follow-up of 4 years for the 63 HCC patients was conducted. Univariate tests and multivariate Cox regression were performed for statistical analysis. The Kaplan-Meier method was used to analyze the survival. Positive expression of Cox-2 (84.3%) and Survivin (77.8%) was detected significantly more frequently in the HCC samples than in the normal liver tissues (30% and 0, respectively). Bcl-2 was highly expressed in the adjacent non-tumor tissue. Cox-2 was positively correlative to Survivin. Survivin and Bcl-2 were significantly associated with the pathological grade of HCC (P<0.05). Expression of both Cox-2 and Survivin was significantly associated with the poor overall survival (OS) (P=0.0141, P=0.0039). Furthermore, multivariate analysis confirmed the independent prognostic value of Survivin expression, along with tumor size and hepatic function. Cox-2 and Survivin were highly expressed in the HCC tissue. Survivin and Bcl-2 were significantly associated with the pathological grade of HCC. The expression of Survivin was an independent prognostic factor for HCC after a hepatectomy. Treatment that inhibits Survivin may be a promising targeted approach in HCC.
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.
Wang, Ming; Flanders, W Dana; Bostick, Roberd M; Long, Qi
2012-12-20
Measurement error is common in epidemiological and biomedical studies. When biomarkers are measured in batches or groups, measurement error is potentially correlated within each batch or group. In regression analysis, most existing methods are not applicable in the presence of batch-specific measurement error in predictors. We propose a robust conditional likelihood approach to account for batch-specific error in predictors when batch effect is additive and the predominant source of error, which requires no assumptions on the distribution of measurement error. Although a regression model with batch as a categorical covariable yields the same parameter estimates as the proposed conditional likelihood approach for linear regression, this result does not hold in general for all generalized linear models, in particular, logistic regression. Our simulation studies show that the conditional likelihood approach achieves better finite sample performance than the regression calibration approach or a naive approach without adjustment for measurement error. In the case of logistic regression, our proposed approach is shown to also outperform the regression approach with batch as a categorical covariate. In addition, we also examine a 'hybrid' approach combining the conditional likelihood method and the regression calibration method, which is shown in simulations to achieve good performance in the presence of both batch-specific and measurement-specific errors. We illustrate our method by using data from a colorectal adenoma study.
Rastegari, Azam; Haghdoost, Ali Akbar; Baneshi, Mohammad Reza
2013-01-01
Background Due to the importance of medical studies, researchers of this field should be familiar with various types of statistical analyses to select the most appropriate method based on the characteristics of their data sets. Classification and regression trees (CARTs) can be as complementary to regression models. We compared the performance of a logistic regression model and a CART in predicting drug injection among prisoners. Methods Data of 2720 Iranian prisoners was studied to determine the factors influencing drug injection. The collected data was divided into two groups of training and testing. A logistic regression model and a CART were applied on training data. The performance of the two models was then evaluated on testing data. Findings The regression model and the CART had 8 and 4 significant variables, respectively. Overall, heroin use, history of imprisonment, age at first drug use, and marital status were important factors in determining the history of drug injection. Subjects without the history of heroin use or heroin users with short-term imprisonment were at lower risk of drug injection. Among heroin addicts with long-term imprisonment, individuals with higher age at first drug use and married subjects were at lower risk of drug injection. Although the logistic regression model was more sensitive than the CART, the two models had the same levels of specificity and classification accuracy. Conclusion In this study, both sensitivity and specificity were important. While the logistic regression model had better performance, the graphical presentation of the CART simplifies the interpretation of the results. In general, a combination of different analytical methods is recommended to explore the effects of variables. PMID:24494152
Deng, Yangyang; Parajuli, Prem B.
2011-08-10
Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.
Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles
Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi; Kiliccote, Sila
2009-06-28
This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.
Criteria for the use of regression analysis for remote sensing of sediment and pollutants
NASA Technical Reports Server (NTRS)
Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)
1982-01-01
Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.
ERIC Educational Resources Information Center
Ferrer, Alvaro J. Arce; Wang, Lin
This study compared the classification performance among parametric discriminant analysis, nonparametric discriminant analysis, and logistic regression in a two-group classification application. Field data from an organizational survey were analyzed and bootstrapped for additional exploration. The data were observed to depart from multivariate…
Partitioning Predicted Variance into Constituent Parts: A Primer on Regression Commonality Analysis.
ERIC Educational Resources Information Center
Amado, Alfred J.
Commonality analysis is a method of decomposing the R squared in a multiple regression analysis into the proportion of explained variance of the dependent variable associated with each independent variable uniquely and the proportion of explained variance associated with the common effects of one or more independent variables in various…
Modeling of retardance in ferrofluid with Taguchi-based multiple regression analysis
NASA Astrophysics Data System (ADS)
Lin, Jing-Fung; Wu, Jyh-Shyang; Sheu, Jer-Jia
2015-03-01
The citric acid (CA) coated Fe3O4 ferrofluids are prepared by a co-precipitation method and the magneto-optical retardance property is measured by a Stokes polarimeter. Optimization and multiple regression of retardance in ferrofluids are executed by combining Taguchi method and Excel. From the nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, volume of CA, and coating temperature, influence sequence and excellent program are found. Multiple regression analysis and F-test on the significance of regression equation are performed. It is found that the model F value is much larger than Fcritical and significance level P <0.0001. So it can be concluded that the regression model has statistically significant predictive ability. Substituting excellent program into equation, retardance is obtained as 32.703°, higher than the highest value in tests by 11.4%.
Hawkey, C J
2005-01-01
The role of selective cyclooxygenase (COX)-2 inhibitors in medical practice has become controversial since evidence emerged that their use is associated with an increased risk of myocardial infarction. Selective COX-2 inhibitors were seen as successor to non-selective non-steroidal anti-inflammatory drugs, in turn successors to aspirin. The importance of pain relief means that such drugs have always attracted attention. The fact that they work through inhibition of cyclooxygenase, are widespread, and have multiple effects also means that adverse effects that were unanticipated (even though predictable) have always emerged. In this paper I therefore present an historical perspective so that the lessons of the past may be applied to the present. PMID:16227351
Mitochondrial disease genes COA6, COX6B and SCO2 have overlapping roles in COX2 biogenesis
Ghosh, Alok; Pratt, Anthony T.; Soma, Shivatheja; Theriault, Sarah G.; Griffin, Aaron T.; Trivedi, Prachi P.; Gohil, Vishal M.
2016-01-01
Biogenesis of cytochrome c oxidase (CcO), the terminal enzyme of the mitochondrial respiratory chain, is a complex process facilitated by several assembly factors. Pathogenic mutations were recently reported in one such assembly factor, COA6, and our previous work linked Coa6 function to mitochondrial copper metabolism and expression of Cox2, a copper-containing subunit of CcO. However, the precise role of Coa6 in Cox2 biogenesis remained unknown. Here we show that yeast Coa6 is an orthologue of human COA6, and like Cox2, is regulated by copper availability, further implicating it in copper delivery to Cox2. In order to place Coa6 in the Cox2 copper delivery pathway, we performed a comprehensive genetic epistasis analysis in the yeast Saccharomyces cerevisiae and found that simultaneous deletion of Coa6 and Sco2, a mitochondrial copper metallochaperone, or Coa6 and Cox12/COX6B, a structural subunit of CcO, completely abrogates Cox2 biogenesis. Unlike Coa6 deficient cells, copper supplementation fails to rescue Cox2 levels of these double mutants. Overexpression of Cox12 or Sco proteins partially rescues the coa6Δ phenotype, suggesting their overlapping but non-redundant roles in copper delivery to Cox2. These genetic data are strongly corroborated by biochemical studies demonstrating physical interactions between Coa6, Cox2, Cox12 and Sco proteins. Furthermore, we show that patient mutations in Coa6 disrupt Coa6–Cox2 interaction, providing the biochemical basis for disease pathogenesis. Taken together, these results place COA6 in the copper delivery pathway to CcO and, surprisingly, link it to a previously unidentified function of CcO subunit Cox12 in Cox2 biogenesis. PMID:26669719
Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-01-01
Abstract Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (−1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis.
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-06-01
Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight
A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina.
Curtis, Andrew; Li, Bin; Marx, Brian D; Mills, Jacqueline W; Pine, John
2011-01-01
This paper analyses structural and personal exposure to Hurricane Katrina. Structural exposure is measured by flood height and building damage; personal exposure is measured by the locations of 911 calls made during the response. Using these variables, this paper characterises the geography of exposure and also demonstrates the utility of a robust analytical approach in understanding health-related challenges to disadvantaged populations during recovery. Analysis is conducted using a contemporary statistical approach, a multiple additive regression tree (MART), which displays considerable improvement over traditional regression analysis. By using MART, the percentage of improvement in R-squares over standard multiple linear regression ranges from about 62 to more than 100 per cent. The most revealing finding is the modelled verification that African Americans experienced disproportionate exposure in both structural and personal contexts. Given the impact of exposure to health outcomes, this finding has implications for understanding the long-term health challenges facing this population.
A general framework for the use of logistic regression models in meta-analysis.
Simmonds, Mark C; Higgins, Julian Pt
2016-12-01
Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy.
Using Refined Regression Analysis To Assess The Ecological Services Of Restored Wetlands
A hierarchical approach to regression analysis of wetland water treatment was conducted to determine which factors are the most appropriate for characterizing wetlands of differing structure and function. We used this approach in an effort to identify the types and characteristi...
Factor Regression Analysis: A New Method for Weighting Predictors. Final Report.
ERIC Educational Resources Information Center
Curtis, Ervin W.
The optimum weighting of variables to predict a dependent-criterion variable is an important problem in nearly all of the social and natural sciences. Although the predominant method, multiple regression analysis (MR), yields optimum weights for the sample at hand, these weights are not generally optimum in the population from which the sample was…
Catching up with Harvard: Results from Regression Analysis of World Universities League Tables
ERIC Educational Resources Information Center
Li, Mei; Shankar, Sriram; Tang, Kam Ki
2011-01-01
This paper uses regression analysis to test if the universities performing less well according to Shanghai Jiao Tong University's world universities league tables are able to catch up with the top performers, and to identify national and institutional factors that could affect this catching up process. We have constructed a dataset of 461…
Multiple Logistic Regression Analysis of Cigarette Use among High School Students
ERIC Educational Resources Information Center
Adwere-Boamah, Joseph
2011-01-01
A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…
ERIC Educational Resources Information Center
Schulz, Wolfram
Differences in student knowledge about democracy, institutions, and citizenship and students skills in interpreting political communication were studied through multilevel regression analysis of results from the second International Education Association (IEA) Study. This study provides data on 14-year-old students from 28 countries in Europe,…
What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis
ERIC Educational Resources Information Center
Thomas, Emily H.; Galambos, Nora
2004-01-01
To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…
Multiple Regression Analysis of Sib-Pair Data on Reading to Detect Quantitative Trait Loci.
ERIC Educational Resources Information Center
Fulker, D. W.; And Others
1991-01-01
Applies an extension of an earlier multiple regression model for twin analysis to the problem of detecting linkage in a quantitative trait. Detects a number of possible linkages, indicating that the approach is effective. Discusses detecting genotype-environment interaction and the issue of power. (RS)
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…
Ultrasound-enhanced bioscouring of greige cotton: regression analysis of process factors
Technology Transfer Automated Retrieval System (TEKTRAN)
Process factors of enzyme concentration, time, power and frequency were investigated for ultrasound-enhanced bioscouring of greige cotton. A fractional factorial experimental design and subsequent regression analysis of the process factors were employed to determine the significance of each factor a...
Predictive Discriminant Analysis Versus Logistic Regression in Two-Group Classification Problems.
ERIC Educational Resources Information Center
Meshbane, Alice; Morris, John D.
A method for comparing the cross-validated classification accuracies of predictive discriminant analysis and logistic regression classification models is presented under varying data conditions for the two-group classification problem. With this method, separate-group, as well as total-sample proportions of the correct classifications, can be…
Barros, Aluísio JD; Hirakata, Vânia N
2003-01-01
Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations. PMID:14567763
Quantile regression in the presence of monotone missingness with sensitivity analysis.
Liu, Minzhao; Daniels, Michael J; Perri, Michael G
2016-01-01
In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management.
Regression analysis of non-contact acousto-thermal signature data
NASA Astrophysics Data System (ADS)
Criner, Amanda; Schehl, Norman
2016-05-01
The non-contact acousto-thermal signature (NCATS) is a nondestructive evaluation technique with potential to detect fatigue in materials such as noisy titanium and polymer matrix composites. The underlying physical mechanisms and properties may be determined by parameter estimation via nonlinear regression. The nonlinear regression analysis formulation, including the underlying models, is discussed. Several models and associated data analyses are given along with the assumptions implicit in the underlying model. The results are anomalous. These anomalous results are evaluated with respect to the accuracy of the implicit assumptions.
ERIC Educational Resources Information Center
Carter, Richard D.; And Others
The use of canonical analysis and multiple discriminant analysis to analyze equity-parity in colleges and universities is assessed and distinguished from multiple regression analysis. Multiple regression analysis forces the variable weights throughout the salary structure to be uniform, permits only one criterion or dependent variable to be…
NASA Astrophysics Data System (ADS)
Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya
2013-03-01
This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.
Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis
NASA Astrophysics Data System (ADS)
Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae
The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916
Greensmith, David J
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow.
NASA Technical Reports Server (NTRS)
Rummler, D. R.
1976-01-01
The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.
The stochastic regression analysis as a tool in ecotoxicological QSAR studies.
Devillers, J; Zakarya, D; Chastrette, M; Doré, J C
1989-12-01
Correspondence factor analysis (CFA) was used in conjunction with linear regression analysis to examine the structure-activity relationships of 50 benzene derivatives tested on Pimephales promelas. From nine molecular descriptions (numbers of C, H, O, N, Br, Cl, NO2, OH, and NH2 included in the molecules), CFA made it possible to define five new independent variables which were introduced in a stepwise regression analysis procedure to describe the acute toxicity (96-h LC50) of the aromatic compounds. The model log 1/C = -0.727F1 + 1.248F3 + 4.052 (r = 0.918; s = 0.270) is more relevant to describe the ecotoxicological behavior of the studied compounds on the fathead minnow than that obtained with principal components (log 1/C = 0.151 PC1 -0.271 PC2 + 4.124; r = 0.737; s = 0.460). The heuristic potency of this particular statistical analysis, which is called stochastic regression analysis, is discussed in detail.
Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.
Hu, Yi-Chung
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.
The estimation of Aerosol Optical Depth in eastern China based on regression analysis
NASA Astrophysics Data System (ADS)
Wang, Jing; Shi, Runhe; Liu, Chaoshun; Zhou, Cong
2015-09-01
The atmospheric pollution and air quality issues are getting worse in China, the formation mechanism of aerosols and their environment effects attracted more and more attention. Aerosol Optical Depth (AOD) is one of the most important parameters which can indicate the atmospheric turbidity and aerosol load. High-quality AOD data are significant for the study in the atmospheric environment (i.e., air quality). This paper used MODIS/Terra AOD in 2008 to improve the coverage of MODIS/Aqua AOD, which was based on linear regression analysis model. RMSE between estimation value and AquaAOD detected through satellite is 0.132. The average value of test data was 0.812. The average of regression result was 0.807. It showed that the regression model between AODTerra and AODAqua worked well. Also, we built two sets of estimation models (MODIS AOD and OMI AOD) through stepwise regression analysis model. One is using OMI AOD and meteorological elements to estimate MODIS AOD. The value of RMSE was 0.113, which represents 13.916% of the average(R2=0.782). The other one is using MODIS AOD and meteorological elements to estimate OMI AOD. RMSE of the model is 0.132, which represents 18.182% of the average (R2=0.726).
Prasanna, S; Manivannan, E; Chaturvedi, S C
2005-04-15
As a part of our continuing efforts in discerning the structural and physicochemical requirements for selective COX-2 over COX-1 inhibition among the fused pyrazole ring systems, herein we report the QSAR analyses of the title compounds. The conformational flexibility of the title compounds was examined using a simple connection table representation. The conformational investigation was aided by calculating a connection table parameter called fraction of rotable bonds, b_rotR encompassing the number of rotable bonds and b_count, the number of bonds including implicit hydrogens of each ligand. The hydrophobic and steric correlation of the title compounds towards selective COX-2 inhibition was reported previously in one of our recent publications. In this communication, we attempt to calculate Wang-Ford charges of the non-hydrogen common atoms of AM1 optimized geometries of the title compounds. Owing to the partial conformational flexibility of title compounds, conformationally restricted and unrestricted descriptors were calculated from MOE. Correlation analysis of these 2D, 3D and Wang-Ford charges was accomplished by linear regression analysis. 2D molecular descriptor b_single, 3D molecular descriptors glob, std_dim3 showed significant contribution towards COX-2 inhibitory activity. Balaban J, a connectivity topological index showed a negative and positive contribution towards COX-1 and selective COX-2 over COX-1 inhibition, respectively. Wang-Ford charges calculated on C(7) showed a significant contribution towards COX-1 inhibitory activity whereas charges calculated on C(8) were crucial in governing the selectivity of COX-2 over COX-1 inhibition among these congeners.
Dimitriu, G; Poiata, Antonia; Tuchiluş, Cristina; Buiuc, D
2006-01-01
Linezolid is a new synthetic antibiotic belonging to the oxazolidinone class, available for the therapy of gram-positive infections, caused by methicillin-resistant staphylococci, vancomycin-resistant enterococci and penicillin-resistant pneumococci. The aim of the study was to determine the in vitro activity of linezolid against staphylococci strains and also to determine the relationship between the minimum inhibitory concentration (MIC) and inhibition zone diameter by calculating the regression analysis. We tested one hundred S. aureus isolates, obtained from healthy persons (naso-pharyngeal swabs) during 2005 year. The antibiotic susceptibility of strains was determined by disk diffusion standardized method and by agar dilution method using a multipoint inoculator. The relationship between the diameter of the inhibition zone produced by a linezolid disc impregnated with a fixed amount (30 eg) was determined by regression performed with the least squares method, considering the log2 of the minimum inhibitory concentrations (MICs) as the independent variable and the zone diameter as the dependent variable. The MIC values expressed in logarithmic form are plotted against inhibition zone diameter (arithmetic scale) of the same strain. The activity of linezolid against staphylococci was very good, with MIC 90 of 1 mg/l. All strains were fully sensitive. The regression line for linezolid passes through a continuous series of points that all are approximately located on the a straight line. For each of the MIC values the differences result no greater than 23 mm in diameter sizes were registered. Regression equation was y= -0.188x + 8.048. In conclusion, the regression line analysis calculated for linezolid, demonstrates a significant correlation between MIC values and the inhibition zone diameters obtained by a 30 mg disc.
ERIC Educational Resources Information Center
Serdahl, Eric
The information that is gained through various analyses of the residual scores yielded by the least squares regression model is explored. In fact, the most widely used methods for detecting data that do not fit this model are based on an analysis of residual scores. First, graphical methods of residual analysis are discussed, followed by a review…
Gao, Jun; Johnston, Grace M; Lavergne, M Ruth; McIntyre, Paul
2011-01-01
Classification and regression tree (CART) analysis was used to identify subpopulations with lower palliative care program (PCP) enrolment rates. CART analysis uses recursive partitioning to group predictors. The PCP enrolment rate was 72 percent for the 6,892 adults who died of cancer from 2000 and 2005 in two counties in Nova Scotia, Canada. The lowest PCP enrolment rates were for nursing home residents over 82 years (27 percent), a group residing more than 43 kilometres from the PCP (31 percent), and another group living less than two weeks after their cancer diagnosis (37 percent). The highest rate (86 percent) was for the 2,118 persons who received palliative radiation. Findings from multiple logistic regression (MLR) were provided for comparison. CART findings identified low PCP enrolment subpopulations that were defined by interactions among demographic, social, medical, and health system predictors.
Forecasting municipal solid waste generation using prognostic tools and regression analysis.
Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria
2016-11-01
For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction.
Air Leakage of US Homes: Regression Analysis and Improvements from Retrofit
Chan, Wanyu R.; Joh, Jeffrey; Sherman, Max H.
2012-08-01
LBNL Residential Diagnostics Database (ResDB) contains blower door measurements and other diagnostic test results of homes in United States. Of these, approximately 134,000 single-family detached homes have sufficient information for the analysis of air leakage in relation to a number of housing characteristics. We performed regression analysis to consider the correlation between normalized leakage and a number of explanatory variables: IECC climate zone, floor area, height, year built, foundation type, duct location, and other characteristics. The regression model explains 68% of the observed variability in normalized leakage. ResDB also contains the before and after retrofit air leakage measurements of approximately 23,000 homes that participated in weatherization assistant programs (WAPs) or residential energy efficiency programs. The two types of programs achieve rather similar reductions in normalized leakage: 30% for WAPs and 20% for other energy programs.
Inhibition of cyclooxygenase (COX)-2 affects endothelial progenitor cell proliferation
Colleselli, Daniela; Bijuklic, Klaudija; Mosheimer, Birgit A.; Kaehler, Christian M. . E-mail: C.M.Kaehler@uibk.ac.at
2006-09-10
Growing evidence indicates that inducible cyclooxygenase-2 (COX-2) is involved in the pathogenesis of inflammatory disorders and various types of cancer. Endothelial progenitor cells recruited from the bone marrow have been shown to be involved in the formation of new vessels in malignancies and discussed for being a key point in tumour progression and metastasis. However, until now, nothing is known about an interaction between COX and endothelial progenitor cells (EPC). Expression of COX-1 and COX-2 was detected by semiquantitative RT-PCR and Western blot. Proliferation kinetics, cell cycle distribution and rate of apoptosis were analysed by MTT test and FACS analysis. Further analyses revealed an implication of Akt phosphorylation and caspase-3 activation. Both COX-1 and COX-2 expression can be found in bone-marrow-derived endothelial progenitor cells in vitro. COX-2 inhibition leads to a significant reduction in proliferation of endothelial progenitor cells by an increase in apoptosis and cell cycle arrest. COX-2 inhibition leads further to an increased cleavage of caspase-3 protein and inversely to inhibition of Akt activation. Highly proliferating endothelial progenitor cells can be targeted by selective COX-2 inhibition in vitro. These results indicate that upcoming therapy strategies in cancer patients targeting COX-2 may be effective in inhibiting tumour vasculogenesis as well as angiogenic processes.
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.
Non-Stationary Hydrologic Frequency Analysis using B-Splines Quantile Regression
NASA Astrophysics Data System (ADS)
Nasri, B.; St-Hilaire, A.; Bouezmarni, T.; Ouarda, T.
2015-12-01
Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic structures and water resources system under the assumption of stationarity. However, with increasing evidence of changing climate, it is possible that the assumption of stationarity would no longer be valid and the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extreme flows based on B-Splines quantile regression, which allows to model non-stationary data that have a dependence on covariates. Such covariates may have linear or nonlinear dependence. A Markov Chain Monte Carlo (MCMC) algorithm is used to estimate quantiles and their posterior distributions. A coefficient of determination for quantiles regression is proposed to evaluate the estimation of the proposed model for each quantile level. The method is applied on annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in these variables and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for annual maximum and minimum discharge with high annual non-exceedance probabilities. Keywords: Quantile regression, B-Splines functions, MCMC, Streamflow, Climate indices, non-stationarity.
Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models.
Shieh, Gwowen
2009-01-01
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference procedures of the squared multiple correlation coefficient have been extensively developed. In contrast, a full range of statistical methods for the analysis of the squared cross-validity coefficient is considerably far from complete. This article considers a distinct expression for the definition of the squared cross-validity coefficient as the direct connection and monotone transformation to the squared multiple correlation coefficient. Therefore, all the currently available exact methods for interval estimation, power calculation, and sample size determination of the squared multiple correlation coefficient are naturally modified and extended to the analysis of the squared cross-validity coefficient. The adequacies of the existing approximate procedures and the suggested exact method are evaluated through a Monte Carlo study. Furthermore, practical applications in areas of psychology and management are presented to illustrate the essential features of the proposed methodologies. The first empirical example uses 6 control variables related to driver characteristics and traffic congestion and their relation to stress in bus drivers, and the second example relates skills, cognitive performance, and personality to team performance measures. The results in this article can facilitate the recommended practice of cross-validation in psychological and other areas of social science research.
Ruman, M; Olkowska, E; Kozioł, K; Absalon, D; Matysik, M; Polkowska, Ż
2014-03-01
Monitoring contamination in river water is an expensive procedure, particularly for developing countries where pollution is a significant problem. This study was conducted to provide a pollution monitoring strategy that reduces the cost of laboratory analysis. The new monitoring strategy was designed as a result of cluster and regression analysis on field data collected from an industrially influenced river. Pollution sources in the study site were coal mining, metallurgy, chemical industry, and metropolitan sewage. This river resembles those in other areas of the world, including developing countries where environmental monitoring is financially constrained. Data were collected on variability of contaminant concentrations during four seasons at the same points on tributaries of the river. The variables described in the study are pH, electrical conductivity, inorganic ions, trace elements, and selected organic pollutants. These variables were divided into groups using cluster analysis. These groups were then tested using regression models to identify how the behavior of one variable changes in relation to another. It was found that up to 86.8% of variability of one parameter could be determined by another in the dataset. We adopted 60, 65, and 70% determination levels () for accepting a regression model. As a result, monitoring could be reduced by 15 (60% level) and 10 variables (65 and 70%) out of 43, which comprises 35 and 23% of the monitored variable total. Cost reduction would be most effective if trace elements or organic pollutants were excluded from monitoring because these are the constituents most expensive to analyze.
Gulbransen, Dana J; McGlathery, Karen J; Marklund, Maria; Norris, James N; Gurgel, Carlos Frederico D
2012-10-01
Gracilaria vermiculophylla (Ohmi) Papenfuss is an invasive alga that is native to Southeast Asia and has invaded many estuaries in North America and Europe. It is difficult to differentiate G. vermiculophylla from native forms using morphology and therefore molecular techniques are needed. In this study, we used three molecular markers (rbcL, cox2-cox3 spacer, cox1) to identify G. vermiculophylla at several locations in the western Atlantic. RbcL and cox2-cox3 spacer markers confirmed the presence of G. vermiculophylla on the east coast of the USA from Massachusetts to South Carolina. We used a 507 base pair region of cox1 mtDNA to (i) verify the widespread distribution of G. vermiculophylla in the Virginia (VA) coastal bays and (ii) determine the intraspecific diversity of these algae. Cox1 haplotype richness in the VA coastal bays was much higher than that previously found in other invaded locations, as well as some native locations. This difference is likely attributed to the more intensive sampling design used in this study, which was able to detect richness created by multiple, diverse introductions. On the basis of our results, we recommend that future studies take differences in sampling design into account when comparing haplotype richness and diversity between native and non-native studies in the literature.
Yao, Yan; Wang, Chang-yue; Liu, Hui-jun; Tang, Jian-bin; Cai, Jin-hui; Wang, Jing-jun
2015-07-01
Forest bio-fuel, a new type renewable energy, has attracted increasing attention as a promising alternative. In this study, a new method called Sparse Partial Least Squares Regression (SPLS) is used to construct the proximate analysis model to analyze the fuel characteristics of sawdust combining Near Infrared Spectrum Technique. Moisture, Ash, Volatile and Fixed Carbon percentage of 80 samples have been measured by traditional proximate analysis. Spectroscopic data were collected by Nicolet NIR spectrometer. After being filtered by wavelet transform, all of the samples are divided into training set and validation set according to sample category and producing area. SPLS, Principle Component Regression (PCR), Partial Least Squares Regression (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO) are presented to construct prediction model. The result advocated that SPLS can select grouped wavelengths and improve the prediction performance. The absorption peaks of the Moisture is covered in the selected wavelengths, well other compositions have not been confirmed yet. In a word, SPLS can reduce the dimensionality of complex data sets and interpret the relationship between spectroscopic data and composition concentration, which will play an increasingly important role in the field of NIR application.
NASA Astrophysics Data System (ADS)
Dervilis, N.; Worden, K.; Cross, E. J.
2015-07-01
In the data-based approach to structural health monitoring (SHM), the absence of data from damaged structures in many cases forces a dependence on novelty detection as a means of diagnosis. Unfortunately, this means that benign variations in the operating or environmental conditions of the structure must be handled very carefully, lest they lead to false alarms. If novelty detection is implemented in terms of outlier detection, the outliers may arise in the data as the result of both benign and malign causes and it is important to understand their sources. Comparatively recent developments in the field of robust regression have the potential to provide ways of exploring and visualising SHM data as a means of shedding light on the different origins of outliers. The current paper will illustrate the use of robust regression for SHM data analysis through experimental data acquired from the Z24 and Tamar Bridges, although the methods are general and not restricted to SHM or civil infrastructure.
Cao, Han-Han; Du, Ruo-Fei; Yang, Jia-Ning; Feng, Yi
2014-03-01
In this paper, microcrystalline cellulose WJ101 was used as a model material to investigate the effect of various process parameters on granule yield and friability after dry granulation with a single factor and the effect of comprehensive inspection process parameters on the effect of granule yield and friability, then the correlation between process parameters and granule quality was established. The regress equation was established between process parameters and granule yield and friability by multiple regression analysis, the affecting the order of the size of the order of the process parameters on granule yield and friability was: rollers speed > rollers pressure > speed of horizontal feed. Granule yield was positively correlated with pressure and speed of horizontal feed and negatively correlated rollers speed, while friability was on the contrary. By comparison, fitted value and real value, fitted and real value are basically the same of no significant differences (P > 0.05) and with high precision and reliability.
Alados, C.L.; Pueyo, Y.; Giner, M.L.; Navarro, T.; Escos, J.; Barroso, F.; Cabezudo, B.; Emlen, J.M.
2003-01-01
We studied the effect of grazing on the degree of regression of successional vegetation dynamic in a semi-arid Mediterranean matorral. We quantified the spatial distribution patterns of the vegetation by fractal analyses, using the fractal information dimension and spatial autocorrelation measured by detrended fluctuation analyses (DFA). It is the first time that fractal analysis of plant spatial patterns has been used to characterize the regressive ecological succession. Plant spatial patterns were compared over a long-term grazing gradient (low, medium and heavy grazing pressure) and on ungrazed sites for two different plant communities: A middle dense matorral of Chamaerops and Periploca at Sabinar-Romeral and a middle dense matorral of Chamaerops, Rhamnus and Ulex at Requena-Montano. The two communities differed also in the microclimatic characteristics (sea oriented at the Sabinar-Romeral site and inland oriented at the Requena-Montano site). The information fractal dimension increased as we moved from a middle dense matorral to discontinuous and scattered matorral and, finally to the late regressive succession, at Stipa steppe stage. At this stage a drastic change in the fractal dimension revealed a change in the vegetation structure, accurately indicating end successional vegetation stages. Long-term correlation analysis (DFA) revealed that an increase in grazing pressure leads to unpredictability (randomness) in species distributions, a reduction in diversity, and an increase in cover of the regressive successional species, e.g. Stipa tenacissima L. These comparisons provide a quantitative characterization of the successional dynamic of plant spatial patterns in response to grazing perturbation gradient. ?? 2002 Elsevier Science B.V. All rights reserved.
ERIC Educational Resources Information Center
Johns, Stephanie
2010-01-01
Kathy Cox, the superintendent of schools for Georgia, believes "excellence is not an accident". She made a name for herself by winning $1 million proving she was smarter than a fifth-grader on a popular television show. This article presents a profile of Cox, her family, her role as school superintendent, and her accomplishments.…
Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T
2016-12-20
Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
A refined method for multivariate meta-analysis and meta-regression.
Jackson, Daniel; Riley, Richard D
2014-02-20
Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples.
Regression analysis of mixed recurrent-event and panel-count data with additive rate models.
Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L
2015-03-01
Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study.
An application of a microcomputer compiler program to multiple logistic regression analysis.
Sakai, R
1988-01-01
Microcomputer programs for multiple logistic regression analysis were written in BASIC language to determine the usefulness of microcomputers for multivariate analysis, which is an important method in epidemiological studies. The program, carried out by an interpreter system, required a comparatively long computing time for a small amount of data. For example, it took approximately thirty minutes to compute the data of 6 independent variables and 63 matched sets of case and controls (1:4). The majority of the calculation time was spent computing a matrix. The matrix computation time increased cumulatively in proportion to additions in the number of subjects, and increased exponentially with the number of variables. A BASIC compiler was utilized for the program of multiple logistic regression analysis. The compiled program carried out the same computations as above, but within 4 minutes. Therefore, it is evident that a compiler can be an extremely convenient tool for computing multivariate analysis. The two programs produced here were also easily linked with spreadsheet packages to enter data.
Spontaneous skin regression and predictors of skin regression in Thai scleroderma patients.
Foocharoen, Chingching; Mahakkanukrauh, Ajanee; Suwannaroj, Siraphop; Nanagara, Ratanavadee
2011-09-01
Skin tightness is a major clinical manifestation of systemic sclerosis (SSc). Importantly for both clinicians and patients, spontaneous regression of the fibrosis process has been documented. The purpose of this study is to identify the incidence and related clinical characteristics of spontaneous regression among Thai SSc patients. A historical cohort with 4 years of follow-up was performed among SSc patients over 15 years of age diagnosed with SSc between January 1, 2005 and December 31, 2006 in Khon Kaen, Thailand. The start date was the date of the first symptom and the end date was the date of the skin score ≤2. To estimate the respective probability of regression and to assess the associated factors, the Kaplan-Meier method and Cox regression analysis was used. One hundred seventeen cases of SSc were included with a female to male ratio of 1.5:1. Thirteen patients (11.1%) experienced regression. The incidence rate of spontaneous skin regression was 0.31 per 100 person-months and the average duration of SSc at the time of regression was 35.9±15.6 months (range, 15.7-60 months). The factors that negatively correlated with regression were (a) diffuse cutaneous type, (b) Raynaud's phenomenon, (c) esophageal dysmotility, and (d) colchicine treatment at onset with a respective hazard ratio (HR) of 0.19, 0.19, 0.26, and 0.20. By contrast, the factor that positively correlated with regression was active alveolitis with cyclophosphamide therapy at onset with an HR of 4.23 (95% CI, 1.23-14.10). After regression analysis, only Raynaud's phenomenon at onset and diffuse cutaneous type had a significantly negative correlation to regression. A spontaneous regression of the skin fibrosis process was not uncommon among Thai SSc patients. The factors suggesting a poor predictor for cutaneous manifestation were Raynaud's phenomenon, diffuse cutaneous type while early cyclophosphamide therapy might be related to a better skin outcome.
Chikae, Miyuki; Ikeda, Ryuzoh; Kerman, Kagan; Morita, Yasutaka; Tamiya, Eiichi
2006-11-01
The composting process of food wastes and tree cuttings was examined on four composting types composed from two kinds of systems and added mixture of microorganisms. The time courses of 32 parameters in each composting type were observed. The efficient composting system was found to be the static aerated reactor system in comparison with the turning pile one. Using the multiple regression analysis of all the data (159 samples) obtained from this study, some parameters were selected to predict the germination index (GI) value, which was adopted as a marker of compost maturity. For example, using the regression model generated from pH, NH(4)(+) concentration, acid phosphatase activity, and esterase activity of water extracts of the compost, GI value was expressed by the multi-linear regression equation (p<0.0001). High correlations between the measured GI value and the predicted one were made in each type of compost. As a result of these observations, the compost maturity might be predicted by only sensing of the water extract at the composting site without any requirements for a large-size equipment and skill, and this prediction system could contribute to the production of a stable compost in wide-spread use for the recycling market.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
Bayesian analysis of a multivariate null intercept errors-in-variables regression model.
Aoki, Reiko; Bolfarine, Heleno; Achcar, Jorge A; Dorival, Leão P Júnior
2003-11-01
Longitudinal data are of great interest in analysis of clinical trials. In many practical situations the covariate can not be measured precisely and a natural alternative model is the errors-in-variables regression models. In this paper we study a null intercept errors-in-variables regression model with a structure of dependency between the response variables within the same group. We apply the model to real data presented in Hadgu and Koch (Hadgu, A., Koch, G. (1999). Application of generalized estimating equations to a dental randomized clinical trial. J. Biopharmaceutical Statistics 9(1):161-178). In that study volunteers with preexisting dental plaque were randomized to two experimental mouth rinses (A and B) or a control mouth rinse with double blinding. The dental plaque index was measured for each subject in the beginning of the study and at two follow-up times, which leads to the presence of an interclass correlation. We propose the use of a Bayesian approach to model a multivariate null intercept errors-in-variables regression model to the longitudinal data. The proposed Bayesian approach accommodates the correlated measurements and incorporates the restriction that the slopes must lie in the (0, 1) interval. A Gibbs sampler is used to perform the computations.
Irrechukwu, Onyi N; Reiter, David A; Lin, Ping-Chang; Roque, Remigio A; Fishbein, Kenneth W; Spencer, Richard G
2012-06-01
Increased sensitivity in the characterization of cartilage matrix status by magnetic resonance (MR) imaging, through the identification of surrogate markers for tissue quality, would be of great use in the noninvasive evaluation of engineered cartilage. Recent advances in MR evaluation of cartilage include multiexponential and multiparametric analysis, which we now extend to engineered cartilage. We studied constructs which developed from chondrocytes seeded in collagen hydrogels. MR measurements of transverse relaxation times were performed on samples after 1, 2, 3, and 4 weeks of development. Corresponding biochemical measurements of sulfated glycosaminoglycan (sGAG) were also performed. sGAG per wet weight increased from 7.74±1.34 μg/mg in week 1 to 21.06±4.14 μg/mg in week 4. Using multiexponential T₂ analysis, we detected at least three distinct water compartments, with T₂ values and weight fractions of (45 ms, 3%), (200 ms, 4%), and (500 ms, 97%), respectively. These values are consistent with known properties of engineered cartilage and previous studies of native cartilage. Correlations between sGAG and MR measurements were examined using conventional univariate analysis with T₂ data from monoexponential fits with individual multiexponential compartment fractions and sums of these fractions, through multiple linear regression based on linear combinations of fractions, and, finally, with multivariate analysis using the support vector regression (SVR) formalism. The phenomenological relationship between T₂ from monoexponential fitting and sGAG exhibited a correlation coefficient of r²=0.56, comparable to the more physically motivated correlations between individual fractions or sums of fractions and sGAG; the correlation based on the sum of the two proteoglycan-associated fractions was r²=0.58. Correlations between measured sGAG and those calculated using standard linear regression were more modest, with r² in the range 0
Regression Analysis of Stage Variability for West-Central Florida Lakes
Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy
2008-01-01
The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground
NASA Astrophysics Data System (ADS)
Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei
2014-10-01
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
COX2 Inhibition Reduces Aortic Valve Calcification In Vivo
Wirrig, Elaine E.; Gomez, M. Victoria; Hinton, Robert B.; Yutzey, Katherine E.
2016-01-01
Objective Calcific aortic valve disease (CAVD) is a significant cause of morbidity and mortality, which affects approximately 1% of the US population and is characterized by calcific nodule formation and stenosis of the valve. Klotho-deficient mice were used to study the molecular mechanisms of CAVD as they develop robust aortic valve (AoV) calcification. Through microarray analysis of AoV tissues from klotho-deficient and wild type mice, increased expression of the gene encoding cyclooxygenase 2/COX2 (Ptgs2) was found. COX2 activity contributes to bone differentiation and homeostasis, thus the contribution of COX2 activity to AoV calcification was assessed. Approach and Results In klotho-deficient mice, COX2 expression is increased throughout regions of valve calcification and is induced in the valvular interstitial cells (VICs) prior to calcification formation. Similarly, COX2 expression is increased in human diseased AoVs. Treatment of cultured porcine aortic VICs with osteogenic media induces bone marker gene expression and calcification in vitro, which is blocked by inhibition of COX2 activity. In vivo, genetic loss of function of COX2 cyclooxygenase activity partially rescues AoV calcification in klotho-deficient mice. Moreover, pharmacologic inhibition of COX2 activity in klotho-deficient mice via celecoxib-containing diet reduces AoV calcification and blocks osteogenic gene expression. Conclusions COX2 expression is upregulated in CAVD and its activity contributes to osteogenic gene induction and valve calcification in vitro and in vivo. PMID:25722432
Spatial regression analysis on 32 years of total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-08-01
Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively
Choudhary, Jaipal S; Naaz, Naiyar; Prabhakar, Chandra S; Lemtur, Moanaro
2016-10-01
The study examined the genetic diversity and demographic history of Bactrocera dorsalis, a destructive and polyphagous insect pest of fruit crops in diverse geographic regions of India. 19 widely dispersed populations of the fly from India and other Asian countries were analysed using partial sequences of mitochondrial cytochrome oxidase I (cox1) and NADH dehydrogenase 1 (nad1) genes to investigate genetic diversity, genetic structure, and demographic history in the region. Genetic diversity indices [number of haplotypes (H), haloptype diversity (Hd), nucleotide diversity (π) and average number of nucleotide difference (k)] of populations revealed that B. dorsalis maintains fairly high level of genetic diversity without isolation by distance among the geographic regions. Demographic analysis showed significant (negative) Tajimas' D and Fu's F S with non significant sum of squared deviations (SSD) values, which indicate the possibility of recent sudden expansion of species and is further supported through distinctively star-like distribution structure of haplotypes among populations. Thus, the results indicate that both ongoing and historical factors have played important role in determining the genetic structure and diversity of the species in India. Consequently, sterile insect technique (SIT) could be a possible management strategy of species in the regions.
NASA Technical Reports Server (NTRS)
Waller, M. C.
1976-01-01
An electro-optical device called an oculometer which tracks a subject's lookpoint as a time function has been used to collect data in a real-time simulation study of instrument landing system (ILS) approaches. The data describing the scanning behavior of a pilot during the instrument approaches have been analyzed by use of a stepwise regression analysis technique. A statistically significant correlation between pilot workload, as indicated by pilot ratings, and scanning behavior has been established. In addition, it was demonstrated that parameters derived from the scanning behavior data can be combined in a mathematical equation to provide a good representation of pilot workload.
Regression models for the analysis of longitudinal Gaussian data from multiple sources.
O'Brien, Liam M; Fitzmaurice, Garrett M
2005-06-15
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.
Zlatarić, Dubravka Knezović; Celebić, Asja
2008-01-01
This study aimed to analyze factors related to patients' general satisfaction with removable partial dentures (RPDs), such as esthetics, retention, speech, chewing, and comfort. A total of 103 patients with Kennedy Class I RPDs (34 to 82 years old; mean age: 63; 35 men, 68 women) assessed their satisfaction with dentures. Stepwise multiple regression analysis was used to evaluate the relationship among the factors. Significant correlations were found between general satisfaction and each of the individual components (P < .05). The patients' assessment of esthetics explained almost 50% of general satisfaction in both arches (P < .05). Esthetics, chewing, and speech had significant effects on the patients' general satisfaction with dentures.
NASA Astrophysics Data System (ADS)
Mandal, Nilrudra; Doloi, Biswanath; Mondal, Biswanath
2016-01-01
In the present study, an attempt has been made to apply the Taguchi parameter design method and regression analysis for optimizing the cutting conditions on surface finish while machining AISI 4340 steel with the help of the newly developed yttria based Zirconia Toughened Alumina (ZTA) inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Experiments have been carried out based on an orthogonal array L9 with three parameters (cutting speed, depth of cut and feed rate) at three levels (low, medium and high). Based on the mean response and signal to noise ratio (SNR), the best optimal cutting condition has been arrived at A3B1C1 i.e. cutting speed is 420 m/min, depth of cut is 0.5 mm and feed rate is 0.12 m/min considering the condition smaller is the better approach. Analysis of Variance (ANOVA) is applied to find out the significance and percentage contribution of each parameter. The mathematical model of surface roughness has been developed using regression analysis as a function of the above mentioned independent variables. The predicted values from the developed model and experimental values are found to be very close to each other justifying the significance of the model. A confirmation run has been carried out with 95 % confidence level to verify the optimized result and the values obtained are within the prescribed limit.
Ziemssen, Tjalf; Reimann, Manja; Gasch, Julia; Rüdiger, Heinz
2013-09-01
Biological rhythms, describing the temporal variation of biological processes, are a characteristic feature of complex systems. The analysis of biological rhythms can provide important insights into the pathophysiology of different diseases, especially, in cardiovascular medicine. In the field of the autonomic nervous system, heart rate variability (HRV) and baroreflex sensitivity (BRS) describe important fluctuations of blood pressure and heart rate which are often analyzed by Fourier transformation. However, these parameters are stochastic with overlaying rhythmical structures. R-R intervals as independent variables of time are not equidistant. That is why the trigonometric regressive spectral (TRS) analysis--reviewed in this paper--was introduced, considering both the statistical and rhythmical features of such time series. The data segments required for TRS analysis can be as short as 20 s allowing for dynamic evaluation of heart rate and blood pressure interaction over longer periods. Beyond HRV, TRS also estimates BRS based on linear regression analyses of coherent heart rate and blood pressure oscillations. An additional advantage is that all oscillations are analyzed by the same (maximal) number of R-R intervals thereby providing a high number of individual BRS values. This ensures a high confidence level of BRS determination which, along with short recording periods, may be of profound clinical relevance. The dynamic assessment of heart rate and blood pressure spectra by TRS allows a more precise evaluation of cardiovascular modulation under different settings as has already been demonstrated in different clinical studies.
Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression
Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi
2016-01-01
The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73–0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer. PMID:26786590
Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression.
Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi
2016-01-20
The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73-0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer.
Jalal, Hawre; Goldhaber-Fiebert, Jeremy D.; Kuntz, Karen M.
2016-01-01
Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made [i.e., from value of information (VOI) analysis]. Unfortunately, VOI analysis remains underutilized due to the conceptual, mathematical and computational challenges of implementing Bayesian decision theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function – a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters. PMID:25840900
Bareth, Bettina; Dennerlein, Sven; Mick, David U; Nikolov, Miroslav; Urlaub, Henning; Rehling, Peter
2013-10-01
Cox1, the core subunit of the cytochrome c oxidase, receives two heme a cofactors during assembly of the 13-subunit enzyme complex. However, at which step of the assembly process and how heme is inserted into Cox1 have remained an enigma. Shy1, the yeast SURF1 homolog, has been implicated in heme transfer to Cox1, whereas the heme a synthase, Cox15, catalyzes the final step of heme a synthesis. Here we performed a comprehensive analysis of cytochrome c oxidase assembly intermediates containing Shy1. Our analyses suggest that Cox15 displays a role in cytochrome c oxidase assembly, which is independent of its functions as the heme a synthase. Cox15 forms protein complexes with Shy1 and also associates with Cox1-containing complexes independently of Shy1 function. These findings indicate that Shy1 does not serve as a mobile heme carrier between the heme a synthase and maturing Cox1 but rather cooperates with Cox15 for heme transfer and insertion in early assembly intermediates of cytochrome c oxidase.
NASA Astrophysics Data System (ADS)
Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.
2013-06-01
This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.
Automated particle identification through regression analysis of size, shape and colour
NASA Astrophysics Data System (ADS)
Rodriguez Luna, J. C.; Cooper, J. M.; Neale, S. L.
2016-04-01
Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to "predict" with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own.
Regression analysis of mixed panel count data with dependent terminal events.
Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L
2017-05-10
Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd.
NASA Astrophysics Data System (ADS)
Buck, J. A.; Underhill, P. R.; Morelli, J.; Krause, T. W.
2016-02-01
Nuclear steam generators (SGs) are a critical component for ensuring safe and efficient operation of a reactor. Life management strategies are implemented in which SG tubes are regularly inspected by conventional eddy current testing (ECT) and ultrasonic testing (UT) technologies to size flaws, and safe operating life of SGs is predicted based on growth models. ECT, the more commonly used technique, due to the rapidity with which full SG tube wall inspection can be performed, is challenged when inspecting ferromagnetic support structure materials in the presence of magnetite sludge and multiple overlapping degradation modes. In this work, an emerging inspection method, pulsed eddy current (PEC), is being investigated to address some of these particular inspection conditions. Time-domain signals were collected by an 8 coil array PEC probe in which ferromagnetic drilled support hole diameter, depth of rectangular tube frets and 2D tube off-centering were varied. Data sets were analyzed with a modified principal components analysis (MPCA) to extract dominant signal features. Multiple linear regression models were applied to MPCA scores to size hole diameter as well as size rectangular outer diameter tube frets. Models were improved through exploratory factor analysis, which was applied to MPCA scores to refine selection for regression models inputs by removing nonessential information.
Regression-based adaptive sparse polynomial dimensional decomposition for sensitivity analysis
NASA Astrophysics Data System (ADS)
Tang, Kunkun; Congedo, Pietro; Abgrall, Remi
2014-11-01
Polynomial dimensional decomposition (PDD) is employed in this work for global sensitivity analysis and uncertainty quantification of stochastic systems subject to a large number of random input variables. Due to the intimate structure between PDD and Analysis-of-Variance, PDD is able to provide simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to polynomial chaos (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of the standard method unaffordable for real engineering applications. In order to address this problem of curse of dimensionality, this work proposes a variance-based adaptive strategy aiming to build a cheap meta-model by sparse-PDD with PDD coefficients computed by regression. During this adaptive procedure, the model representation by PDD only contains few terms, so that the cost to resolve repeatedly the linear system of the least-square regression problem is negligible. The size of the final sparse-PDD representation is much smaller than the full PDD, since only significant terms are eventually retained. Consequently, a much less number of calls to the deterministic model is required to compute the final PDD coefficients.
Jiang, Mingfeng; Zhu, Lingyan; Wang, Yaming; Xia, Ling; Shou, Guofa; Liu, Feng; Crozier, Stuart
2011-03-21
Non-invasively reconstructing the transmembrane potentials (TMPs) from body surface potentials (BSPs) constitutes one form of the inverse ECG problem that can be treated as a regression problem with multi-inputs and multi-outputs, and which can be solved using the support vector regression (SVR) method. In developing an effective SVR model, feature extraction is an important task for pre-processing the original input data. This paper proposes the application of principal component analysis (PCA) and kernel principal component analysis (KPCA) to the SVR method for feature extraction. Also, the genetic algorithm and simplex optimization method is invoked to determine the hyper-parameters of the SVR. Based on the realistic heart-torso model, the equivalent double-layer source method is applied to generate the data set for training and testing the SVR model. The experimental results show that the SVR method with feature extraction (PCA-SVR and KPCA-SVR) can perform better than that without the extract feature extraction (single SVR) in terms of the reconstruction of the TMPs on epi- and endocardial surfaces. Moreover, compared with the PCA-SVR, the KPCA-SVR features good approximation and generalization ability when reconstructing the TMPs.
Poisson regression analysis of mortality among male workers at a thorium-processing plant
Liu, Zhiyuan; Lee, Tze-San; Kotek, T.J.
1991-12-31
Analyses of mortality among a cohort of 3119 male workers employed between 1915 and 1973 at a thorium-processing plant were updated to the end of 1982. Of the whole group, 761 men were deceased and 2161 men were still alive, while 197 men were lost to follow-up. A total of 250 deaths was added to the 511 deaths observed in the previous study. The standardized mortality ratio (SMR) for all causes of death was 1.12 with 95% confidence interval (CI) of 1.05-1.21. The SMRs were also significantly increased for all malignant neoplasms (SMR = 1.23, 95% CI = 1.04-1.43) and lung cancer (SMR = 1.36, 95% CI = 1.02-1.78). Poisson regression analysis was employed to evaluate the joint effects of job classification, duration of employment, time since first employment, age and year at first employment on mortality of all malignant neoplasms and lung cancer. A comparison of internal and external analyses with the Poisson regression model was also conducted and showed no obvious difference in fitting the data on lung cancer mortality of the thorium workers. The results of the multivariate analysis showed that there was no significant effect of all the study factors on mortality due to all malignant neoplasms and lung cancer. Therefore, further study is needed for the former thorium workers.
Regression Analysis for Complex Doping of X8R Ceramics Based on Uniform Design
NASA Astrophysics Data System (ADS)
Tang, Bin; Zhang, Shuren; Zhou, Xiaohua; Wang, Ding; Yuan, Ying
2007-10-01
Regression analysis based on uniform design was introduced as a new approach for designing BaTiO3-based X8R ceramics. The amounts of Nb2O5, Nd2O3, Zn0.8Mg0.2TiO3 (ZMT), and magnesium lithium borosilicate (MLBS) were the four investigated factors with respect to the dielectric constant at room temperature (ɛ) and temperature-capacitance characteristics (TCC) at 125°C (TCC125°C) and TCC150°C. Experiments were designed according to the uniform design with four factors for each at twelve levels. For each response, the second-order polynomial equations were obtained by multiple regression analysis. As a result, the empirical mathematical models could successfully predict the experimental results with very good accuracy. Finally, based on optimization strategy, we succeeded in producing lead-free X8R ceramics with various dielectric constants ranging from 1500 to 3300, which is promising for developing X8R MLCC with different capacities.
Fornetti, Jaime; Jindal, Sonali; Middleton, Kara A; Borges, Virginia F; Schedin, Pepper
2014-04-01
Cyclooxygenase-2 (COX-2) overexpression is implicated in increased risk and poorer outcomes in breast cancer in young women. We investigated COX-2 regulation in normal premenopausal breast tissue and its relationship to malignancy in young women. Quantitative COX-2 immunohistochemistry was performed on adjacent normal and breast cancer tissues from 96 premenopausal women with known clinical reproductive histories, and on rat mammary glands with distinct ovarian hormone exposures. COX-2 expression in the normal breast epithelium varied more than 40-fold between women and was associated with COX-2 expression levels in ductal carcinoma in situ and invasive cancer. Normal breast COX-2 expression was independent of known breast cancer prognostic indicators, including tumor stage and clinical subtype, indicating that factors regulating physiological COX-2 expression may be the primary drivers of COX-2 expression in breast cancer. Ovarian hormones, particularly at pregnancy levels, were identified as modulators of COX-2 in normal mammary epithelium. However, serial breast biopsy analysis in nonpregnant premenopausal women suggested relatively stable baseline levels of COX-2 expression, which persisted independent of menstrual cycling. These data provide impetus to investigate how baseline COX-2 expression is regulated in premenopausal breast tissue because COX-2 levels in normal breast epithelium may prove to be an indicator of breast cancer risk in young women, and predict the chemopreventive and therapeutic efficacy of COX-2 inhibitors in this population.
Analysis of changes in extreme temperature and precipitation using quantile regression
NASA Astrophysics Data System (ADS)
Lee, Kyoungmi; Baek, Hee-Jeong; Cho, ChunHo
2013-04-01
One of the important research areas in climatology is to identify whether the long-period tendencies of change in meteorological variables appear. In the past, the analysis has been limited by the estimation of long-period trends for annual or seasonal average values on meteorological variables. However, recently, the interest in the trends regarding the whole range of values for meteorological variables, including the extreme ones, has arisen. The quantile regression is the regression analysis method for estimating the regression slopes for the values of any quantile from 0 to 1 of dependent variable distributions. This method provides a more complete picture for the conditional distribution of the dependent variable given the independent variable when both lower and upper or all quantiles are of interest. This study examines the changes in regional extreme temperature and precipitation in South Korea using quantile regression, which is applied to analyze trends, not only in the mean but in all parts of the data distribution. The results show considerable diversity across space and quantile level in South Korea. For daily temperatures in winter, the slopes in lower quantiles generally have a more distinct increase trend compared to the upper quantiles. The time series for daily minimum temperature during the winter season only shows a significant increasing trend in the lower quantile. In case of summer, most sites show an increase trend in both lower and upper quantiles for daily minimum temperature, while there are a number of sites with a decrease trend for daily maximum temperature. It was also found that the increase trend of extreme low temperature in large urban areas (0.80°C/decade) is much larger than in rural areas (0.54°C/decade) due to the effects of urbanization. Extreme climate events can have greater negative impacts on society, economy and natural environments than changes in climate means. The fast growth of population and industrialization in
NASA Astrophysics Data System (ADS)
Junek, W. N.; Jones, W. L.; Woods, M. T.
2011-12-01
An automated event tree analysis system for estimating the probability of short term volcanic activity is presented. The algorithm is driven by a suite of empirical statistical models that are derived through logistic regression. Each model is constructed from a multidisciplinary dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. InSAR, seismic), source modeling results, and historic eruption activity. This provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. The algorithm is extensible and can be easily recalibrated to include new or additional monitoring, modeling, or historic information. Standard cross validation techniques are employed to optimize its forecasting capabilities. Analysis results from several recent volcanic unrest episodes are presented.
Regression analysis of overdispersed correlated count data with subject specific covariates.
Solis-Trapala, I L; Farewell, V T
2005-08-30
A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satisfies the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coefficients. In addition we extend the mean specification to include within- and between-cluster effects. The method is illustrated through the analysis of data from two studies. In the first study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the efficacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints.
Probabilistic partial least squares regression for quantitative analysis of Raman spectra.
Li, Shuo; Nyagilo, James O; Dave, Digant P; Wang, Wei; Zhang, Baoju; Gao, Jean
2015-01-01
With the latest development of Surface-Enhanced Raman Scattering (SERS) technique, quantitative analysis of Raman spectra has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Squares Regression (PLSR) is state-of-the-art method. But it only relies on training samples, which makes it difficult to incorporate complex domain knowledge. Based on probabilistic Principal Component Analysis (PCA) and probabilistic curve fitting idea, we propose a probabilistic PLSR (PPLSR) model and an Estimation Maximisation (EM) algorithm for estimating parameters. This model explains PLSR from a probabilistic viewpoint, describes its essential meaning and provides a foundation to develop future Bayesian nonparametrics models. Two real Raman spectra datasets were used to evaluate this model, and experimental results show its effectiveness.
JOINT STRUCTURE SELECTION AND ESTIMATION IN THE TIME-VARYING COEFFICIENT COX MODEL
Xiao, Wei; Lu, Wenbin; Zhang, Hao Helen
2016-01-01
Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group nonnegative garrote, we develop a new penalization approach to achieve such goals. Our method is able to identify the underlying true model structure with probability tending to one and simultaneously estimate the time-varying coefficients consistently. The asymptotic normalities of the resulting estimators are also established. We demonstrate the performance of our method using simulations and an application to the primary biliary cirrhosis data. PMID:27540275
P300 Amplitude in Alzheimer's Disease: A Meta-Analysis and Meta-Regression.
Hedges, Dawson; Janis, Rebecca; Mickelson, Stephen; Keith, Cierra; Bennett, David; Brown, Bruce L
2016-01-01
Alzheimer's disease accounts for 60% of all dementia. Numerous biomarkers have been developed that can help in making an early diagnosis. The P300 is an event-related potential that may be abnormal in Alzheimer's disease. Given the possible association between P300 amplitude and Alzheimer's disease and the need for biomarkers in early Alzheimer's disease, the main purpose of this meta-analysis and meta-regression was to characterize P300 amplitude in probable Alzheimer's disease compared to healthy controls. Using online search engines, we identified peer-reviewed articles containing amplitude measures for the P300 in response to a visual or auditory oddball stimulus in subjects with Alzheimer's disease and in a healthy control group and pooled effect sizes for differences in P300 amplitude between Alzheimer's disease and control groups to obtain summary effect sizes. We also used meta-regression to determine whether age, sex, educational attainment, or dementia severity affected the association between P300 amplitude and Alzheimer's disease. Twenty articles containing a total of 646 subjects met inclusion and exclusion criteria. The overall effect size from all electrode locations was 1.079 (95% confidence interval=0.745-1.412, P<.001). The pooled effect sizes for the Cz, Fz, and Pz locations were 1.226 (P<.001), 0.724 (P=.0007), and 1.430 (P<.001), respectively. Meta-regression showed an association between amplitude and educational attainment, but no association between amplitude and age, sex, and dementia severity. In conclusion, P300 amplitude is smaller in subjects with Alzheimer's disease than in healthy controls.
Fu, Yuan-Yuan; Wang, Ji-Hua; Yang, Gui-Jun; Song, Xiao-Yu; Xu, Xin-Gang; Feng, Hai-Kuan
2013-05-01
The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550-750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2 = 0.792, RMSE = 0.164 kg x m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.
A cautionary note on the use of EESC-based regression analysis for ozone trend studies
NASA Astrophysics Data System (ADS)
Kuttippurath, J.; Bodeker, G. E.; Roscoe, H. K.; Nair, P. J.
2015-01-01
Equivalent effective stratospheric chlorine (EESC) construct of ozone regression models attributes ozone changes to EESC changes using a single value of the sensitivity of ozone to EESC over the whole period. Using space-based total column ozone (TCO) measurements, and a synthetic TCO time series constructed such that EESC does not fall below its late 1990s maximum, we demonstrate that the EESC-based estimates of ozone changes in the polar regions (70-90°) after 2000 may, falsely, suggest an EESC-driven increase in ozone over this period. An EESC-based regression of our synthetic "failed Montreal Protocol with constant EESC" time series suggests a positive TCO trend that is statistically significantly different from zero over 2001-2012 when, in fact, no recovery has taken place. Our analysis demonstrates that caution needs to be exercised when using explanatory variables, with a single fit coefficient, fitted to the entire data record, to interpret changes in only part of the record.
Árnadóttir, Í.; Gíslason, M. K.; Carraro, U.
2016-01-01
Muscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration. PMID:28115982
Combining regression analysis and air quality modelling to predict benzene concentration levels
NASA Astrophysics Data System (ADS)
Vlachokostas, Ch.; Achillas, Ch.; Chourdakis, E.; Moussiopoulos, N.
2011-05-01
State of the art epidemiological research has found consistent associations between traffic-related air pollution and various outcomes, such as respiratory symptoms and premature mortality. However, many urban areas are characterised by the absence of the necessary monitoring infrastructure, especially for benzene (C 6H 6), which is a known human carcinogen. The use of environmental statistics combined with air quality modelling can be of vital importance in order to assess air quality levels of traffic-related pollutants in an urban area in the case where there are no available measurements. This paper aims at developing and presenting a reliable approach, in order to forecast C 6H 6 levels in urban environments, demonstrated for Thessaloniki, Greece. Multiple stepwise regression analysis is used and a strong statistical relationship is detected between C 6H 6 and CO. The adopted regression model is validated in order to depict its applicability and representativeness. The presented results demonstrate that the adopted approach is capable of capturing C 6H 6 concentration trends and should be considered as complementary to air quality monitoring.
An innovative land use regression model incorporating meteorology for exposure analysis.
Su, Jason G; Brauer, Michael; Ainslie, Bruce; Steyn, Douw; Larson, Timothy; Buzzelli, Michael
2008-02-15
The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.
Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, N.
2015-01-01
An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.
Montgomery, M E; White, M E; Martin, S W
1987-01-01
Results from discriminant analysis and logistic regression were compared using two data sets from a study on predictors of coliform mastitis in dairy cows. Both techniques selected the same set of variables as important predictors and were of nearly equal value in classifying cows as having, or not having mastitis. The logistic regression model made fewer classification errors. The magnitudes of the effects were considerably different for some variables. Given the failure to meet the underlying assumptions of discriminant analysis, the coefficients from logistic regression are preferable. PMID:3453271
Modeling age-of-onset: Cox model with latent major gene effects
Li, H.; Thompson, E.A.
1994-09-01
Analysis of age-of-onset is a key factor in the segregation and linkage analysis of complex genetic traits, but is complicated by the censoring of unaffected individuals. Most previous work has used parametric distributional assumptions, but it is hard to characterize the distribution of age-of-onset by a single distribution. Other approaches discretize age-of-onset and use logistic regression to model incidence; this approach does not use the information fully. Frailty models have been used for age-of-oset in the biostatistics literature, but these models do not lend themselves to modeling the correlations due to genetic effects which segregate within a family. Here, we propose use of the Cox model with latent major gene effects; conditional on the major genotypes, Cox`s proportional hazards model is used for age-of-onset for each individual. This is a semiparametric model; we do not specify the baseline hazard function. Likelihood analysis of such models is restricted by the difficulty in evaluating of maximizing the likelihood, especially when data are available for some of the members of an extended pedigree. Markov chain Monte Carlo permits genotypic configurations to be realized from the posterior distributions given a current model and the observed data. Hence methods for likelihood analysis can be developed: Monte Carlo EM is used for estimation of the parameters and their variance-covariance matrix. Markers and observed covariates are easily incorporated into this analysis. We present the model, methods for likelihood analysis and the results of a simulation study. The results are comparable with those based on a Cox model with known genotypic dependence in a pedigree. An early-onset Alzheimer`s pedigree and some breast cancer pedigrees have been used as real data examples. Some possible extensions are also discussed.
Melanin and blood concentration in human skin studied by multiple regression analysis: experiments.
Shimada, M; Yamada, Y; Itoh, M; Yatagai, T
2001-09-01
Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.
Lee, Yueh-Chiang; Sun, Ya Chung
2009-01-01
Even though use of the internet by adolescents has grown exponentially, little is known about the correlation between their interaction via Instant Messaging (IM) and the evolution of their interpersonal relationships in real life. In the present study, 369 junior high school students in Taiwan responded to questions regarding their IM usage and their dispositional measures of real-life interpersonal relationships. Descriptive statistics, factor analysis, and quantile regression methods were used to analyze the data. Results indicate that (1) IM helps define adolescents' self-identity (forming and maintaining individual friendships) and social-identity (belonging to a peer group), and (2) how development of an interpersonal relationship is impacted by the use of IM since it appears that adolescents use IM to improve their interpersonal relationships in real life.
NASA Astrophysics Data System (ADS)
Gad, R. S.; Parab, J. S.; Naik, G. M.
2010-11-01
Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.
Melanin and blood concentration in human skin studied by multiple regression analysis: experiments
NASA Astrophysics Data System (ADS)
Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.
2001-09-01
Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.
Tam, Vivian W Y; Wang, K; Tam, C M
2008-04-01
Recycled demolished concrete (DC) as recycled aggregate (RA) and recycled aggregate concrete (RAC) is generally suitable for most construction applications. Low-grade applications, including sub-base and roadwork, have been implemented in many countries; however, higher-grade activities are rarely considered. This paper examines relationships among DC characteristics, properties of their RA and strength of their RAC using regression analysis. Ten samples collected from demolition sites are examined. The results show strong correlation among the DC samples, properties of RA and RAC. It should be highlighted that inferior quality of DC will lower the quality of RA and thus their RAC. Prediction of RAC strength is also formulated from the DC characteristics and the RA properties. From that, the RAC performance from DC and RA can be estimated. In addition, RAC design requirements can also be developed at the initial stage of concrete demolition. Recommendations are also given to improve the future concreting practice.
Shen, Chung-Wei; Chen, Yi-Hau
2015-10-01
Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations.
Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.
Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W
2016-11-15
There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects.
Japanese elderly persons walk faster than non-Asian elderly persons: a meta-regression analysis
Ando, Masataka; Kamide, Naoto
2015-01-01
[Purpose] The purpose of this study was to clarify ethnic differences in walking speed by comparing walking speed in both Japanese and non-Asian elderly individuals and to investigate the necessity of consideration of ethnic differences in walking speed. [Subjects and Methods] Articles that reported comfortable walking speeds for community-dwelling elderly individuals were identified from electronic databases. Articles that involved community-dwelling individuals who were 60 years old or older and well functioning were included in the study. Articles that involved Asians were excluded. Weighted means for 5-m walking times were calculated as walking speeds from the Japanese and non-Asian sample data. The effects of age, gender, and ethnicity on 5-m walking times were then investigated using meta-regression analysis. [Results] Twenty studies (34 groups) were included for Japanese, and 16 studies (28 groups) were included for non-Asians. The weighted mean 5-m walking time was estimated to be 4.15 sec (95% confidence interval [CI]: 3.87–4.44) for Japanese and 4.24 sec (95% CI: 4.09–4.40) for non-Asians. Furthermore, using meta-regression analysis adjusted for age and gender, the 5-m walking time was 0.40 sec faster (95% CI: 0.03–0.77) for Japanese than for non-Asian elderly individuals. [Conclusion] Walking speed appeared faster for Japanese community-dwelling elderly individuals than for non-Asian elderly individuals. PMID:26696722
Menon, Prashanthi; Podolsky, Irina; Feig, Jonathan E.; Aderem, Alan; Fisher, Edward A.; Gold, Elizabeth S.
2014-01-01
We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy – leveraging macrophage epigenomic measurements – to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5′ regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5′ regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that β-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in
Bode, Manuela; Woellhaf, Michael W.; Bohnert, Maria; van der Laan, Martin; Sommer, Frederik; Jung, Martin; Zimmermann, Richard; Schroda, Michael; Herrmann, Johannes M.
2015-01-01
Members of the twin Cx9C protein family constitute the largest group of proteins in the intermembrane space (IMS) of mitochondria. Despite their conserved nature and their essential role in the biogenesis of the respiratory chain, the molecular function of twin Cx9C proteins is largely unknown. We performed a SILAC-based quantitative proteomic analysis to identify interaction partners of the conserved twin Cx9C protein Cox19. We found that Cox19 interacts in a dynamic manner with Cox11, a copper transfer protein that facilitates metalation of the Cu(B) center of subunit 1 of cytochrome c oxidase. The interaction with Cox11 is critical for the stable accumulation of Cox19 in mitochondria. Cox19 consists of a helical hairpin structure that forms a hydrophobic surface characterized by two highly conserved tyrosine-leucine dipeptides. These residues are essential for Cox19 function and its specific binding to a cysteine-containing sequence in Cox11. Our observations suggest that an oxidative modification of this cysteine residue of Cox11 stimulates Cox19 binding, pointing to a redox-regulated interplay of Cox19 and Cox11 that is critical for copper transfer in the IMS and thus for biogenesis of cytochrome c oxidase. PMID:25926683
Multivariate phenotype association analysis by marker-set kernel machine regression.
Maity, Arnab; Sullivan, Patrick F; Tzeng, Jun-Ying
2012-11-01
Genetic studies of complex diseases often collect multiple phenotypes relevant to the disorders. As these phenotypes can be correlated and share common genetic mechanisms, jointly analyzing these traits may bring more power to detect genes influencing individual or multiple phenotypes. Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, we construct a multivariate regression based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. The kernel machine serves as a powerful dimension-reduction tool to capture complex effects among markers. The multivariate framework incorporates the potentially correlated multidimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes, including the multiple univariate kernel machine tests with original phenotypes or with their principal components. Our results suggest that none of these approaches has the uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger or when there exist genes that affect more than one phenotype. We illustrate the utility of the multivariate kernel machine method through the Clinical Antipsychotic Trails of Intervention Effectiveness antibody study.
NASA Technical Reports Server (NTRS)
Jolly, William H.
1992-01-01
Relationships defining the ballistic limit of Space Station Freedom's (SSF) dual wall protection systems have been determined. These functions were regressed from empirical data found in Marshall Space Flight Center's (MSFC) Hypervelocity Impact Testing Summary (HITS) for the velocity range between three and seven kilometers per second. A stepwise linear least squares regression was used to determine the coefficients of several expressions that define a ballistic limit surface. Using statistical significance indicators and graphical comparisons to other limit curves, a final set of expressions is recommended for potential use in Probability of No Critical Flaw (PNCF) calculations for Space Station. The three equations listed below represent the mean curves for normal, 45 degree, and 65 degree obliquity ballistic limits, respectively, for a dual wall protection system consisting of a thin 6061-T6 aluminum bumper spaced 4.0 inches from a .125 inches thick 2219-T87 rear wall with multiple layer thermal insulation installed between the two walls. Normal obliquity is d(sub c) = 1.0514 v(exp 0.2983 t(sub 1)(exp 0.5228). Forty-five degree obliquity is d(sub c) = 0.8591 v(exp 0.0428) t(sub 1)(exp 0.2063). Sixty-five degree obliquity is d(sub c) = 0.2824 v(exp 0.1986) t(sub 1)(exp -0.3874). Plots of these curves are provided. A sensitivity study on the effects of using these new equations in the probability of no critical flaw analysis indicated a negligible increase in the performance of the dual wall protection system for SSF over the current baseline. The magnitude of the increase was 0.17 percent over 25 years on the MB-7 configuration run with the Bumper II program code.
Andrianov, B V; Goryacheva, I I; Vlasov, S V; Gorelova, T V; Harutyunova, M V; Harutyunova, K V; Mayilyan, K R; Zakharov, I A
2015-03-01
Black flies (Diptera, Simuliidae) are well known for their medical, environmental, and veterinary importance. The simuliid fauna of Armenia includes 53 species. A number of dominant species are of ecological importance. Complex analysis, which involved morphometric, cytogenetic, and molecular genetic approaches, was conducted to characterize the species status of black flies inhabiting the territory of Armenia. It was shown that the predominant simuliid species, Simulium paraequinum and Simulium kiritshenkoi, belong to a group of species with minimal variability of the cox1 gene. The recently discovered species, Simulium noellery and Simulium [B.] erythrocephalum, which are new to Armenia, can be considered as potentially invasive, which is supported by the low level of variability of the cox1 gene.
The value of a statistical life: a meta-analysis with a mixed effects regression model.
Bellavance, François; Dionne, Georges; Lebeau, Martin
2009-03-01
The value of a statistical life (VSL) is a very controversial topic, but one which is essential to the optimization of governmental decisions. We see a great variability in the values obtained from different studies. The source of this variability needs to be understood, in order to offer public decision-makers better guidance in choosing a value and to set clearer guidelines for future research on the topic. This article presents a meta-analysis based on 39 observations obtained from 37 studies (from nine different countries) which all use a hedonic wage method to calculate the VSL. Our meta-analysis is innovative in that it is the first to use the mixed effects regression model [Raudenbush, S.W., 1994. Random effects models. In: Cooper, H., Hedges, L.V. (Eds.), The Handbook of Research Synthesis. Russel Sage Foundation, New York] to analyze studies on the value of a statistical life. We conclude that the variability found in the values studied stems in large part from differences in methodologies.
Machine learning of swimming data via wisdom of crowd and regression analysis.
Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing
2017-04-01
Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.
NASA Astrophysics Data System (ADS)
Rajab, Jasim Mohammed; Jafri, Mohd. Zubir Mat; Lim, Hwee San; Abdullah, Khiruddin
2012-10-01
This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2O) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R=0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R=0.845 to 0.918), indicating the model's efficiency and accuracy. Statistical analysis in terms of β showed that H2O (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.
Coelho, Lúcia H G; Gutz, Ivano G R
2006-03-15
A chemometric method for analysis of conductometric titration data was introduced to extend its applicability to lower concentrations and more complex acid-base systems. Auxiliary pH measurements were made during the titration to assist the calculation of the distribution of protonable species on base of known or guessed equilibrium constants. Conductivity values of each ionized or ionizable species possibly present in the sample were introduced in a general equation where the only unknown parameters were the total concentrations of (conjugated) bases and of strong electrolytes not involved in acid-base equilibria. All these concentrations were adjusted by a multiparametric nonlinear regression (NLR) method, based on the Levenberg-Marquardt algorithm. This first conductometric titration method with NLR analysis (CT-NLR) was successfully applied to simulated conductometric titration data and to synthetic samples with multiple components at concentrations as low as those found in rainwater (approximately 10 micromol L(-1)). It was possible to resolve and quantify mixtures containing a strong acid, formic acid, acetic acid, ammonium ion, bicarbonate and inert electrolyte with accuracy of 5% or better.
A regressive model analysis of congenital sensorineural deafness in German Dalmatian dogs.
Juraschko, Kathrin; Meyer-Lindenberg, Andrea; Nolte, Ingo; Distl, Ottmar
2003-08-01
The objective of the present study was to analyze the mode of inheritance for congenital sensorineural deafness (CSD) in German Dalmatian dogs by consideration of association between phenotypic breed characteristics and CSD. Segregation analysis with regressive logistic models was employed to test for different mechanisms of genetic transmission. Data were obtained from all three Dalmatian kennel clubs associated with the German Association for Dog Breeding and Husbandry (VDH). CSD was tested by veterinary practitioners using standardized protocols for Brainstem Auditory-Evoked Response (BAER). The sample included 1899 Dalmatian dogs from 354 litters in 169 different kennels. BAER testing results were from the years 1986 to 1999. Pedigree information was available for up to seven generations. The segregation analysis showed that a mixed monogenic-polygenic model including eye color as covariate among all other tested models best explained the segregation of affected animals in the pedigrees. The recessive major gene segregated in dogs with blue and brown eye color as well as in dogs with and without pigmented coat patches. Models which took into account the occurrence of patches, percentage of puppies tested per litter, or inbreeding coefficient gave no better adjustment to the most general (saturated) model. A procedure for the simultaneous prediction of breeding values and the estimation of genotype probabilities for CSD is expected to improve breeding programs significantly.
Generalized multilevel function-on-scalar regression and principal component analysis.
Goldsmith, Jeff; Zipunnikov, Vadim; Schrack, Jennifer
2015-06-01
This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly 600 subjects over 5 days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Thus, functional fixed effects are estimated while accounting for within-function and within-subject correlations, and major directions of variability within and between subjects are identified. Fixed effect coefficient functions and principal component basis functions are estimated using penalized splines; model parameters are estimated in a Bayesian framework using Stan, a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a 24-hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects.
High-dimensional Cox models: the choice of penalty as part of the model building process.
Benner, Axel; Zucknick, Manuela; Hielscher, Thomas; Ittrich, Carina; Mansmann, Ulrich
2010-02-01
The Cox proportional hazards regression model is the most popular approach to model covariate information for survival times. In this context, the development of high-dimensional models where the number of covariates is much larger than the number of observations (p>n) is an ongoing challenge. A practicable approach is to use ridge penalized Cox regression in such situations. Beside focussing on finding the best prediction rule, one is often interested in determining a subset of covariates that are the most important ones for prognosis. This could be a gene set in the biostatistical analysis of microarray data. Covariate selection can then, for example, be done by L(1)-penalized Cox regression using the lasso (Tibshirani (1997). Statistics in Medicine 16, 385-395). Several approaches beyond the lasso, that incorporate covariate selection, have been developed in recent years. This includes modifications of the lasso as well as nonconvex variants such as smoothly clipped absolute deviation (SCAD) (Fan and Li (2001). Journal of the American Statistical Association 96, 1348-1360; Fan and Li (2002). The Annals of Statistics 30, 74-99). The purpose of this article is to implement them practically into the model building process when analyzing high-dimensional data with the Cox proportional hazards model. To evaluate penalized regression models beyond the lasso, we included SCAD variants and the adaptive lasso (Zou (2006). Journal of the American Statistical Association 101, 1418-1429). We compare them with "standard" applications such as ridge regression, the lasso, and the elastic net. Predictive accuracy, features of variable selection, and estimation bias will be studied to assess the practical use of these methods. We observed that the performance of SCAD and adaptive lasso is highly dependent on nontrivial preselection procedures. A practical solution to this problem does not yet exist. Since there is high risk of missing relevant covariates when using SCAD or
VanEngelsdorp, Dennis; Speybroeck, Niko; Evans, Jay D; Nguyen, Bach Kim; Mullin, Chris; Frazier, Maryann; Frazier, Jim; Cox-Foster, Diana; Chen, Yanping; Tarpy, David R; Haubruge, Eric; Pettis, Jeffrey S; Saegerman, Claude
2010-10-01
Colony collapse disorder (CCD), a syndrome whose defining trait is the rapid loss of adult worker honey bees, Apis mellifera L., is thought to be responsible for a minority of the large overwintering losses experienced by U.S. beekeepers since the winter 2006-2007. Using the same data set developed to perform a monofactorial analysis (PloS ONE 4: e6481, 2009), we conducted a classification and regression tree (CART) analysis in an attempt to better understand the relative importance and interrelations among different risk variables in explaining CCD. Fifty-five exploratory variables were used to construct two CART models: one model with and one model without a cost of misclassifying a CCD-diagnosed colony as a non-CCD colony. The resulting model tree that permitted for misclassification had a sensitivity and specificity of 85 and 74%, respectively. Although factors measuring colony stress (e.g., adult bee physiological measures, such as fluctuating asymmetry or mass of head) were important discriminating values, six of the 19 variables having the greatest discriminatory value were pesticide levels in different hive matrices. Notably, coumaphos levels in brood (a miticide commonly used by beekeepers) had the highest discriminatory value and were highest in control (healthy) colonies. Our CART analysis provides evidence that CCD is probably the result of several factors acting in concert, making afflicted colonies more susceptible to disease. This analysis highlights several areas that warrant further attention, including the effect of sublethal pesticide exposure on pathogen prevalence and the role of variability in bee tolerance to pesticides on colony survivorship.
The Arabidopsis COX11 Homolog is Essential for Cytochrome c Oxidase Activity.
Radin, Ivan; Mansilla, Natanael; Rödel, Gerhard; Steinebrunner, Iris
2015-01-01
Members of the ubiquitous COX11 (cytochrome c oxidase 11) protein family are involved in copper delivery to the COX complex. In this work, we characterize the Arabidopsis thaliana COX11 homolog (encoded by locus At1g02410). Western blot analyses and confocal microscopy identified Arabidopsis COX11 as an integral mitochondrial protein. Despite sharing high sequence and structural similarities, the Arabidopsis COX11 is not able to functionally replace the Saccharomyces cerevisiae COX11 homolog. Nevertheless, further analysis confirmed the hypothesis that Arabidopsis COX11 is essential for COX activity. Disturbance of COX11 expression through knockdown (KD) or overexpression (OE) affected COX activity. In KD lines, the activity was reduced by ~50%, resulting in root growth inhibition, smaller rosettes and leaf curling. In OE lines, the reduction was less pronounced (~80% of the wild type), still resulting in root growth inhibition. Additionally, pollen germination was impaired in COX11 KD and OE plants. This effect on pollen germination can only partially be attributed to COX deficiency and may indicate a possible auxiliary role of COX11 in ROS metabolism. In agreement with its role in energy production, the COX11 promoter is highly active in cells and tissues with high-energy demand for example shoot and root meristems, or vascular tissues of source and sink organs. In COX11 KD lines, the expression of the plasma-membrane copper transporter COPT2 and of several copper chaperones was altered, indicative of a retrograde signaling pathway pertinent to copper homeostasis. Based on our data, we postulate that COX11 is a mitochondrial chaperone, which plays an important role for plant growth and pollen germination as an essential COX complex assembly factor.
The Arabidopsis COX11 Homolog is Essential for Cytochrome c Oxidase Activity
Radin, Ivan; Mansilla, Natanael; Rödel, Gerhard; Steinebrunner, Iris
2015-01-01
Members of the ubiquitous COX11 (cytochrome c oxidase 11) protein family are involved in copper delivery to the COX complex. In this work, we characterize the Arabidopsis thaliana COX11 homolog (encoded by locus At1g02410). Western blot analyses and confocal microscopy identified Arabidopsis COX11 as an integral mitochondrial protein. Despite sharing high sequence and structural similarities, the Arabidopsis COX11 is not able to functionally replace the Saccharomyces cerevisiae COX11 homolog. Nevertheless, further analysis confirmed the hypothesis that Arabidopsis COX11 is essential for COX activity. Disturbance of COX11 expression through knockdown (KD) or overexpression (OE) affected COX activity. In KD lines, the activity was reduced by ~50%, resulting in root growth inhibition, smaller rosettes and leaf curling. In OE lines, the reduction was less pronounced (~80% of the wild type), still resulting in root growth inhibition. Additionally, pollen germination was impaired in COX11 KD and OE plants. This effect on pollen germination can only partially be attributed to COX deficiency and may indicate a possible auxiliary role of COX11 in ROS metabolism. In agreement with its role in energy production, the COX11 promoter is highly active in cells and tissues with high-energy demand for example shoot and root meristems, or vascular tissues of source and sink organs. In COX11 KD lines, the expression of the plasma-membrane copper transporter COPT2 and of several copper chaperones was altered, indicative of a retrograde signaling pathway pertinent to copper homeostasis. Based on our data, we postulate that COX11 is a mitochondrial chaperone, which plays an important role for plant growth and pollen germination as an essential COX complex assembly factor. PMID:26734017
Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia
Li, Yue; Liang, Minggao; Zhang, Zhaolei
2014-01-01
Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML
ERIC Educational Resources Information Center
Hecht, Jeffrey B.
Previous research has demonstrated particular inadequacies in conventional methods used to identify outlier cases in bivariate regression models. Only through a combination of methods can one detect all of the deviant points potentially overinfluencing a regression model's parameters. This paper investigates whether a range of data points might…
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…
ERIC Educational Resources Information Center
Kaplan, David
2005-01-01
This article considers the problem of estimating dynamic linear regression models when the data are generated from finite mixture probability density function where the mixture components are characterized by different dynamic regression model parameters. Specifically, conventional linear models assume that the data are generated by a single…
COX-2 and PPAR-γ confer cannabidiol-induced apoptosis of human lung cancer cells.
Ramer, Robert; Heinemann, Katharina; Merkord, Jutta; Rohde, Helga; Salamon, Achim; Linnebacher, Michael; Hinz, Burkhard
2013-01-01
The antitumorigenic mechanism of cannabidiol is still controversial. This study investigates the role of COX-2 and PPAR-γ in cannabidiol's proapoptotic and tumor-regressive action. In lung cancer cell lines (A549, H460) and primary cells from a patient with lung cancer, cannabidiol elicited decreased viability associated with apoptosis. Apoptotic cell death by cannabidiol was suppressed by NS-398 (COX-2 inhibitor), GW9662 (PPAR-γ antagonist), and siRNA targeting COX-2 and PPAR-γ. Cannabidiol-induced apoptosis was paralleled by upregulation of COX-2 and PPAR-γ mRNA and protein expression with a maximum induction of COX-2 mRNA after 8 hours and continuous increases of PPAR-γ mRNA when compared with vehicle. In response to cannabidiol, tumor cell lines exhibited increased levels of COX-2-dependent prostaglandins (PG) among which PGD(2) and 15-deoxy-Δ(12,14)-PGJ(2) (15d-PGJ(2)) caused a translocation of PPAR-γ to the nucleus and induced a PPAR-γ-dependent apoptotic cell death. Moreover, in A549-xenografted nude mice, cannabidiol caused upregulation of COX-2 and PPAR-γ in tumor tissue and tumor regression that was reversible by GW9662. Together, our data show a novel proapoptotic mechanism of cannabidiol involving initial upregulation of COX-2 and PPAR-γ and a subsequent nuclear translocation of PPAR-γ by COX-2-dependent PGs.
Comparative analysis of regression and artificial neural network models for wind speed prediction
NASA Astrophysics Data System (ADS)
Bilgili, Mehmet; Sahin, Besir
2010-11-01
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.
Greensmith, David J.
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908
Karabatsos, George
2017-02-01
Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected
Wilson, Andrew J; Fadare, Oluwole; Beeghly-Fadiel, Alicia; Son, Deok-Soo; Liu, Qi; Zhao, Shilin; Saskowski, Jeanette; Uddin, Md Jashim; Daniel, Cristina; Crews, Brenda; Lehmann, Brian D; Pietenpol, Jennifer A; Crispens, Marta A; Marnett, Lawrence J; Khabele, Dineo
2015-08-28
Cyclooxygenase-1 (COX-1) is implicated in ovarian cancer. However, patterns of COX expression and function have been unclear and controversial. In this report, patterns of COX-1 and COX-2 gene expression were obtained from RNA-seq data through The Cancer Genome Atlas. Our analysis revealed markedly higher COX-1 mRNA expression than COX-2 in high-grade serous ovarian cancers (HGSOC) and higher COX-1 expression in HGSOC tumors than 10 other tumor types. High expression of COX-1 in HGSOC tumors was confirmed in an independent tissue microarray. In contrast, lower or similar expression of COX-1 compared to COX-2 was observed in endometrioid, mucinous and clear cell tumors. Stable COX-1 knockdown in HGSOC-representative OVCAR-3 ovarian cancer cells reduced gene expression in multiple pro-tumorigenic pathways. Functional cell viability, clonogenicity, and migration/invasion assays were consistent with transcriptomic changes. These effects were reversed by stable over-expression of COX-1 in SKOV-3 cells. Our results demonstrate a distinct pattern of COX-1 over-expression in HGSOC tumors and strong association of COX-1 with multiple pro-tumorigenic pathways in ovarian cancer cells. These findings provide additional insight into the role of COX-1 in human ovarian cancer and support further development of methods to selectively target COX-1 in the management of HGSOC tumors.
Wilson, Andrew J.; Fadare, Oluwole; Beeghly-Fadiel, Alicia; Son, Deok-Soo; Liu, Qi; Zhao, Shilin; Saskowski, Jeanette; Uddin, Md. Jashim; Daniel, Cristina; Crews, Brenda; Lehmann, Brian D.; Pietenpol, Jennifer A.; Crispens, Marta A.; Marnett, Lawrence J.; Khabele, Dineo
2015-01-01
Cyclooxygenase-1 (COX-1) is implicated in ovarian cancer. However, patterns of COX expression and function have been unclear and controversial. In this report, patterns of COX-1 and COX-2 gene expression were obtained from RNA-seq data through The Cancer Genome Atlas. Our analysis revealed markedly higher COX-1 mRNA expression than COX-2 in high-grade serous ovarian cancers (HGSOC) and higher COX-1 expression in HGSOC tumors than 10 other tumor types. High expression of COX-1 in HGSOC tumors was confirmed in an independent tissue microarray. In contrast, lower or similar expression of COX-1 compared to COX-2 was observed in endometrioid, mucinous and clear cell tumors. Stable COX-1 knockdown in HGSOC-representative OVCAR-3 ovarian cancer cells reduced gene expression in multiple pro-tumorigenic pathways. Functional cell viability, clonogenicity, and migration/invasion assays were consistent with transcriptomic changes. These effects were reversed by stable over-expression of COX-1 in SKOV-3 cells. Our results demonstrate a distinct pattern of COX-1 over-expression in HGSOC tumors and strong association of COX-1 with multiple pro-tumorigenic pathways in ovarian cancer cells. These findings provide additional insight into the role of COX-1 in human ovarian cancer and support further development of methods to selectively target COX-1 in the management of HGSOC tumors. PMID:25972361
Ridge Regression: A Regression Procedure for Analyzing correlated Independent Variables
ERIC Educational Resources Information Center
Rakow, Ernest A.
1978-01-01
Ridge regression is a technique used to ameliorate the problem of highly correlated independent variables in multiple regression analysis. This paper explains the fundamentals of ridge regression and illustrates its use. (JKS)
Tahsin, Subrina; Chang, Ni-Bin
2016-02-01
Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds.
Effect of acute hypoxia on cognition: A systematic review and meta-regression analysis.
McMorris, Terry; Hale, Beverley J; Barwood, Martin; Costello, Joseph; Corbett, Jo
2017-03-01
A systematic meta-regression analysis of the effects of acute hypoxia on the performance of central executive and non-executive tasks, and the effects of the moderating variables, arterial partial pressure of oxygen (PaO2) and hypobaric versus normobaric hypoxia, was undertaken. Studies were included if they were performed on healthy humans; within-subject design was used; data were reported giving the PaO2 or that allowed the PaO2 to be estimated (e.g. arterial oxygen saturation and/or altitude); and the duration of being in a hypoxic state prior to cognitive testing was ≤6days. Twenty-two experiments met the criteria for inclusion and demonstrated a moderate, negative mean effect size (g=-0.49, 95% CI -0.64 to -0.34, p<0.001). There were no significant differences between central executive and non-executive, perception/attention and short-term memory, tasks. Low (35-60mmHg) PaO2 was the key predictor of cognitive performance (R(2)=0.45, p<0.001) and this was independent of whether the exposure was in hypobaric hypoxic or normobaric hypoxic conditions.
Wong, Y Joel; Owen, Jesse; Shea, Munyi
2012-01-01
How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse sample of 223 men. The authors identified a 2-class solution. Both latent classes demonstrated very different associations between conformity to masculine norms and psychological distress. In Class 1 (labeled risk avoiders; n = 133), conformity to the masculine norm of risk-taking was negatively related to psychological distress. In Class 2 (labeled detached risk-takers; n = 90), conformity to the masculine norms of playboy, self-reliance, and risk-taking was positively related to psychological distress, whereas conformity to the masculine norm of violence was negatively related to psychological distress. A post hoc analysis revealed that younger men and Asian American men (compared with Latino and White American men) had significantly greater odds of being in Class 2 versus Class 1. The implications of these findings for future research and clinical practice are examined.
A systematic review and meta-regression analysis of mivacurium for tracheal intubation.
Vanlinthout, L E H; Mesfin, S H; Hens, N; Vanacker, B F; Robertson, E N; Booij, L H D J
2014-12-01
We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent intubation conditions: mivacurium dose, 24.4%; opioid use, 29.9%; time to intubation and age together, 18.6%. The odds ratio (95% CI) for excellent intubation was 3.14 (1.65-5.73) for doubling the mivacurium dose, 5.99 (2.14-15.18) for adding opioids to the intubation sequence, and 6.55 (6.01-7.74) for increasing the delay between mivacurium injection and airway insertion from 1 to 2 min in subjects aged 25 years and 2.17 (2.01-2.69) for subjects aged 70 years, p < 0.001 for all. We conclude that good conditions for tracheal intubation are more likely by delaying laryngoscopy after injecting a higher dose of mivacurium with an opioid, particularly in older people.
Screening houses for vapor intrusion risks: a multiple regression analysis approach.
Johnston, Jill E; Gibson, Jacqueline MacDonald
2013-06-04
The migration of chlorinated volatile organic compounds from groundwater to indoor air-known as vapor intrusion-can be an important exposure pathway at hazardous waste sites. Because sampling indoor air at every potentially affected home is often logistically infeasible, screening tools are needed to help identify at-risk homes. Currently, the U.S. Environmental Protection Agency (EPA) uses a simple screening approach that employs a generic vapor "attenuation factor," the ratio of the indoor air pollutant concentration to the pollutant concentration in the soil gas directly above the groundwater table. At every potentially affected home above contaminated groundwater, the EPA assumes the vapor attenuation factor is less than 1/1000--that is, that the indoor air concentration will not exceed 1/1000 times the soil-gas concentration immediately above groundwater. This paper reports on a screening-level model that improves on the EPA approach by considering environmental, contaminant, and household characteristics. The model is based on an analysis of the EPA's vapor intrusion database, which contains almost 2,400 indoor air and corresponding subsurface concentration samples collected in 15 states. We use the site data to develop a multilevel regression model for predicting the vapor attenuation factor. We find that the attenuation factor varies significantly with soil type, depth to groundwater, season, household foundation type, and contaminant molecular weight. The resulting model decreases the rate of false negatives compared to EPA's screening approach.
A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Bridge, Thomas M.; Amaya, Max A.
2014-01-01
A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper.
Power Law Regression Analysis of Heat Flux Width in Type I ELMs
NASA Astrophysics Data System (ADS)
Stephens, C. D.; Makowski, M. A.; Leonard, A. W.; Osborne, T. H.
2014-10-01
In this project, a database of Type I ELM characteristics has been assembled and will be used to investigate possible dependencies of the heat flux width on physics and engineering parameters. At the edge near the divertor, high impulsive heat loads are imparted onto the surface. The impact of these ELMs can cause a reduction in divertor lifetime if the heat flux is great enough due to material erosion. A program will be used to analyze data, extract relevant, measurable quantities, and record the quantities in the table. Care is taken to accurately capture the complex space/time structure of the ELM. Then correlations between discharge and equilibrium parameters will be investigated. Power law regression analysis will be used to help determine the dependence of the heat flux width on these various measurable quantities and parameters. This will enable us to better understand the physics of heat flux at the edge. Work supported in part by the National Undergraduate Fellowship Program in Plasma Physics and Fusion Energy Sciences and the US DOE under DE-FG02-04ER54761, DE-AC52-07NA27344, DE-FC02-04ER54698.
NASA Astrophysics Data System (ADS)
Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad
2015-01-01
Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.
Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia.
Tan, K C; Lim, H S; Mat Jafri, M Z
2014-06-01
This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.
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) C2 -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) C2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% (13) C2 -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.
Polanczyk, Guilherme V; Willcutt, Erik G; Salum, Giovanni A; Kieling, Christian; Rohde, Luis A
2014-01-01
Background: Previous studies have identified significant variability in attention-deficit / hyperactivity disorder (ADHD) prevalence estimates worldwide, largely explained by methodological procedures. However, increasing rates of ADHD diagnosis and treatment throughout the past few decades have fuelled concerns about whether the true prevalence of the disorder has increased over time. Methods: We updated the two most comprehensive systematic reviews on ADHD prevalence available in the literature. Meta-regression analyses were conducted to test the effect of year of study in the context of both methodological variables that determined variability in ADHD prevalence (diagnostic criteria, impairment criterion and source of information), and the geographical location of studies. Results: We identified 154 original studies and included 135 in the multivariate analysis. Methodological procedures investigated were significantly associated with heterogeneity of studies. Geographical location and year of study were not associated with variability in ADHD prevalence estimates. Conclusions: Confirming previous findings, variability in ADHD prevalence estimates is mostly explained by methodological characteristics of the studies. In the past three decades, there has been no evidence to suggest an increase in the number of children in the community who meet criteria for ADHD when standardized diagnostic procedures are followed. PMID:24464188
Li, Wentian; Sun, Fengzhu; Grosse, Ivo
2004-01-01
One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, L(D(0)|M). Typically, the computational burden for obtaining L(D(0)M) is immense, often exceeding the limits of available computing resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.
Predicting pesticide removal efficacy of vegetated filter strips: A meta-regression analysis.
Chen, Huajin; Grieneisen, Michael L; Zhang, Minghua
2016-04-01
Vegetated Filter Strips (VFS's) are widely used for alleviating agricultural pesticide loadings to surface water bodies. However, effective tools are lacking to quantify the performance of VFS's in reducing off-site pesticide transport. In this study, we applied meta-regression to develop a model for predicting VFS pesticide retention efficiency based on hydrologic responses of VFS's, incoming pollutant characteristics and the interaction within and between these two factor groups (R(2)=0.83). In cross-validation analysis, our model (Q(2)=0.81) outperformed the existing pesticide retention module of VFSMOD (Q(2)=0.72) by explicitly accounting for interaction effect and the categorical effect of pesticide adsorption properties. Based on the 181 data points studied, infiltration had a leading, positive influence on pesticide retention, followed by sedimentation and interaction between the two. Interaction between infiltration and pesticide adsorption properties was also prominent, as the influence of infiltration was significantly lower for strongly adsorbed pesticides. In addition, the clay content of incoming sediment was negatively associated with pesticide retention. Our model is not only valuable in predicting VFS performance, but also provides a quantitative characterization of the interacting VFS processes, thereby facilitating a deeper understanding of the underlying mechanisms.
Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto
2017-02-01
Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc.
Tipayamongkholgul, Mathuros; Lisakulruk, Sunisa
2011-05-01
Focusing on the socio-geographical factors that influence local vulnerability to dengue at the village level, spatial regression methods were applied to analyse, over a 5-year period, the village-specific, cumulative incidence of all reported dengue cases among 437 villages in Prachuap Khiri Khan, a semi-urban province of Thailand. The K-order nearest neighbour method was used to define the range of neighbourhoods. Analysis showed a significant neighbourhood effect (ρ = 0.405, P <0.001), which implies that villages with geographical proximity shared a similar level of vulnerability to dengue. The two independent social factors, associated with a higher incidence of dengue, were a shorter distance to the nearest urban area (β = -0.133, P <0.05) and a smaller average family size (β = -0.102, P <0.05). These results indicate that the trend of increasing dengue occurrence in rural Thailand arose in areas under stronger urban influence rather than in remote rural areas.
Wagner, Philippe; Ghith, Nermin; Leckie, George
2016-01-01
Background and Aim Many multilevel logistic regression analyses of “neighbourhood and health” focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between “specific” (measures of association) and “general” (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. Methods We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. Results For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. Conclusion Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible
NASA Astrophysics Data System (ADS)
Gizaw, Mesgana Seyoum; Gan, Thian Yew
2016-07-01
Regional Flood Frequency Analysis (RFFA) is a statistical method widely used to estimate flood quantiles of catchments with limited streamflow data. In addition, to estimate the flood quantile of ungauged sites, there could be only a limited number of stations with complete dataset are available from hydrologically similar, surrounding catchments. Besides traditional regression based RFFA methods, recent applications of machine learning algorithms such as the artificial neural network (ANN) have shown encouraging results in regional flood quantile estimations. Another novel machine learning technique that is becoming widely applicable in the hydrologic community is the Support Vector Regression (SVR). In this study, an RFFA model based on SVR was developed to estimate regional flood quantiles for two study areas, one with 26 catchments located in southeastern British Columbia (BC) and another with 23 catchments located in southern Ontario (ON), Canada. The SVR-RFFA model for both study sites was developed from 13 sets of physiographic and climatic predictors for the historical period. The Ef (Nash Sutcliffe coefficient) and R2 of the SVR-RFFA model was about 0.7 when estimating flood quantiles of 10, 25, 50 and 100 year return periods which indicate satisfactory model performance in both study areas. In addition, the SVR-RFFA model also performed well based on other goodness-of-fit statistics such as BIAS (mean bias) and BIASr (relative BIAS). If the amount of data available for training RFFA models is limited, the SVR-RFFA model was found to perform better than an ANN based RFFA model, and with significantly lower median CV (coefficient of variation) of the estimated flood quantiles. The SVR-RFFA model was then used to project changes in flood quantiles over the two study areas under the impact of climate change using the RCP4.5 and RCP8.5 climate projections of five Coupled Model Intercomparison Project (CMIP5) GCMs (Global Climate Models) for the 2041
A Bayesian ridge regression analysis of congestion's impact on urban expressway safety.
Shi, Qi; Abdel-Aty, Mohamed; Lee, Jaeyoung
2016-03-01
With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the
The development of a flyover noise prediction technique using multiple linear regression analysis
NASA Astrophysics Data System (ADS)
Rathgeber, R. K.
1981-04-01
At Cessna Aircraft Company, statistical analyses have been developed to define important trends in flyover noise data. Multiple regression techniques have provided the means to develop flyover noise prediction methods which have resulted in better accuracy than methods used in the past. Regression analyses have been conducted to determine the important relationship between propeller helical tip Mach number and the flyover noise level. Other variables have been included in the regression models either because the added variable contributed to reducing the remaining variation in the model or the variable appeared to be a strong causal agent of flyover noise.
2009-01-01
Background The central nervous system is considered a sanctuary site for HIV-1 replication. Variables associated with HIV cerebrospinal fluid (CSF) viral load in the context of opportunistic CNS infections are poorly understood. Our objective was to evaluate the relation between: (1) CSF HIV-1 viral load and CSF cytological and biochemical characteristics (leukocyte count, protein concentration, cryptococcal antigen titer); (2) CSF HIV-1 viral load and HIV-1 plasma viral load; and (3) CSF leukocyte count and the peripheral blood CD4+ T lymphocyte count. Methods Our approach was to use a prospective collection and analysis of pre-treatment, paired CSF and plasma samples from antiretroviral-naive HIV-positive patients with cryptococcal meningitis and assisted at the Francisco J Muñiz Hospital, Buenos Aires, Argentina (period: 2004 to 2006). We measured HIV CSF and plasma levels by polymerase chain reaction using the Cobas Amplicor HIV-1 Monitor Test version 1.5 (Roche). Data were processed with Statistix 7.0 software (linear regression analysis). Results Samples from 34 patients were analyzed. CSF leukocyte count showed statistically significant correlation with CSF HIV-1 viral load (r = 0.4, 95% CI = 0.13-0.63, p = 0.01). No correlation was found with the plasma viral load, CSF protein concentration and cryptococcal antigen titer. A positive correlation was found between peripheral blood CD4+ T lymphocyte count and the CSF leukocyte count (r = 0.44, 95% CI = 0.125-0.674, p = 0.0123). Conclusion Our study suggests that CSF leukocyte count influences CSF HIV-1 viral load in patients with meningitis caused by Cryptococcus neoformans.
Regression Analysis of Top of Descent Location for Idle-thrust Descents
NASA Technical Reports Server (NTRS)
Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg
2013-01-01
In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.
Lees, Mackenzie C.; Merani, Shaheed; Tauh, Keerit; Khadaroo, Rachel G.
2015-01-01
Background Older adults (≥ 65 yr) are the fastest growing population and are presenting in increasing numbers for acute surgical care. Emergency surgery is frequently life threatening for older patients. Our objective was to identify predictors of mortality and poor outcome among elderly patients undergoing emergency general surgery. Methods We conducted a retrospective cohort study of patients aged 65–80 years undergoing emergency general surgery between 2009 and 2010 at a tertiary care centre. Demographics, comorbidities, in-hospital complications, mortality and disposition characteristics of patients were collected. Logistic regression analysis was used to identify covariate-adjusted predictors of in-hospital mortality and discharge of patients home. Results Our analysis included 257 patients with a mean age of 72 years; 52% were men. In-hospital mortality was 12%. Mortality was associated with patients who had higher American Society of Anesthesiologists (ASA) class (odds ratio [OR] 3.85, 95% confidence interval [CI] 1.43–10.33, p = 0.008) and in-hospital complications (OR 1.93, 95% CI 1.32–2.83, p = 0.001). Nearly two-thirds of patients discharged home were younger (OR 0.92, 95% CI 0.85–0.99, p = 0.036), had lower ASA class (OR 0.45, 95% CI 0.27–0.74, p = 0.002) and fewer in-hospital complications (OR 0.69, 95% CI 0.53–0.90, p = 0.007). Conclusion American Society of Anesthesiologists class and in-hospital complications are perioperative predictors of mortality and disposition in the older surgical population. Understanding the predictors of poor outcome and the importance of preventing in-hospital complications in older patients will have important clinical utility in terms of preoperative counselling, improving health care and discharging patients home. PMID:26204143
Nakasone, Yutaka Ikeda, Osamu; Yamashita, Yasuyuki; Kudoh, Kouichi; Shigematsu, Yoshinori; Harada, Kazunori
2007-09-15
We applied multivariate analysis to the clinical findings in patients with acute gastrointestinal (GI) hemorrhage and compared the relationship between these findings and angiographic evidence of extravasation. Our study population consisted of 46 patients with acute GI bleeding. They were divided into two groups. In group 1 we retrospectively analyzed 41 angiograms obtained in 29 patients (age range, 25-91 years; average, 71 years). Their clinical findings including the shock index (SI), diastolic blood pressure, hemoglobin, platelet counts, and age, which were quantitatively analyzed. In group 2, consisting of 17 patients (age range, 21-78 years; average, 60 years), we prospectively applied statistical analysis by a logistics regression model to their clinical findings and then assessed 21 angiograms obtained in these patients to determine whether our model was useful for predicting the presence of angiographic evidence of extravasation. On 18 of 41 (43.9%) angiograms in group 1 there was evidence of extravasation; in 3 patients it was demonstrated only by selective angiography. Factors significantly associated with angiographic visualization of extravasation were the SI and patient age. For differentiation between cases with and cases without angiographic evidence of extravasation, the maximum cutoff point was between 0.51 and 0.0.53. Of the 21 angiograms obtained in group 2, 13 (61.9%) showed evidence of extravasation; in 1 patient it was demonstrated only on selective angiograms. We found that in 90% of the cases, the prospective application of our model correctly predicted the angiographically confirmed presence or absence of extravasation. We conclude that in patients with GI hemorrhage, angiographic visualization of extravasation is associated with the pre-embolization SI. Patients with a high SI value should undergo study to facilitate optimal treatment planning.
Determinants for changing the treatment of COPD: a regression analysis from a clinical audit
López-Campos, Jose Luis; Abad Arranz, María; Calero Acuña, Carmen; Romero Valero, Fernando; Ayerbe García, Ruth; Hidalgo Molina, Antonio; Aguilar Perez-Grovas, Ricardo I; García Gil, Francisco; Casas Maldonado, Francisco; Caballero Ballesteros, Laura; Sánchez Palop, María; Pérez-Tejero, Dolores; Segado, Alejandro; Calvo Bonachera, Jose; Hernández Sierra, Bárbara; Doménech, Adolfo; Arroyo Varela, Macarena; González Vargas, Francisco; Cruz Rueda, Juan J
2016-01-01
Introduction This study is an analysis of a pilot COPD clinical audit that evaluated adherence to guidelines for patients with COPD in a stable disease phase during a routine visit in specialized secondary care outpatient clinics in order to identify the variables associated with the decision to step-up or step-down pharmacological treatment. Methods This study was a pilot clinical audit performed at hospital outpatient respiratory clinics in the region of Andalusia, Spain (eight provinces with over eight million inhabitants), in which 20% of centers in the area (catchment population 3,143,086 inhabitants) were invited to participate. Treatment changes were evaluated in terms of the number of prescribed medications and were classified as step-up, step-down, or no change. Three backward stepwise binominal multivariate logistic regression analyses were conducted to evaluate variables associated with stepping up, stepping down, and inhaled corticosteroids discontinuation. Results The present analysis evaluated 565 clinical records (91%) of the complete audit. Of those records, 366 (64.8%) cases saw no change in pharmacological treatment, while 99 patients (17.5%) had an increase in the number of drugs, 55 (9.7%) had a decrease in the number of drugs, and 45 (8.0%) noted a change to other medication for a similar therapeutic scheme. Exacerbations were the main factor in stepping up treatment, as were the symptoms themselves. In contrast, rather than symptoms, doctors used forced expiratory volume in 1 second and previous treatment with long-term antibiotics or inhaled corticosteroids as the key determinants to stepping down treatment. Conclusion The majority of doctors did not change the prescription. When changes were made, a number of related factors were noted. Future trials must evaluate whether these therapeutic changes impact clinically relevant outcomes at follow-up. PMID:27330285
Pineda, Silvia; Real, Francisco X; Kogevinas, Manolis; Carrato, Alfredo; Chanock, Stephen J; Malats, Núria; Van Steen, Kristel
2015-12-01
Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease
Integrative analysis of multiple diverse omics datasets by sparse group multitask regression
Lin, Dongdong; Zhang, Jigang; Li, Jingyao; He, Hao; Deng, Hong-Wen; Wang, Yu-Ping
2014-01-01
A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the “small sample, but large variables” problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other
ERIC Educational Resources Information Center
Martin, Mary P.; Williams, John D.
1978-01-01
A series of multiple regression analyses was used to investigate a salary equity policy in a statewide system of institutions of higher education. Faculty rank, number of publications, and teaching effectiveness were among the variables examined. (JKS)
Galling, Britta; Roldán, Alexandra; Hagi, Katsuhiko; Rietschel, Liz; Walyzada, Frozan; Zheng, Wei; Cao, Xiao‐Lan; Xiang, Yu‐Tao; Zink, Mathias; Kane, John M.; Nielsen, Jimmi; Leucht, Stefan; Correll, Christoph U.
2017-01-01
Antipsychotic polypharmacy in schizophrenia is much debated, since it is common and costly with unclear evidence for its efficacy and safety. We conducted a systematic literature search and a random effects meta‐analysis of randomized trials comparing augmentation with a second antipsychotic vs. continued antipsychotic monotherapy in schizophrenia. Co‐primary outcomes were total symptom reduction and study‐defined response. Antipsychotic augmentation was superior to monotherapy regarding total symptom reduction (16 studies, N=694, standardized mean difference, SMD=–0.53, 95% CI: −0.87 to −0.19, p=0.002). However, superiority was only apparent in open‐label and low‐quality trials (both p<0.001), but not in double‐blind and high‐quality ones (p=0.120 and 0.226, respectively). Study‐defined response was similar between antipsychotic augmentation and monotherapy (14 studies, N=938, risk ratio = 1.19, 95% CI: 0.99 to 1.42, p=0.061), being clearly non‐significant in double‐blind and high‐quality studies (both p=0.990). Findings were replicated in clozapine and non‐clozapine augmentation studies. No differences emerged regarding all‐cause/specific‐cause discontinuation, global clinical impression, as well as positive, general and depressive symptoms. Negative symptoms improved more with augmentation treatment (18 studies, N=931, SMD=–0.38, 95% CI: −0.63 to −0.13, p<0.003), but only in studies augmenting with aripiprazole (8 studies, N=532, SMD=–0.41, 95% CI: −0.79 to −0.03, p=0.036). Few adverse effect differences emerged: D2 antagonist augmentation was associated with less insomnia (p=0.028), but more prolactin elevation (p=0.015), while aripiprazole augmentation was associated with reduced prolactin levels (p<0.001) and body weight (p=0.030). These data suggest that the common practice of antipsychotic augmentation in schizophrenia lacks double‐blind/high‐quality evidence for efficacy, except for negative symptom
Zhang, Man; Liu, Xu-Hua; He, Xiong-Kui; Zhang, Lu-Da; Zhao, Long-Lian; Li, Jun-Hui
2010-05-01
In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl's values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model's predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl's values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information, but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples.
NASA Astrophysics Data System (ADS)
Denli, H. H.; Koc, Z.
2015-12-01
Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.
NASA Astrophysics Data System (ADS)
Middlebrook, A. M.; Murphy, D. M.; Lee, S.; Lee, S.; Lee, S.; Thomson, D. S.; Thomson, D. S.
2001-12-01
During the Atlanta Supersites project in August 1999, the PALMS (Particle Analysis by Laser Mass Spectrometry) instrument collected over 500,000 individual particle spectra. The Atlanta data were originally analyzed by examining combinations of peaks and relative peak areas [Lee et al., 2001a,b], and a wide range of particle components such as sulfate, nitrate, mineral species, metals, organic species, and elemental carbon were detected. To further study the dataset, a classification program using regression tree analysis was developed and applied. Spectral data were compressed into a lower resolution spectrum (every 0.25 mass units) of the raw data and a list of peak areas (every mass unit). Each spectrum started as a normalized classification vector by itself. If the dot product of two classification vectors was within a certain threshold, they were combined into a new classification. The new classification vector was a normalized running average of the classifications being combined. In subsequent steps, the threshold for combining classifications was continuously lowered until a reasonable number of classifications remained. After the final iteration, each spectrum was compared individually with the entire set of classification vectors. Classifications were also combined manually. The classification results from the Atlanta data are generally consistent with those determined by peak identification. However, the classification program identified specific patterns in the mass spectra that were not found by peak identification and generated new particle types. Furthermore, rare particle types that may affect human health were studied in more detail. A description of the classification program as well as the results for the Atlanta data will be presented. Lee, S.-H., D. M. Murphy, D. S. Thomson, and A. M. Middlebrook, Chemical components of single particles measured with particle analysis by laser mass spectrometry (PALMS) during the Atlanta Supersites Project
Regression analysis to predict growth performance from dietary net energy in growing-finishing pigs.
Nitikanchana, S; Dritz, S S; Tokach, M D; DeRouchey, J M; Goodband, R D; White, B J
2015-06-01
Data from 41 trials with multiple energy levels (285 observations) were used in a meta-analysis to predict growth performance based on dietary NE concentration. Nutrient and energy concentrations in all diets were estimated using the NRC ingredient library. Predictor variables examined for best fit models using Akaike information criteria included linear and quadratic terms of NE, BW, CP, standardized ileal digestible (SID) Lys, crude fiber, NDF, ADF, fat, ash, and their interactions. The initial best fit models included interactions between NE and CP or SID Lys. After removal of the observations that fed SID Lys below the suggested requirement, these terms were no longer significant. Including dietary fat in the model with NE and BW significantly improved the G:F prediction model, indicating that NE may underestimate the influence of fat on G:F. The meta-analysis indicated that, as long as diets are adequate for other nutrients (i.e., Lys), dietary NE is adequate to predict changes in ADG across different dietary ingredients and conditions. The analysis indicates that ADG increases with increasing dietary NE and BW but decreases when BW is above 87 kg. The G:F ratio improves with increasing dietary NE and fat but decreases with increasing BW. The regression equations were then evaluated by comparing the actual and predicted performance of 543 finishing pigs in 2 trials fed 5 dietary treatments, included 3 different levels of NE by adding wheat middlings, soybean hulls, dried distillers grains with solubles (DDGS; 8 to 9% oil), or choice white grease (CWG) to a corn-soybean meal-based diet. Diets were 1) 30% DDGS, 20% wheat middlings, and 4 to 5% soybean hulls (low energy); 2) 20% wheat middlings and 4 to 5% soybean hulls (low energy); 3) a corn-soybean meal diet (medium energy); 4) diet 2 supplemented with 3.7% CWG to equalize the NE level to diet 3 (medium energy); and 5) a corn-soybean meal diet with 3.7% CWG (high energy). Only small differences were observed
NASA Astrophysics Data System (ADS)
Lu, Dan; Ye, Ming; Hill, Mary C.
2012-09-01
Confidence intervals based on classical regression theories augmented to include prior information and credible intervals based on Bayesian theories are conceptually different ways to quantify parametric and predictive uncertainties. Because both confidence and credible intervals are used in environmental modeling, we seek to understand their differences and similarities. This is of interest in part because calculating confidence intervals typically requires tens to thousands of model runs, while Bayesian credible intervals typically require tens of thousands to millions of model runs. Given multi-Gaussian distributed observation errors, our theoretical analysis shows that, for linear or linearized-nonlinear models, confidence and credible intervals are always numerically identical when consistent prior information is used. For nonlinear models, nonlinear confidence and credible intervals can be numerically identical if parameter confidence regions defined using the approximate likelihood method and parameter credible regions estimated using Markov chain Monte Carlo realizations are numerically identical and predictions are a smooth, monotonic function of the parameters. Both occur if intrinsic model nonlinearity is small. While the conditions of Gaussian errors and small intrinsic model nonlinearity are violated by many environmental models, heuristic tests using analytical and numerical models suggest that linear and nonlinear confidence intervals can be useful approximations of uncertainty even under significantly nonideal conditions. In the context of epistemic model error for a complex synthetic nonlinear groundwater problem, the linear and nonlinear confidence and credible intervals for individual models performed similarly enough to indicate that the computationally frugal confidence intervals can be useful in many circumstances. Experiences with these groundwater models are expected to be broadly applicable to many environmental models. We suggest that for
Zhang, Yiwei; Pan, Wei
2014-01-01
Genome-wide association studies (GWAS) have been established as a major tool to identify genetic variants associated with complex traits, such as common diseases. However, GWAS may suffer from false positives and false negatives due to confounding population structures, including known or unknown relatedness. Another important issue is unmeasured environmental risk factors. Among many methods for adjusting for population structures, two approaches stand out: one is principal component regression (PCR) based on principal component analysis (PCA), which is perhaps most popular due to its early appearance, simplicity and general effectiveness; the other is based on a linear mixed model (LMM) that has emerged recently as perhaps the most flexible and effective, especially for samples with complex structures as in model organisms. As shown previously, the PCR approach can be regarded as an approximation to a LMM; such an approximation depends on the number of the top principal components (PCs) used, the choice of which is often difficult in practice. Hence, in the presence of population structure, the LMM appears to outperform the PCR method. However, due to the different treatments of fixed versus random effects in the two approaches, we show an advantage of PCR over LMM: in the presence of an unknown but spatially confined environmental confounder (e.g. environmental pollution or life style), the PCs may be able to implicitly and effectively adjust for the confounder while the LMM cannot. Accordingly, to adjust for both population structures and non-genetic confounders, we propose a hybrid method combining the use and thus strengths of PCR and LMM. We use real genotype data and simulated phenotypes to confirm the above points, and establish the superior performance of the hybrid method across all scenarios. PMID:25536929
Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
Osório, Luís; Cavadas, Vitor; Fraga, Avelino; Carrasquinho, Eduardo; Cardoso de Oliveira, Eduardo; Castelo-Branco, Miguel; Roobol, Monique J
2016-01-01
Background Urological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to medical app downloads have yet to be identified, including the hypothetical influence of expert involvement in app development. Objective The objective of our study was to identify predictors of the number of urology app downloads. Methods We reviewed urology apps available in the Google Play Store and collected publicly available data. Multivariate ordinal logistic regression evaluated the effect of publicly available app variables on the number of apps being downloaded. Results Of 129 urology apps eligible for study, only 2 (1.6%) had >10,000 downloads, with half having ≤100 downloads and 4 (3.1%) having none at all. Apps developed with expert urologist involvement (P=.003), optional in-app purchases (P=.01), higher user rating (P<.001), and more user reviews (P<.001) were more likely to be installed. App cost was inversely related to the number of downloads (P<.001). Only data from the Google Play Store and the developers’ websites, but not other platforms, were publicly available for analysis, and the level and nature of expert involvement was not documented. Conclusions The explicit participation of urologists in app development is likely to enhance its chances to have a higher number of downloads. This finding should help in the design of better apps and further promote urologist involvement in mHealth. Official certification processes are required to ensure app quality and user safety. PMID:27421338
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert M.
2013-01-01
A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.
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…
ERIC Educational Resources Information Center
Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois
2013-01-01
This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…
ERIC Educational Resources Information Center
Ou, Dongshu
2010-01-01
The high school exit exam (HSEE) is rapidly becoming a standardized assessment procedure for educational accountability in the United States. I use a unique, state-specific dataset to identify the effects of failing the HSEE on the likelihood of dropping out of high school based on a regression discontinuity design. The analysis shows that…
ERIC Educational Resources Information Center
Kanyongo, Gibbs Y.; Certo, Janine; Launcelot, Brown I.
2006-01-01
In this study, we report results of a study examining the relationship between home environment factors and reading achievement in Zimbabwe. The study utilised data collected by the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ). The data were submitted to linear regression analysis through structural equation…
ERIC Educational Resources Information Center
Thomas, Emily H.; Galambos, Nora
To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…
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 &…
COX-2 and Prostate Cancer Angiogenesis
2002-03-01
sphingomyelin to ceramide , a mediator of apopto- sis (Figure 1).10 The initial interest in COX-2 grew out of the In colon cancer, much of the focus has been...Sphingomyelin - Ceramide Arachidanic Acid \\\\ COX-1 / COX-2 Prostaglandin G2 Prostaglandin H2 Oxidation of Xenobiolics Prostaglandins Malordialdehyde FIGURE 1...variety of tissues in- corneal model, it was demonstrated that endothe- cluding skin , urinary bladder, gastric mucosa, lial cell COX-2 is essential for
Development of LACIE CCEA-1 weather/wheat yield models. [regression analysis
NASA Technical Reports Server (NTRS)
Strommen, N. D.; Sakamoto, C. M.; Leduc, S. K.; Umberger, D. E. (Principal Investigator)
1979-01-01
The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.
Zou, Kelly H.; O’Malley, A. James
2005-01-01
Receiver operating characteristic (ROC) analysis is a useful evaluative method of diagnostic accuracy. A Bayesian hierarchical nonlinear regression model for ROC analysis was developed. A validation analysis of diagnostic accuracy was conducted using prospective multi-center clinical trial prostate cancer biopsy data collected from three participating centers. The gold standard was based on radical prostatectomy to determine local and advanced disease. To evaluate the diagnostic performance of PSA level at fixed levels of Gleason score, a normality transformation was applied to the outcome data. A hierarchical regression analysis incorporating the effects of cluster (clinical center) and cancer risk (low, intermediate, and high) was performed, and the area under the ROC curve (AUC) was estimated. PMID:16161801
NASA Astrophysics Data System (ADS)
Tomczyk, Aleksandra; Ewertowski, Marek; White, Piran; Kasprzak, Leszek
2016-04-01
The dual role of many Protected Natural Areas in providing benefits for both conservation and recreation poses challenges for management. Although recreation-based damage to ecosystems can occur very quickly, restoration can take many years. The protection of conservation interests at the same as providing for recreation requires decisions to be made about how to prioritise and direct management actions. Trails are commonly used to divert visitors from the most important areas of a site, but high visitor pressure can lead to increases in trail width and a concomitant increase in soil erosion. Here we use detailed field data on condition of recreational trails in Gorce National Park, Poland, as the basis for a regression tree analysis to determine the factors influencing trail deterioration, and link specific trail impacts with environmental, use related and managerial factors. We distinguished 12 types of trails, characterised by four levels of degradation: (1) trails with an acceptable level of degradation; (2) threatened trails; (3) damaged trails; and (4) heavily damaged trails. Damaged trails were the most vulnerable of all trails and should be prioritised for appropriate conservation and restoration. We also proposed five types of monitoring of recreational trail conditions: (1) rapid inventory of negative impacts; (2) monitoring visitor numbers and variation in type of use; (3) change-oriented monitoring focusing on sections of trail which were subjected to changes in type or level of use or subjected to extreme weather events; (4) monitoring of dynamics of trail conditions; and (5) full assessment of trail conditions, to be carried out every 10-15 years. The application of the proposed framework can enhance the ability of Park managers to prioritise their trail management activities, enhancing trail conditions and visitor safety, while minimising adverse impacts on the conservation value of the ecosystem. A.M.T. was supported by the Polish Ministry of
Witt, Katrina; van Dorn, Richard; Fazel, Seena
2013-01-01
Background Previous reviews on risk and protective factors for violence in psychosis have produced contrasting findings. There is therefore a need to clarify the direction and strength of association of risk and protective factors for violent outcomes in individuals with psychosis. Method We conducted a systematic review and meta-analysis using 6 electronic databases (CINAHL, EBSCO, EMBASE, Global Health, PsycINFO, PUBMED) and Google Scholar. Studies were identified that reported factors associated with violence in adults diagnosed, using DSM or ICD criteria, with schizophrenia and other psychoses. We considered non-English language studies and dissertations. Risk and protective factors were meta-analysed if reported in three or more primary studies. Meta-regression examined sources of heterogeneity. A novel meta-epidemiological approach was used to group similar risk factors into one of 10 domains. Sub-group analyses were then used to investigate whether risk domains differed for studies reporting severe violence (rather than aggression or hostility) and studies based in inpatient (rather than outpatient) settings. Findings There were 110 eligible studies reporting on 45,533 individuals, 8,439 (18.5%) of whom were violent. A total of 39,995 (87.8%) were diagnosed with schizophrenia, 209 (0.4%) were diagnosed with bipolar disorder, and 5,329 (11.8%) were diagnosed with other psychoses. Dynamic (or modifiable) risk factors included hostile behaviour, recent drug misuse, non-adherence with psychological therapies (p values<0.001), higher poor impulse control scores, recent substance misuse, recent alcohol misuse (p values<0.01), and non-adherence with medication (p value <0.05). We also examined a number of static factors, the strongest of which were criminal history factors. When restricting outcomes to severe violence, these associations did not change materially. In studies investigating inpatient violence, associations differed in strength but not direction
Regression analysis of time trends in perinatal mortality in Germany 1980-1993.
Scherb, H; Weigelt, E; Brüske-Hohlfeld, I
2000-01-01
Numerous investigations have been carried out on the possible impact of the Chernobyl accident on the prevalence of anomalies at birth and on perinatal mortality. In many cases the studies were aimed at the detection of differences of pregnancy outcome measurements between regions or time periods. Most authors conclude that there is no evidence of a detrimental physical effect on congenital anomalies or other outcomes of pregnancy following the accident. In this paper, we report on statistical analyses of time trends of perinatal mortality in Germany. Our main intention is to investigate whether perinatal mortality, as reflected in official records, was increased in 1987 as a possible effect of the Chernobyl accident. We show that, in Germany as a whole, there was a significantly elevated perinatal mortality proportion in 1987 as compared to the trend function. The increase is 4.8% (p = 0.0046) of the expected perinatal death proportion for 1987. Even more pronounced levels of 8.2% (p = 0. 0458) and 8.5% (p = 0.0702) may be found in the higher contaminated areas of the former German Democratic Republic (GDR), including West Berlin, and of Bavaria, respectively. To investigate the impact of statistical models on results, we applied three standard regression techniques. The observed significant increase in 1987 is independent of the statistical model used. Stillbirth proportions show essentially the same behavior as perinatal death proportions, but the results for all of Germany are nonsignificant due to the smaller numbers involved. Analysis of the association of stillbirth proportions with the (137)Cs deposition on a district level in Bavaria discloses a significant relationship. Our results are in contrast to those of many analyses of the health consequences of the Chernobyl accident and contradict the present radiobiologic knowledge. As we are dealing with highly aggregated data, other causes or artifacts may explain the observed effects. Hence, the findings
Regression analysis of recent changes in cardiovascular morbidity and mortality in The Netherlands.
Bonneux, L.; Looman, C. W.; Barendregt, J. J.; Van der Maas, P. J.
1997-01-01
OBJECTIVES: To test whether recent declines in mortality from coronary heart disease were associated with increased mortality from other cardiovascular diseases. DESIGN: Poisson regression analysis of national data on causes of death and hospital discharges. SETTING AND SUBJECTS: Population of the Netherlands, 1969-93. MAIN OUTCOME MEASURES: Annual changes in mortality from coronary heart disease, stroke, and other cardiovascular diseases and annual changes in hospital discharge rates for acute coronary events, stroke, and congestive heart failures. RESULTS: Patterns of cardiovascular mortality changed abruptly in 1987-93. Annual decline in mortality from coronary heart disease increased sharply for women and men: from -1.9% (95% confidence interval -2.2% to -1.6%) and -1.7% (-1.9% to -1.4%) respectively in 1979-86 to -3.1% (-3.5% to -2.6%) and -4.2% (-4.6% to -3.9%) in 1987-93. The longstanding decline in mortality from stroke levelled off: from annual change of -3.3% (-3.7% to -2.8%) and -3.2% (-3.7% to -2.8%) in 1979-86 to -0.1% (-0.7% to 0.4%) and -1.1% (-1.7% to -0.5%) in 1987-93. Mortality from other cardiovascular diseases, however, started to increase: from -2.0% (-2.4% to -1.6%) and -0.2% (-0.5% to 0.2%) in 1979-86 to 1.5% (1.0% to 2.0%) and 1.9% (1.5% to 2.3%) in 1987-93. Hospital discharge rates for acute coronary heart disease, congestive heart failure, and stroke increased during 1980-6. During 1987-93 discharge rates for stroke and coronary heart disease stabilised but rates for congestive heart failure increased. CONCLUSION: Improved management of coronary heart disease seems to have reduced mortality, but some of the gains are lost to deaths from stroke and other cardiovascular diseases. The increasing numbers of patients with coronary heart disease who survive will increase demands on health services for long term care. PMID:9080996
Huntley, J D; Gould, R L; Liu, K; Smith, M; Howard, R J
2015-01-01
Objectives To review the efficacy of cognitive interventions on improving general cognition in dementia. Method Online literature databases and trial registers, previous systematic reviews and leading journals were searched for relevant randomised controlled trials. A systematic review, random-effects meta-analyses and meta-regression were conducted. Cognitive interventions were categorised as: cognitive stimulation (CS), involving a range of social and cognitive activities to stimulate multiple cognitive domains; cognitive training (CT), involving repeated practice of standardised tasks targeting a specific cognitive function; cognitive rehabilitation (CR), which takes a person-centred approach to target impaired function; or mixed CT and stimulation (MCTS). Separate analyses were conducted for general cognitive outcome measures and for studies using ‘active’ (designed to control for non-specific therapeutic effects) and non-active (minimal or no intervention) control groups. Results 33 studies were included. Significant positive effect sizes (Hedges’ g) were found for CS with the mini-mental state examination (MMSE) (g=0.51, 95% CI 0.29 to 0.69; p<0.001) compared to non-active controls and (g=0.35, 95% CI 0.06 to 0.65; p=0.019) compared to active controls. Significant benefit was also seen with the Alzheimer's disease Assessment Scale-Cognition (ADAS-Cog) (g=−0.26, 95% CI −0.445 to −0.08; p=0.005). There was no evidence that CT or MCTS produced significant improvements on general cognition outcomes and not enough CR studies for meta-analysis. The lowest accepted minimum clinically important difference was reached in 11/17 CS studies for the MMSE, but only 2/9 studies for the ADAS-Cog. Additionally, 95% prediction intervals suggested that although statistically significant, CS may not lead to benefits on the ADAS-Cog in all clinical settings. Conclusions CS improves scores on MMSE and ADAS-Cog in dementia, but benefits on the ADAS-Cog are generally
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…
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
ERIC Educational Resources Information Center
Cohen, Ayala; Nahum-Shani, Inbal; Doveh, Etti
2010-01-01
In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. Although this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the…
Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis
ERIC Educational Resources Information Center
Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John
2012-01-01
Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…
ERIC Educational Resources Information Center
Stewart, Kenneth D.; And Others
1996-01-01
Development of a comprehensive, pro-active, value-centered model for reviewing college faculty salaries is described. The model, used at Frostburg State University (Maryland), draws on multiple-regression applications to salary equity issues. Applications of the model in evaluating, redressing, and preventing salary equity problems are presented.…
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…
On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis
ERIC Educational Resources Information Center
Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas
2011-01-01
The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…
Hoeflinger, Jennifer L; Hoeflinger, Daniel E; Miller, Michael J
2017-01-01
Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent.
ERIC Educational Resources Information Center
Luna, Andrew L.; Brennan, Kelly A.
2009-01-01
This study uses a regression model to determine if a significant difference exists between the actual budget allocation that an academic department received and the model's predicted budget allocation for that same department. Budget data from a Southeastern Master's/Comprehensive state university were used as the dependent variable, and the…
Selection on plasticity of seasonal life-history traits using random regression mixed model analysis
Brommer, Jon E; Kontiainen, Pekka; Pietiäinen, Hannu
2012-01-01
Theory considers the covariation of seasonal life-history traits as an optimal reaction norm, implying that deviating from this reaction norm reduces fitness. However, the estimation of reaction-norm properties (i.e., elevation, linear slope, and higher order slope terms) and the selection on these is statistically challenging. We here advocate the use of random regression mixed models to estimate reaction-norm properties and the use of bivariate random regression to estimate selection on these properties within a single model. We illustrate the approach by random regression mixed models on 1115 observations of clutch sizes and laying dates of 361 female Ural owl Strix uralensis collected over 31 years to show that (1) there is variation across individuals in the slope of their clutch size–laying date relationship, and that (2) there is selection on the slope of the reaction norm between these two traits. Hence, natural selection potentially drives the negative covariance in clutch size and laying date in this species. The random-regression approach is hampered by inability to estimate nonlinear selection, but avoids a number of disadvantages (stats-on-stats, connecting reaction-norm properties to fitness). The approach is of value in describing and studying selection on behavioral reaction norms (behavioral syndromes) or life-history reaction norms. The approach can also be extended to consider the genetic underpinning of reaction-norm properties. PMID:22837818
ERIC Educational Resources Information Center
Wiley, Kristofor R.
2013-01-01
Many of the social and emotional needs that have historically been associated with gifted students have been questioned on the basis of recent empirical evidence. Research on the topic, however, is often limited by sample size, selection bias, or definition. This study addressed these limitations by applying linear regression methodology to data…
A Latent Class Regression Analysis of Men's Conformity to Masculine Norms and Psychological Distress
ERIC Educational Resources Information Center
Wong, Y. Joel; Owen, Jesse; Shea, Munyi
2012-01-01
How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse…
NASA Astrophysics Data System (ADS)
Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat
2015-04-01
Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.
Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...
Immunohistologic analysis of the phenomenon of spontaneous regression of numerous flat warts.
Aiba, S; Rokugo, M; Tagami, H
1986-09-15
Among various tumors induced by human papilloma virus (HPV), flat warts are unique in that they show a systemic regression phenomenon after sudden occurrence of inflammation in all the tumors, leaving permanent immunity to flat warts in the host. When studied immunohistochemically, the presence of HPV antigen using papilloma virus genus-specific antiserum in 31 cases of regressing flat warts was not found; whereas it was demonstrated in the nuclei of upper epidermal cells of ordinary flat warts in 12 of 19 cases (63%). T-cell phenotype assessment in nine regressing flat warts using monoclonal antibodies showed that helper/inducer subsets constituted a major peritumoral dermal infiltrate with a moderate number of intermingling OKT6+ cells. In contrast, the tumoral epidermis was invaded by almost equal number of suppressor/cytotoxic T-cells and helper/inducer T-cells, where at least some keratinocytes also expressed HLA-DR antigen in addition to Langerhans cells. Most T-cells expressed HLA-DR antigen, a marker of activation, but only a small number of them were Tac antigen+, i.e., bearing interleukin 2 receptors. Leu 7+ natural killer cells were seldom found in the infiltrate. These data provide evidence that T-cell-mediated immune attack against tumor cells and not against intranuclear HPV antigen, induces the systemic spontaneous regression of numerous flat warts in humans.
Multiple Regression Analysis of Factors that May Influence Middle School Science Scores
ERIC Educational Resources Information Center
Glover, Judith
2012-01-01
The purpose of this quantitative multiple regression study was to determine whether a relationship existed between Maryland State Assessment (MSA) reading scores, MSA math scores, gender, ethnicity, age, and MSA science scores. Also examined was if MSA reading scores, MSA math scores, gender, ethnicity, and age can be used in combination or alone…
ERIC Educational Resources Information Center
Cohen, Ira L.; Liu, Xudong; Hudson, Melissa; Gillis, Jennifer; Cavalari, Rachel N. S.; Romanczyk, Raymond G.; Karmel, Bernard Z.; Gardner, Judith M.
2016-01-01
In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80%,…
NASA Astrophysics Data System (ADS)
Grégoire, G.
2014-12-01
The logistic regression originally is intended to explain the relationship between the probability of an event and a set of covariables. The model's coefficients can be interpreted via the odds and odds ratio, which are presented in introduction of the chapter. The observations are possibly got individually, then we speak of binary logistic regression. When they are grouped, the logistic regression is said binomial. In our presentation we mainly focus on the binary case. For statistical inference the main tool is the maximum likelihood methodology: we present the Wald, Rao and likelihoods ratio results and their use to compare nested models. The problems we intend to deal with are essentially the same as in multiple linear regression: testing global effect, individual effect, selection of variables to build a model, measure of the fitness of the model, prediction of new values… . The methods are demonstrated on data sets using R. Finally we briefly consider the binomial case and the situation where we are interested in several events, that is the polytomous (multinomial) logistic regression and the particular case of ordinal logistic regression.
Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H
2017-02-01
At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification.
DWCox: A density-weighted Cox model for outlier-robust prediction of prostate cancer survival
Xiao, Jinfeng; Wang, Sheng; Shang, Jingbo; Lin, Henry; Xin, Doris; Ren, Xiang; Han, Jiawei; Peng, Jian
2016-01-01
Reliable predictions on the risk and survival time of prostate cancer patients based on their clinical records can help guide their treatment and provide hints about the disease mechanism. The Cox regression is currently a commonly accepted approach for such tasks in clinical applications. More complex methods, like ensemble approaches, have the potential of reaching better prediction accuracy at the cost of increased training difficulty and worse result interpretability. Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose DWCox, a density-weighted Cox model that has improved robustness against outliers and thus can provide more accurate predictions of prostate cancer survival. DWCox assigns weights to the training data according to their local kernel density in the feature space, and incorporates those weights into the partial likelihood function. A linear regression is then used to predict the actual survival times from the predicted risks. In the 2015 Prostate Cancer DREAM Challenge, DWCox obtained the best average ranking in prediction accuracy on the risk and survival time. The success of DWCox is remarkable given that it is one of the smallest and most interpretable models submitted to the challenge. In simulations, DWCox performed consistently better than a standard Cox model when the training data contained many sparsely distributed outliers. Although developed for prostate cancer patients, DWCox can be easily re-trained and applied to other survival analysis problems. DWCox is implemented in R and can be downloaded from https://github.com/JinfengXiao/DWCox. PMID:28299178
Replicating psychiatric ratings through multiple regression analysis: The Midtown Manhattan Restudy.
Singer, E; Cohen, S M; Garfinkel, R; Srole, L
1976-12-01
This paper examines three related questions: First, can psychiatrists' judgments be successfully predicted by multiple regression techniques? Second, assuming that they can, are such ratings a valid measure of mental health for the same sample at a later point in time? Third, what is the relation between mental health ratings made in 1954 and such subsequently reported behavioral outcomes as nervous breakdown, mental hospitalization, or seeking professional help for emotional problems? The evidence presented warrants two conclusion. (1) The computer-derived mental health ratings are an adequate substitute for the original ratings. The regression equation accounts for 69 percent of the variance in those ratings; and the computer-derived ratings behave in the same way as the psychiatrists' ratings in relation to other variables. (2) However, neither the psychiatrists' ratings nor the computer-derived ratings are very accurate in predicting subsequent self-reported behavior indicative of mental impairment.
Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis
ERIC Educational Resources Information Center
Johnson, William L.; Johnson, Annabel M.; Johnson, Jared
2012-01-01
Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…
Sanford, Ward E.; Nelms, David L.; Pope, Jason P.; Selnick, David L.
2012-01-01
This study by the U.S. Geological Survey, prepared in cooperation with the Virginia Department of Environmental Quality, quantifies the components of the hydrologic cycle across the Commonwealth of Virginia. Long-term, mean fluxes were calculated for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971–2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. The base-flow proportion for the 48 watersheds averaged 72 percent using specific conductance, a value that was substantially higher than the 61 percent average calculated using a graphical-separation technique (the USGS program PART). Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia.
Scarneciu, Camelia C.; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D.; Varciu, Mihai S.; Andreescu, Oana; Scarneciu, Ioan
2017-01-01
Objectives: This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. Methods: The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). Results: From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. Conclusions: The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is
Capacitance Regression Modelling Analysis on Latex from Selected Rubber Tree Clones
NASA Astrophysics Data System (ADS)
Rosli, A. D.; Hashim, H.; Khairuzzaman, N. A.; Mohd Sampian, A. F.; Baharudin, R.; Abdullah, N. E.; Sulaiman, M. S.; Kamaru'zzaman, M.
2015-11-01
This paper investigates the capacitance regression modelling performance of latex for various rubber tree clones, namely clone 2002, 2008, 2014 and 3001. Conventionally, the rubber tree clones identification are based on observation towards tree features such as shape of leaf, trunk, branching habit and pattern of seeds texture. The former method requires expert persons and very time-consuming. Currently, there is no sensing device based on electrical properties that can be employed to measure different clones from latex samples. Hence, with a hypothesis that the dielectric constant of each clone varies, this paper discusses the development of a capacitance sensor via Capacitance Comparison Bridge (known as capacitance sensor) to measure an output voltage of different latex samples. The proposed sensor is initially tested with 30ml of latex sample prior to gradually addition of dilution water. The output voltage and capacitance obtained from the test are recorded and analyzed using Simple Linear Regression (SLR) model. This work outcome infers that latex clone of 2002 has produced the highest and reliable linear regression line with determination coefficient of 91.24%. In addition, the study also found that the capacitive elements in latex samples deteriorate if it is diluted with higher volume of water.
NASA Astrophysics Data System (ADS)
Schlechtingen, Meik; Ferreira Santos, Ilmar
2011-07-01
This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.
GIS-assisted regression analysis to identify sources of selenium in streams
See, Randolph B.; Naftz, David L.; Qualls, Charles L.
1992-01-01
Using a geographic information system, a regression model has been developed to identify and to assess potential sources of selenium in the Kendrick Reclamation Project Area, Wyoming. A variety of spatially distributed factors was examined to determine which factors are most likely to affect selenium discharge in tributaries to the North Platte River. Areas of Upper Cretaceous Cody Shale and Quaternary alluvial deposits and irrigated land, length of irrigation canals, and boundaries of hydrologic subbasins of the major tributaries to the North Platte River were digitized and stored in a geographic information system. Selenium concentrations in samples of soil, plant material, ground water, and surface water were determined and evaluated. The location of all sampling sites was digitized and stored in the geographic information system, together with the selenium concentrations in samples. A regression model was developed using stepwise multiple regression of median selenium discharges on the physical and chemical characteristics of hydrologic subbasins. Results indicate that the intensity of irrigation in a hydrologic subbasin, as determined by area of irrigated land and length of irrigation delivery canals, accounts for the largest variation in median selenium discharges among subbasins. Tributaries draining hydrologic subbasins with greater intensity of irrigation result in greater selenium discharges to the North Platte River than do tributaries draining subbasins with lesser intensity of irrigation.
Unification of regression-based methods for the analysis of natural selection.
Morrissey, Michael B; Sakrejda, Krzysztof
2013-07-01
Regression analyses are central to characterization of the form and strength of natural selection in nature. Two common analyses that are currently used to characterize selection are (1) least squares-based approximation of the individual relative fitness surface for the purpose of obtaining quantitatively useful selection gradients, and (2) spline-based estimation of (absolute) fitness functions to obtain flexible inference of the shape of functions by which fitness and phenotype are related. These two sets of methodologies are often implemented in parallel to provide complementary inferences of the form of natural selection. We unify these two analyses, providing a method whereby selection gradients can be obtained for a given observed distribution of phenotype and characterization of a function relating phenotype to fitness. The method allows quantitatively useful selection gradients to be obtained from analyses of selection that adequately model nonnormal distributions of fitness, and provides unification of the two previously separate regression-based fitness analyses. We demonstrate the method by calculating directional and quadratic selection gradients associated with a smooth regression-based generalized additive model of the relationship between neonatal survival and the phenotypic traits of gestation length and birth mass in humans.
Detection of Cutting Tool Wear using Statistical Analysis and Regression Model
NASA Astrophysics Data System (ADS)
Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin
2010-10-01
This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.
Gmur, Stephan; Vogt, Daniel; Zabowski, Darlene; Moskal, L Monika
2012-01-01
The characterization of soil attributes using hyperspectral sensors has revealed patterns in soil spectra that are known to respond to mineral composition, organic matter, soil moisture and particle size distribution. Soil samples from different soil horizons of replicated soil series from sites located within Washington and Oregon were analyzed with the FieldSpec Spectroradiometer to measure their spectral signatures across the electromagnetic range of 400 to 1,000 nm. Similarity rankings of individual soil samples reveal differences between replicate series as well as samples within the same replicate series. Using classification and regression tree statistical methods, regression trees were fitted to each spectral response using concentrations of nitrogen, carbon, carbonate and organic matter as the response variables. Statistics resulting from fitted trees were: nitrogen R(2) 0.91 (p < 0.01) at 403, 470, 687, and 846 nm spectral band widths, carbonate R(2) 0.95 (p < 0.01) at 531 and 898 nm band widths, total carbon R(2) 0.93 (p < 0.01) at 400, 409, 441 and 907 nm band widths, and organic matter R(2) 0.98 (p < 0.01) at 300, 400, 441, 832 and 907 nm band widths. Use of the 400 to 1,000 nm electromagnetic range utilizing regression trees provided a powerful, rapid and inexpensive method for assessing nitrogen, carbon, carbonate and organic matter for upper soil horizons in a nondestructive method.
Cox1 mutation abrogates need for Cox23 in cytochrome c oxidase biogenesis
Dela Cruz, Richard; Jeong, Mi-Young; Winge, Dennis R.
2016-01-01
Cox23 is a known conserved assembly factor for cytochrome c oxidase, although its role in cytochrome c oxidase (CcO) biogenesis remains unresolved. To gain additional insights into its role, we isolated spontaneous suppressors of the respiratory growth defect in cox23∆ yeast cells. We recovered independent colonies that propagated on glycerol/lactate medium for cox23∆ cells at 37°C. We mapped these mutations to the mitochondrial genome and specifically to COX1 yielding an I101F substitution. The I101F Cox1 allele is a gain-of-function mutation enabling yeast to respire in the absence of Cox23. CcO subunit steady-state levels were restored with the I101F Cox1 suppressor mutation and oxygen consumption and CcO activity were likewise restored. Cells harboring the mitochondrial genome encoding I101F Cox1 were used to delete genes for other CcO assembly factors to test the specificity of the Cox1 mutation as a suppressor of cox23∆ cells. The Cox1 mutant allele fails to support respiratory growth in yeast lacking Cox17, Cox19, Coa1, Coa2, Cox14 or Shy1, demonstrating its specific suppressor activity for cox23∆ cells.
NASA Astrophysics Data System (ADS)
Coe, Rob; Dalrymple, Brent
More than 1000 friends, students, and colleagues from all over the country filled Stanford Memorial Chapel (Stanford, Calif.) on February 3, 1987, to join in “A Celebration of the Life of Allan Cox.” Allan died early on the morning of January 27 while bicycling, the sport he had come to love the most. Between pieces of his favorite music by Bach and Mozart, Stanford administrators and colleagues spoke in tribute of Allan's unique qualities as friend, scientist, teacher, and dean of the School of Earth Sciences. James Rosse, Vice President and Provost of Stanford University, struck a particularly resonant chord with his personal remarks: "Allan reached out to each person he knew with the warmth and attention that can only come from deep respect and affection for others. I never heard him speak ill of others, and I do not believe he was capable of doing anything that would harm another being. He cared too much to intrude where he was not wanted, but his curiosity about people and the loving care with which he approached them broke down reserve to create remarkable friendships. His enthusiasm and good humor made him a welcome guest in the hearts of the hundreds of students and colleagues who shared the opportunity of knowing Allan Cox as a person."
Honjyo, Kohji; Yonemitsu, Kosei; Tsunenari, Shigeyuki
2005-10-01
Five general methods based on rectal temperature and a multiple regression analysis using rectal temperature and non-temperature based postmortem changes were applied to 212 postmortem cases of within 24h postmortem (PM) intervals. Non-temperature based postmortem changes of rigidity, hypostasis and corneal turbidity were numerically categorized and used with rectal temperatures as four statistical variables in the multiple regression analysis. The correlation coefficient values between true and calculated postmortem intervals were 0.78-0.82 in the five general methods based on rectal temperature. The multiple regression analysis produced a multiple correlation coefficient value of 0.89 and according to the error ranges of the PM intervals, 72% of the cases were estimated within the error of +/-1.0 h and 92% within +/-5.0 h. Although assessments of non-temperature based PM changes are mostly subjective and have a wide variation, the present study demonstrated a usefulness of non-temperature based PM changes in the estimation of PM intervals.
Regression-based model of skin diffuse reflectance for skin color analysis
NASA Astrophysics Data System (ADS)
Tsumura, Norimichi; Kawazoe, Daisuke; Nakaguchi, Toshiya; Ojima, Nobutoshi; Miyake, Yoichi
2008-11-01
A simple regression-based model of skin diffuse reflectance is developed based on reflectance samples calculated by Monte Carlo simulation of light transport in a two-layered skin model. This reflectance model includes the values of spectral reflectance in the visible spectra for Japanese women. The modified Lambert Beer law holds in the proposed model with a modified mean free path length in non-linear density space. The averaged RMS and maximum errors of the proposed model were 1.1 and 3.1%, respectively, in the above range.
Pagnini, Francesco; Manzoni, Gian Mauro; Tagliaferri, Aurora; Gibbons, Chris J
2015-08-01
Depression in people with amyotrophic lateral sclerosis, a fatal and progressive neurodegenerative disorder, is a serious issue with important clinical consequences. However, physical impairment may confound the diagnosis when using generic questionnaires. We conducted a comprehensive review of literature. Mean scores from depression questionnaires were meta-regressed on study-level mean time since onset of symptoms. Data from 103 studies (3190 subjects) indicate that the Beck Depression Inventory and, to a lesser degree, the Hospital Anxiety and Depression Scale are influenced by the time since symptom onset, strongly related to physical impairment. Our results suggest that widely used depression scales overestimate depression due to confounding with physical symptoms.
Zhang, Chen; Li, Xiaoming; Su, Shaobing; Hong, Yan; Zhou, Yuejiao; Tang, Zhenzhu; Shen, Zhiyong
2015-01-01
Limited data are available regarding risk factors that are related to intimate partner violence (IPV) against female sex workers (FSWs) in the context of stable partnerships. Out of the 1,022 FSWs, 743 reported ever having a stable partnership and 430 (more than half) of those reported experiencing IPV. Hierarchical multivariate regression revealed that some characteristics of stable partners (e.g., low education, alcohol use) and relationship stressors (e.g., frequent friction, concurrent partnerships) were independently predictive of IPV against FSWs. Public health professionals who design future violence prevention interventions targeting FSWs need to consider the influence of their stable partners.
NASA Astrophysics Data System (ADS)
Vesnin, V. L.; Muradov, V. G.
2012-09-01
Absorption spectra of multicomponent hydrocarbon mixtures based on n-heptane and isooctane with addition of benzene (up to 1%) and toluene and o-xylene (up to 20%) were investigated experimentally in the region of the first overtones of the hydrocarbon groups (λ = 1620-1780 nm). It was shown that their concentrations could be determined separately by using a multiple linear regression method. The optimum result was obtained by including four wavelengths at 1671, 1680, 1685, and 1695 nm, which took into account absorption of CH groups in benzene, toluene, and o-xylene and CH3 groups, respectively.
COX-1 and COX-2 expression in feline oral squamous cell carcinoma.
Hayes, A; Scase, T; Miller, J; Murphy, S; Sparkes, A; Adams, V
2006-01-01
This study demonstrated immunohistochemically the expression of cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) in feline oral squamous cell carcinoma (FOSCC), with primary polyclonal antibodies raised against human epitopes. COX-2 immunolabelling was intracytoplasmic and, in some neoplastic cells, perinuclear; it was demonstrated in a small proportion (< or = 1%) of neoplastic cells and its intensity was usually mild to moderate. In contrast, all neoplastic tissues showed extensive nuclear and cytoplasmic COX-1 immunolabelling. Cytoplasmic COX-1 immunolabelling was less intense than nuclear labelling in neoplastic tissue. In the adjacent histologically normal oral mucosa, COX-2 immunolabelling was absent. The cytoplasmic and nuclear intensity and distribution of COX-1 immunolabelling was significantly higher in neoplastic tissue than in adjacent normal oral mucosa. The results indicate that COX-1 and COX-2 are overexpressed in FOSCC, but the clinical and pathophysiological significance of this finding remains to be determined.
2011-01-01
Background The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004. Methods Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption. Results All the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models. Conclusion These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models. PMID:22082256
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.
2000-01-01
The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.
Ordinal logistic regression analysis on the nutritional status of children in KarangKitri village
NASA Astrophysics Data System (ADS)
Ohyver, Margaretha; Yongharto, Kimmy Octavian
2015-09-01
Ordinal logistic regression is a statistical technique that can be used to describe the relationship between ordinal response variable with one or more independent variables. This method has been used in various fields including in the health field. In this research, ordinal logistic regression is used to describe the relationship between nutritional status of children with age, gender, height, and family status. Nutritional status of children in this research is divided into over nutrition, well nutrition, less nutrition, and malnutrition. The purpose for this research is to describe the characteristics of children in the KarangKitri Village and to determine the factors that influence the nutritional status of children in the KarangKitri village. There are three things that obtained from this research. First, there are still children who are not categorized as well nutritional status. Second, there are children who come from sufficient economic level which include in not normal status. Third, the factors that affect the nutritional level of children are age, family status, and height.
NASA Astrophysics Data System (ADS)
Shortridge, J.; Guikema, S.; Zaitchik, B. F.
2015-12-01
In the past decade, machine-learning methods for empirical rainfall-runoff modeling have seen extensive development. However, the majority of research has focused on a small number of methods, such as artificial neural networks, while not considering other approaches for non-parametric regression that have been developed in recent years. These methods may be able to achieve comparable predictive accuracy to ANN's and more easily provide physical insights into the system of interest through evaluation of covariate influence. Additionally, these methods could provide a straightforward, computationally efficient way of evaluating climate change impacts in basins where data to support physical hydrologic models is limited. In this paper, we use multiple regression and machine-learning approaches to predict monthly streamflow in five highly-seasonal rivers in the highlands of Ethiopia. We find that generalized additive models, random forests, and cubist models achieve better predictive accuracy than ANNs in many basins assessed and are also able to outperform physical models developed for the same region. We discuss some challenges that could hinder the use of such models for climate impact assessment, such as biases resulting from model formulation and prediction under extreme climate conditions, and suggest methods for preventing and addressing these challenges. Finally, we demonstrate how predictor variable influence can be assessed to provide insights into the physical functioning of data-sparse watersheds.
Miguel Cruz, Antonio; Guarín, Mayra R
2017-02-01
To date, there are no broadly accepted or accurate models to determine appropriate staffing [levels] for clinical engineering departments (CEDs). The purpose of this study is to determine what the determinants of the staffing levels are (total number of full time equivalents (FTEs)) in CEDs in healthcare organisations. In doing so, we used a cross-sectional exploratory approach by using a multivariate regression model over a secondary source of data information from the AAMI Benchmarking Solutions-Healthcare Technology Management database. Two hundred and one healthcare organisations were included in our study. Our study revealed that on average, there are almost 14 biomedical technicians (BMETs) per clinical engineer and one FTE per 1083.72 devices (SD 545.69). The results of this study also revealed that the total number of devices and the total technology management hours devoted to these devices positively affects the number of FTEs in a CED, whereas the hospital complexity, measured by healthcare organisation patient discharges matters inversely. The most important factor that matters in the number of FTEs in CEDs was the total technology management hours devoted to devices. A value of explained variance (i.e. R(2)) of 85% was obtained, indicating the strong power of the prediction accuracy of our multivariate regression model.
Yoo, Yun Joo; Sun, Lei; Bull, Shelley B
2013-01-01
Multi-marker methods for genetic association analysis can be performed for common and low frequency SNPs to improve power. Regression models are an intuitive way to formulate multi-marker tests. In previous studies we evaluated regression-based multi-marker tests for common SNPs, and through identification of bins consisting of correlated SNPs, developed a multi-bin linear combination (MLC) test that is a compromise between a 1 df linear combination test and a multi-df global test. Bins of SNPs in high linkage disequilibrium (LD) are identified, and a linear combination of individual SNP statistics is constructed within each bin. Then association with the phenotype is represented by an overall statistic with df as many or few as the number of bins. In this report we evaluate multi-marker tests for SNPs that occur at low frequencies. There are many linear and quadratic multi-marker tests that are suitable for common or low frequency variant analysis. We compared the performance of the MLC tests with various linear and quadratic statistics in joint or marginal regressions. For these comparisons, we performed a simulation study of genotypes and quantitative traits for 85 genes with many low frequency SNPs based on HapMap Phase III. We compared the tests using (1) set of all SNPs in a gene, (2) set of common SNPs in a gene (MAF ≥ 5%), (3) set of low frequency SNPs (1% ≤ MAF < 5%). For different trait models based on low frequency causal SNPs, we found that combined analysis using all SNPs including common and low frequency SNPs is a good and robust choice whereas using common SNPs alone or low frequency SNP alone can lose power. MLC tests performed well in combined analysis except where two low frequency causal SNPs with opposing effects are positively correlated. Overall, across different sets of analysis, the joint regression Wald test showed consistently good performance whereas other statistics including the ones based on marginal regression had lower power for
Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana
2017-02-01
The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism
Zhang, Xiaona; Sun, Xiaoxuan; Wang, Junhong; Tang, Liou; Xie, Anmu
2017-01-01
Rapid eye movement sleep behavior disorder (RBD) is thought to be one of the most frequent preceding symptoms of Parkinson's disease (PD). However, the prevalence of RBD in PD stated in the published studies is still inconsistent. We conducted a meta and meta-regression analysis in this paper to estimate the pooled prevalence. We searched the electronic databases of PubMed, ScienceDirect, EMBASE and EBSCO up to June 2016 for related articles. STATA 12.0 statistics software was used to calculate the available data from each research. The prevalence of RBD in PD patients in each study was combined to a pooled prevalence with a 95 % confidence interval (CI). Subgroup analysis and meta-regression analysis were performed to search for the causes of the heterogeneity. A total of 28 studies with 6869 PD cases were deemed eligible and included in our meta-analysis based on the inclusion and exclusion criteria. The pooled prevalence of RBD in PD was 42.3 % (95 % CI 37.4-47.1 %). In subgroup analysis and meta-regression analysis, we found that the important causes of heterogeneity were the diagnosis criteria of RBD and age of PD patients (P = 0.016, P = 0.019, respectively). The results indicate that nearly half of the PD patients are suffering from RBD. Older age and longer duration are risk factors for RBD in PD. We can use the minimal diagnosis criteria for RBD according to the International Classification of Sleep Disorders to diagnose RBD patients in our daily work if polysomnography is not necessary.
COX-1 vs. COX-2 as a determinant of basal tone in the internal anal sphincter
de Godoy, Márcio A. F.; Rattan, Neeru; Rattan, Satish
2009-01-01
Prostanoids, produced endogenously via cyclooxygenases (COXs), have been implicated in the sustained contraction of different smooth muscles. The two major types of COXs are COX-1 and COX-2. The COX subtype involved in the basal state of the internal anal sphincter (IAS) smooth muscle tone is not known. To identify the COX subtype, we examined the effect of COX-1- and COX-2-selective inhibitors, SC-560 and rofecoxib, respectively, on basal tone in the rat IAS. We also determined the effect of selective deletion of COX-1 and COX-2 genes (COX-1−/− and COX-2−/− mice) on basal tone in murine IAS. Our data show that SC-560 causes significantly more efficacious and potent concentration-dependent decreases in IAS tone than rofecoxib. In support of these data, significantly higher levels of COX-1 than COX-2 mRNA were found in the IAS. In addition, higher levels of COX-1 mRNA and protein were expressed in rat IAS than rectal smooth muscle. In wild-type mice, IAS tone was decreased 41.4 ± 3.4% (mean ± SE) by SC-560 (1 × 10−5 M) and 5.4 ± 2.2% by rofecoxib (P < 0.05, n = 5). Basal tone was 0.172 ± 0.021 mN//mg in the IAS from wild-type mice and significantly less (0.080 ± 0.015 mN/mg) in the IAS from COX-1−/− mice (P < 0.05, n = 5). However, basal tone in COX-2−/− mice was not significantly different from that in wild-type mice. We conclude that COX-1-related products contribute significantly to IAS tone. PMID:19056763
Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A
2014-08-01
Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries.
Mokhtari, Mehdi; Miri, Mohammad; Nikoonahad, Ali; Jalilian, Ali; Naserifar, Razi; Ghaffari, Hamid Reza; Kazembeigi, Farogh
2016-11-01
The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.
iNOS signaling interacts with COX-2 pathway in colonic fibroblasts.
Zhu, Yingting; Zhu, Min; Lance, Peter
2012-10-01
COX-2 and iNOS are two major inflammatory mediators implicated in colorectal inflammation and cancer. Previously, the role of colorectal fibroblasts involved in regulation of COX-2 and iNOS expression was largely ignored. In addition, the combined interaction of COX-2 and iNOS signalings and their significance in the progression of colorectal inflammation and cancer within the fibroblasts have received little investigation. To address those issues, we investigated the role of colonic fibroblasts in the regulation of COX-2 and iNOS gene expression, and explored possible mechanisms of interaction between COX-2 and iNOS signalings using a colonic CCD-18Co fibroblast line and LPS, a potential stimulator of COX-2 and iNOS. Our results clearly demonstrated that LPS activated COX-2 gene expression and enhanced PGE(2) production, stimulated iNOS gene expression and promoted NO production in the fibroblasts. Interestingly, activation of COX-2 signaling by LPS was not involved in activation of iNOS signaling, while activation of iNOS signaling by LPS contributed in part to activation of COX-2 signaling. Further analysis indicated that PKC plays a major role in the activation and interaction of COX-2 and iNOS signalings induced by LPS in the fibroblasts.
Ku80 cooperates with CBP to promote COX-2 expression and tumor growth
Qin, Yu; Xuan, Yang; Jia, Yunlu; Hu, Wenxian; Yu, Wendan; Dai, Meng; Li, Zhenglin; Yi, Canhui; Zhao, Shilei; Li, Mei; Du, Sha; Cheng, Wei; Xiao, Xiangsheng; Chen, Yiming; Wu, Taihua; Meng, Songshu; Yuan, Yuhui; Liu, Quentin; Huang, Wenlin; Guo, Wei; Wang, Shusen; Deng, Wuguo
2015-01-01
Cyclooxygenase-2 (COX-2) plays an important role in lung cancer development and progression. Using streptavidin-agarose pulldown and proteomics assay, we identified and validated Ku80, a dimer of Ku participating in the repair of broken DNA double strands, as a new binding protein of the COX-2 gene promoter. Overexpression of Ku80 up-regulated COX-2 promoter activation and COX-2 expression in lung cancer cells. Silencing of Ku80 by siRNA down-regulated COX-2 expression and inhibited tumor cell growth in vitro and in a xenograft mouse model. Ku80 knockdown suppressed phosphorylation of ERK, resulting in an inactivation of the MAPK pathway. Moreover, CBP, a transcription co-activator, interacted with and acetylated Ku80 to co-regulate the activation of COX-2 promoter. Overexpression of CBP increased Ku80 acetylation, thereby promoting COX-2 expression and cell growth. Suppression of CBP by a CBP-specific inhibitor or siRNA inhibited COX-2 expression as well as tumor cell growth. Tissue microarray immunohistochemical analysis of lung adenocarcinomas revealed a strong positive correlation between levels of Ku80 and COX-2 and clinicopathologic variables. Overexpression of Ku80 was associated with poor prognosis in patients with lung cancers. We conclude that Ku80 promotes COX-2 expression and tumor growth and is a potential therapeutic target in lung cancer. PMID:25797267
Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler
2013-02-01
The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.
NASA Astrophysics Data System (ADS)
Ji, Yanju; Huang, Wanyu; Yu, Mingmei; Guan, Shanshan; Wang, Yuan; Zhu, Yu
2017-01-01
This article studies full-waveform associated identification method of airborne time-domain electromagnetic method (ATEM) 3-d anomalies based on multiple linear regression analysis method. By using convolution algorithm, full-waveform theoretical responses are computed to derive sample library including switch-off-time period responses and off-time period responses. Extract full-waveform attributes from theoretical responses to derive linear regression equations which are used to identify the geological parameters. In order to improve the precision ulteriorly, we optimize the identification method by separating the sample library into different groups and identify the parameter respectively. Performance of full-waveform associated identification method with field data of wire-loop test experiments with ATEM system in Daedao of Changchun proves that the full-waveform associated identification method is feasible practically.
Siordia, Carlos; Saenz, Joseph; Tom, Sarah E.
2014-01-01
Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity—variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes PMID:25414731
[Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].
Vanegas, Jairo; Vásquez, Fabián
2016-12-19
Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008.
Determinants of four functional tasks among older adults: an exploratory regression analysis.
Topp, R; Mikesky, A; Thompson, K
1998-02-01
Functional ability declines in later life. The purpose of this project was to determine if strength, postural control, and joint pain predict performance of four functional tasks among older adults. A sample of 28 older adults completed assessments of strength, postural control, joint pain, and four functional tasks. The duration to complete the functional tasks of: 1) getting out of bed, going to a chair, and then returning to bed; 2) crossing a street and getting onto a bus; 3) exiting the passenger side of a car; and 4) climbing a flight of 27 stairs was recorded. Step-wise regression equations indicated that seated row strength and dynamic postural control were significant predictors of all of the tasks and accounted for the largest proportion of the variance in each equation. These results indicate that measures of physical fitness may be more important predictors of functional tasks among older adults than chronological age.
Kang, Seung-Wan; Byun, Gukdo; Park, Hun-Joon
2014-12-01
This paper presents empirical research into the relationship between leader-follower value congruence in social responsibility and the level of ethical satisfaction for employees in the workplace. 163 dyads were analyzed, each consisting of a team leader and an employee working at a large manufacturing company in South Korea. Following current methodological recommendations for congruence research, polynomial regression and response surface modeling methodologies were used to determine the effects of value congruence. Results indicate that leader-follower value congruence in social responsibility was positively related to the ethical satisfaction of employees. Furthermore, employees' ethical satisfaction was stronger when aligned with a leader with high social responsibility. The theoretical and practical implications are discussed.
Gender roles and binge drinking among Latino emerging adults: a latent class regression analysis.
Vaughan, Ellen L; Wong, Y Joel; Middendorf, Katharine G
2014-09-01
Gender roles are often cited as a culturally specific predictor of drinking among Latino populations. This study used latent class regression to test the relationships between gender roles and binge drinking in a sample of Latino emerging adults. Participants were Latino emerging adults who participated in Wave III of the National Longitudinal Study of Adolescent Health (N = 2,442). A subsample of these participants (n = 660) completed the Bem Sex Role Inventory--Short. We conducted latent class regression using 3 dimensions of gender roles (femininity, social masculinity, and personal masculinity) to predict binge drinking. Results indicated a 3-class solution. In Class 1, the protective personal masculinity class, personal masculinity (e.g., being a leader, defending one's own beliefs) was associated with a reduction in the odds of binge drinking. In Class 2, the nonsignificant class, gender roles were not related to binge drinking. In Class 3, the mixed masculinity class, personal masculinity was associated with a reduction in the odds of binge drinking, whereas social masculinity (e.g., forceful, dominant) was associated with an increase in the odds of binge drinking. Post hoc analyses found that females, those born outside the United States, and those with greater English language usage were at greater odds of being in Class 1 (vs. Class 2). Males, those born outside the United States, and those with greater Spanish language usage were at greater odds of being in Class 3 (vs. Class 2). Directions for future research and implications for practice with Latino emerging adults are discussed.
Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China.
Kong, Chunyu; Yang, Jikuang
2010-07-01
A large number of pedestrian fatalities were reported in China since the 1990s, however the exposure of pedestrians in public traffic has never been measured quantitatively using in-depth accident data. This study aimed to investigate the association between the impact speed and risk of pedestrian casualties in passenger vehicle collisions based on real-world accident cases in China. The cases were selected from a database of in-depth investigation of vehicle accidents in Changsha-IVAC. The sampling criteria were defined as (1) the accident was a frontal impact that occurred between 2003 and 2009; (2) the pedestrian age was above 14; (3) the injury according to the Abbreviated Injury Scale (AIS) was 1+; (4) the accident involved passenger cars, SUVs, or MPVs; and (5) the vehicle impact speed can be determined. The selected IVAC data set, which included 104 pedestrian accident cases, was weighted based on the national traffic accident data. The logistical regression models of the risks for pedestrian fatalities and AIS 3+ injuries were developed in terms of vehicle impact speed using the unweighted and weighted data sets. A multiple logistic regression model on the risk of pedestrian AIS 3+ injury was developed considering the age and impact speed as two variables. It was found that the risk of pedestrian fatality is 26% at 50 km/h, 50% at 58 km/h, and 82% at 70 km/h. At an impact speed of 80 km/h, the pedestrian rarely survives. The weighted risk curves indicated that the risks of pedestrian fatality and injury in China were higher than that in other high-income countries, whereas the risks of pedestrian casualty was lower than in these countries 30 years ago. The findings could have a contribution to better understanding of the exposures of pedestrians in urban traffic in China, and provide background knowledge for the development of strategies for pedestrian protection.
Jung, Su Yon; Vitolins, Mara Z.; Fenton, Jenifer; Frazier-Wood, Alexis C.; Hursting, Stephen D.; Chang, Shine
2015-01-01
Purpose Risk factors for obesity and weight gain are typically evaluated individually while “adjusting for” the influence of other confounding factors, and few studies, if any, have created risk profiles by clustering risk factors. We identified subgroups of postmenopausal women homogeneous in their clustered modifiable and non-modifiable risk factors for gaining ≥ 3% weight. Methods This study included 612 postmenopausal women 50–79 years old, enrolled in an ancillary study of the Women's Health Initiative Observational Study between February 1995 and July 1998. Classification and regression tree and stepwise regression models were built and compared. Results Of 27 selected variables, the factors significantly related to ≥ 3% weight gain were weight change in the past 2 years, age at menopause, dietary fiber, fat, alcohol intake, and smoking. In women younger than 65 years, less than 4 kg weight change in the past 2 years sufficiently reduced risk of ≥ 3% weight gain. Different combinations of risk factors related to weight gain were reported for subgroups of women: women 65 years or older (essential factor: < 9.8 g/day dietary factor), African Americans (essential factor: currently smoking), and white women (essential factor: ≥ 5 kg weight change for the past 2 years). Conclusions Our findings suggest specific characteristics for particular subgroups of postmenopausal women that may be useful for identifying those at risk for weight gain. The study results may be useful for targeting efforts to promote strategies to reduce the risk of obesity and weight gain in subgroups of postmenopausal women and maximize the effect of weight control by decreasing obesity-relevant adverse health outcomes. PMID:25822239
Optimization of Game Formats in U-10 Soccer Using Logistic Regression Analysis
Amatria, Mario; Arana, Javier; Anguera, M. Teresa; Garzón, Belén
2016-01-01
Abstract Small-sided games provide young soccer players with better opportunities to develop their skills and progress as individual and team players. There is, however, little evidence on the effectiveness of different game formats in different age groups, and furthermore, these formats can vary between and even within countries. The Royal Spanish Soccer Association replaced the traditional grassroots 7-a-side format (F-7) with the 8-a-side format (F-8) in the 2011-12 season and the country’s regional federations gradually followed suit. The aim of this observational methodology study was to investigate which of these formats best suited the learning needs of U-10 players transitioning from 5-aside futsal. We built a multiple logistic regression model to predict the success of offensive moves depending on the game format and the area of the pitch in which the move was initiated. Success was defined as a shot at the goal. We also built two simple logistic regression models to evaluate how the game format influenced the acquisition of technicaltactical skills. It was found that the probability of a shot at the goal was higher in F-7 than in F-8 for moves initiated in the Creation Sector-Own Half (0.08 vs 0.07) and the Creation Sector-Opponent's Half (0.18 vs 0.16). The probability was the same (0.04) in the Safety Sector. Children also had more opportunities to control the ball and pass or take a shot in the F-7 format (0.24 vs 0.20), and these were also more likely to be successful in this format (0.28 vs 0.19). PMID:28031768
Chi, Peter; Aras, Radha; Martin, Katie; Favero, Carlita
2016-05-15
Fetal Alcohol Spectrum Disorders (FASD) collectively describes the constellation of effects resulting from human alcohol consumption during pregnancy. Even with public awareness, the incidence of FASD is estimated to be upwards of 5% in the general population and is becoming a global health problem. The physical, cognitive, and behavioral impairments of FASD are recapitulated in animal models. Recently rodent models utilizing voluntary drinking paradigms have been developed that accurately reflect moderate consumption, which makes up the majority of FASD cases. The range in severity of FASD characteristics reflects the frequency, dose, developmental timing, and individual susceptibility to alcohol exposure. As most rodent models of FASD use C57BL/6 mice, there is a need to expand the stocks of mice studied in order to more fully understand the complex neurobiology of this disorder. To that end, we allowed pregnant Swiss Webster mice to voluntarily drink ethanol via the drinking in the dark (DID) paradigm throughout their gestation period. Ethanol exposure did not alter gestational outcomes as determined by no significant differences in maternal weight gain, maternal liquid consumption, litter size, or pup weight at birth or weaning. Despite seemingly normal gestation, ethanol-exposed offspring exhibit significantly altered timing to achieve developmental milestones (surface righting, cliff aversion, and open field traversal), as analyzed through mixed-effects Cox proportional hazards models. These results confirm Swiss Webster mice as a viable option to study the incidence and causes of ethanol-induced neurobehavioral alterations during development. Future studies in our laboratory will investigate the brain regions and molecules responsible for these behavioral changes.
Schümberg, Katharina; Polyakova, Maryna; Steiner, Johann; Schroeter, Matthias L.
2016-01-01
S100B has been linked to glial pathology in several psychiatric disorders. Previous studies found higher S100B serum levels in patients with schizophrenia compared to healthy controls, and a number of covariates influencing the size of this effect have been proposed in the literature. Here, we conducted a meta-analysis and meta-regression analysis on alterations of serum S100B in schizophrenia in comparison with healthy control subjects. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guarantee a high quality and reproducibility. With strict inclusion criteria 19 original studies could be included in the quantitative meta-analysis, comprising a total of 766 patients and 607 healthy control subjects. The meta-analysis confirmed higher values of the glial serum marker S100B in schizophrenia if compared with control subjects. Meta-regression analyses revealed significant effects of illness duration and clinical symptomatology, in particular the total score of the Positive and Negative Syndrome Scale (PANSS), on serum S100B levels in schizophrenia. In sum, results confirm glial pathology in schizophrenia that is modulated by illness duration and related to clinical symptomatology. Further studies are needed to investigate mechanisms and mediating factors related to these findings. PMID:26941608
NASA Technical Reports Server (NTRS)
Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.
1998-01-01
The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.
Schümberg, Katharina; Polyakova, Maryna; Steiner, Johann; Schroeter, Matthias L
2016-01-01
S100B has been linked to glial pathology in several psychiatric disorders. Previous studies found higher S100B serum levels in patients with schizophrenia compared to healthy controls, and a number of covariates influencing the size of this effect have been proposed in the literature. Here, we conducted a meta-analysis and meta-regression analysis on alterations of serum S100B in schizophrenia in comparison with healthy control subjects. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guarantee a high quality and reproducibility. With strict inclusion criteria 19 original studies could be included in the quantitative meta-analysis, comprising a total of 766 patients and 607 healthy control subjects. The meta-analysis confirmed higher values of the glial serum marker S100B in schizophrenia if compared with control subjects. Meta-regression analyses revealed significant effects of illness duration and clinical symptomatology, in particular the total score of the Positive and Negative Syndrome Scale (PANSS), on serum S100B levels in schizophrenia. In sum, results confirm glial pathology in schizophrenia that is modulated by illness duration and related to clinical symptomatology. Further studies are needed to investigate mechanisms and mediating factors related to these findings.
Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.
2013-01-01
Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839
Geographical variation of unmet medical needs in Italy: a multivariate logistic regression analysis
2013-01-01
Background Unmet health needs should be, in theory, a minor issue in Italy where a publicly funded and universally accessible health system exists. This, however, does not seem to be the case. Moreover, in the last two decades responsibilities for health care have been progressively decentralized to regional governments, which have differently organized health service delivery within their territories. Regional decision-making has affected the use of health care services, further increasing the existing geographical disparities in the access to care across the country. This study aims at comparing self-perceived unmet needs across Italian regions and assessing how the reported reasons - grouped into the categories of availability, accessibility and acceptability – vary geographically. Methods Data from the 2006 Italian component of the European Union Statistics on Income and Living Conditions are employed to explore reasons and predictors of self-reported unmet medical needs among 45,175 Italian respondents aged 18 and over. Multivariate logistic regression models are used to determine adjusted rates for overall unmet medical needs and for each of the three categories of reasons. Results Results show that, overall, 6.9% of the Italian population stated having experienced at least one unmet medical need during the last 12 months. The unadjusted rates vary markedly across regions, thus resulting in a clear-cut north–south divide (4.6% in the North-East vs. 10.6% in the South). Among those reporting unmet medical needs, the leading reason was problems of accessibility related to cost or transportation (45.5%), followed by acceptability (26.4%) and availability due to the presence of too long waiting lists (21.4%). In the South, more than one out of two individuals with an unmet need refrained from seeing a physician due to economic reasons. In the northern regions, working and family responsibilities contribute relatively more to the underutilization of medical
Wang, Chong; Sun, Qun; Wahab, Magd Abdel; Zhang, Xingyu; Xu, Limin
2015-09-01
Rotary cup brushes mounted on each side of a road sweeper undertake heavy debris removal tasks but the characteristics have not been well known until recently. A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design. However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system. This study develops a mathematical regression model to summarize the FE modeled results. The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc. The data were then fitted by a multiple variable regression model using the maximum likelihood method. The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. The methodology may also be used in the design and optimization of rotary brush tools.
Zhao, Rui-Na; Zhang, Bo; Yang, Xiao; Jiang, Yu-Xin; Lai, Xing-Jian; Zhang, Xiao-Yan
2015-12-01
The purpose of the study described here was to determine specific characteristics of thyroid microcarcinoma (TMC) and explore the value of contrast-enhanced ultrasound (CEUS) combined with conventional ultrasound (US) in the diagnosis of TMC. Characteristics of 63 patients with TMC and 39 with benign sub-centimeter thyroid nodules were retrospectively analyzed. Multivariate logistic regression analysis was performed to determine independent risk factors. Four variables were included in the logistic regression models: age, shape, blood flow distribution and enhancement pattern. The area under the receiver operating characteristic curve was 0.919. With 0.113 selected as the cutoff value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 90.5%, 82.1%, 89.1%, 84.2% and 87.3%, respectively. Independent risk factors for TMC determined with the combination of CEUS and conventional US were age, shape, blood flow distribution and enhancement pattern. Age was negatively correlated with malignancy, whereas shape, blood flow distribution and enhancement pattern were positively correlated. The logistic regression model involving CEUS and conventional US was found to be effective in the diagnosis of sub-centimeter thyroid nodules.
Cabras, Stefano; Castellanos, Maria Eugenia; Perra, Silvia
2014-11-20
This paper considers the problem of selecting a set of regressors when the response variable is distributed according to a specified parametric model and observations are censored. Under a Bayesian perspective, the most widely used tools are Bayes factors (BFs), which are undefined when improper priors are used. In order to overcome this issue, fractional (FBF) and intrinsic (IBF) BFs have become common tools for model selection. Both depend on the size, Nt , of a minimal training sample (MTS), while the IBF also depends on the specific MTS used. In the case of regression with censored data, the definition of an MTS is problematic because only uncensored data allow to turn the improper prior into a proper posterior and also because full exploration of the space of the MTSs, which includes also censored observations, is needed to avoid bias in model selection. To address this concern, a sequential MTS was proposed, but it has the drawback of an increase of the number of possible MTSs as Nt becomes random. For this reason, we explore the behaviour of the FBF, contextualizing its definition to censored data. We show that these are consistent, providing also the corresponding fractional prior. Finally, a large simulation study and an application to real data are used to compare IBF, FBF and the well-known Bayesian information criterion.
A Vector Approach to Regression Analysis and Its Implications to Heavy-Duty Diesel Emissions
McAdams, H.T.
2001-02-14
An alternative approach is presented for the regression of response data on predictor variables that are not logically or physically separable. The methodology is demonstrated by its application to a data set of heavy-duty diesel emissions. Because of the covariance of fuel properties, it is found advantageous to redefine the predictor variables as vectors, in which the original fuel properties are components, rather than as scalars each involving only a single fuel property. The fuel property vectors are defined in such a way that they are mathematically independent and statistically uncorrelated. Because the available data set does not allow definitive separation of vehicle and fuel effects, and because test fuels used in several of the studies may be unrealistically contrived to break the association of fuel variables, the data set is not considered adequate for development of a full-fledged emission model. Nevertheless, the data clearly show that only a few basic patterns of fuel-property variation affect emissions and that the number of these patterns is considerably less than the number of variables initially thought to be involved. These basic patterns, referred to as ''eigenfuels,'' may reflect blending practice in accordance with their relative weighting in specific circumstances. The methodology is believed to be widely applicable in a variety of contexts. It promises an end to the threat of collinearity and the frustration of attempting, often unrealistically, to separate variables that are inseparable.
The effects of invertebrate herbivores on plant population growth: a meta-regression analysis.
Katz, Daniel S W
2016-09-01
Over the last two decades, an increasing number of studies have quantified the effects of herbivory on plant populations using stage-structured population models and integral projection models, allowing for the calculation of plant population growth rates (λ) with and without herbivory. In this paper, I assembled 29 studies and conducted a meta-regression to determine the importance of invertebrate herbivores to population growth rates (λ) while accounting for missing data. I found that invertebrate herbivory often induced important reductions in plant population growth rates (with herbivory, λ was 1.08 ± 0.36; without herbivory, λ was 1.28 ± 0.58). This relationship tended to be weaker for seed predation than for other types of herbivory, except when seed predation rates were very high. Even so, the amount by which studies reduced herbivory was a poor predictor of differences in population growth rates-which strongly cautions against using measured herbivory rates as a proxy for the impact of herbivores. Herbivory reduced plant population growth rates significantly more when potential growth rates were high, which helps to explain why there was less variation in actual population growth rates than in potential population growth rates. The synthesis of these studies also shows the need for future studies to report variance in estimates of λ and to quantify how λ varies as a function of plant density.
Fasina, Oladiran O.; Eckhardt, Lori G.
2016-01-01
Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e., R2 > 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material. PMID:28003929
Jamali, Jamshid; Roustaei, Narges; Ayatollahi, Seyyed Mohammad Taghi; Sadeghi, Erfan
2015-01-01
Background: Mental health is one of the most important dimensions of life and its quality. Minor Psychiatric Disorder as a type of mental health problem is prevalent among health workers. Nursing is considered to be one of the most stressful occupations. Objectives: This study aimed to evaluate the prevalence of minor psychiatric disorder and its associated factors among nurses in southern Iran. Patients and Methods: A cross-sectional study was carried out on 771 nurses working in 20 cities of Bushehr and Fars provinces in southern Iran. Participants were recruited through multi-stage sampling during 2014. The General Health Questionnaire (GHQ-12) was used for screening of minor psychiatric disorder in nurses. Latent Class Regression was used to analyze the data. Results: The prevalence of minor psychiatric disorder among nurses was estimated to be 27.5%. Gender and sleep disorders were significant factors in determining the level of minor psychiatric disorder (P Values of 0.04 and < 0.001, respectively). Female nurses were 20% more likely than males to be classified into the minor psychiatric disorder group. Conclusions: The results of this study provide information about the prevalence of minor psychiatric disorder among nurses, and factors, which affect the prevalence of such disorders. These findings can be used in strategic planning processes to improve nurses’ mental health. PMID:26339670
Stevens, F. J.; Bobrovnik, S. A.; Biosciences Division; Palladin Inst. Biochemistry
2007-12-01
Physiological responses of the adaptive immune system are polyclonal in nature whether induced by a naturally occurring infection, by vaccination to prevent infection or, in the case of animals, by challenge with antigen to generate reagents of research or commercial significance. The composition of the polyclonal responses is distinct to each individual or animal and changes over time. Differences exist in the affinities of the constituents and their relative proportion of the responsive population. In addition, some of the antibodies bind to different sites on the antigen, whereas other pairs of antibodies are sterically restricted from concurrent interaction with the antigen. Even if generation of a monoclonal antibody is the ultimate goal of a project, the quality of the resulting reagent is ultimately related to the characteristics of the initial immune response. It is probably impossible to quantitatively parse the composition of a polyclonal response to antigen. However, molecular regression allows further parameterization of a polyclonal antiserum in the context of certain simplifying assumptions. The antiserum is described as consisting of two competing populations of high- and low-affinity and unknown relative proportions. This simple model allows the quantitative determination of representative affinities and proportions. These parameters may be of use in evaluating responses to vaccines, to evaluating continuity of antibody production whether in vaccine recipients or animals used for the production of antisera, or in optimizing selection of donors for the production of monoclonal antibodies.
Nicoară, Simona D; Ștefănuţ, Anne C; Nascutzy, Constanta; Zaharie, Gabriela C; Toader, Laura E; Drugan, Tudor C
2016-04-10
BACKGROUND Retinopathy is a serious complication related to prematurity and a leading cause of childhood blindness. The aggressive posterior form of retinopathy of prematurity (APROP) has a worse anatomical and functional outcome following laser therapy, as compared with the classic form of the disease. The main outcome measures are the APROP regression rate, structural outcomes, and complications associated with intravitreal bevacizumab (IVB) versus laser photocoagulation in APROP. MATERIAL AND METHODS This is a retrospective case series that includes infants with APROP who received either IVB or laser photocoagulation and had a follow-up of at least 60 weeks (for the laser photocoagulation group) and 80 weeks (for the IVB group). In the first group, laser photocoagulation of the retina was carried out and in the second group, 1 bevacizumab injection was administered intravitreally. The following parameters were analyzed in each group: sex, gestational age, birth weight, postnatal age and postmenstrual age at treatment, APROP regression, sequelae, and complications. Statistical analysis was performed using Microsoft Excel and IBM SPSS (version 23.0). RESULTS The laser photocoagulation group consisted of 6 premature infants (12 eyes) and the IVB group consisted of 17 premature infants (34 eyes). Within the laser photocoagulation group, the evolution was favorable in 9 eyes (75%) and unfavorable in 3 eyes (25%). Within the IVB group, APROP regressed in 29 eyes (85.29%) and failed to regress in 5 eyes (14.71%). These differences are statistically significant, as proved by the McNemar test (P<0.001). CONCLUSIONS The IVB group had a statistically significant better outcome compared with the laser photocoagulation group, in APROP in our series.
Gerber, Samuel; Rubel, Oliver; Bremer, Peer -Timo; Pascucci, Valerio; Whitaker, Ross T.
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
Gerber, Samuel; Rübel, Oliver; Bremer, Peer-Timo; Pascucci, Valerio; Whitaker, Ross T.
2012-01-01
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduce a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse-Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this paper introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to over-fitting. The Morse-Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse-Smale regression. Supplementary materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse-Smale complex approximation and additional tables for the climate-simulation study. PMID:23687424
Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis.
Liu, K-K; Wang, T; Huang, X-D; Wang, G-L; Xia, Y; Zhang, Y-T; Jing, Q-L; Huang, J-W; Liu, X-X; Lu, J-H; Hu, W-B
2017-02-01
Dengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26-46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84-20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.
LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.
Almquist, Zack W; Butts, Carter T
2014-08-01
Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.
POLINSAR Coherence-Based Regression Analysis of Forest Biomass Using RADARSAT-2 Datasets
NASA Astrophysics Data System (ADS)
Singh, J.; Kumar, S.; Kushwaha, S. P. S.
2014-11-01
Forests play a pivotal role in synchronizing earth's carbon cycle by absorbing carbon from the atmosphere and storing it in the form of biomass. Researchers today are trying to understand the climatic variations, especially those occurring due to destruction of forest and its corresponding biomass loss. Hence, quantification of various forest parameters such as biomass is imperative for evaluating the carbon. The objective of this research was to exploit the potential of C-band Radarsat-2 Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique for analysing the relationship between complex coherence and field-estimated aboveground biomass. Association between the backscatter and the aboveground biomass was also established in the process. To serve our objective, Radarsat-2 interferometric pair dated 4th March, 2013 (master image) and 28th March, 2013 (slave image) were procured for the Barkot Reserve Forest region of Dehradun, India. Field sampling was done for 30 plots (31.62 m x 31.62 m) and stem diameter and tree height were measured in each plot. The study emphasized on the application of POLINSAR coherence instead of using conventional method of relying on backscatter values for retrieving forest biomass. Coherence matrices were utilized for generating complex coherence values for different polarization channels and were regressed against field estimated aboveground biomass. Results indicated a negative linear relationship between complex coherence and aboveground biomass with the cross - polarized coherence showing the highest R2 value of 0.71. Further, the backscatter mechanism when studied with respect to aboveground biomass indicated a positive linear relationship between backscatter values and field estimated aboveground biomass with R2 value of 0.45 and 0.61 for slave and master image respectively. The results suggest that PolInSAR technique, in combination with different modelling approaches, can be adopted for estimating forest
A regression approach to the analysis of serial peak flow among fuel oil ash exposed workers.
Hauser, R; Daskalakis, C; Christiani, D C
1996-10-01
We investigated the association between exposure to fuel oil ash and acute airway obstruction in 31 boilermakers and 31 utility workers during the overhaul of a large oil-fired boiler. Air flow was assessed with self-recorded serial peak expiratory flow rate measurements (PEFR) using a mini-Wright meter. Exposure to thoracic particulates with an aerodynamic diameter of 10 gm or smaller (PM10) was assessed using personal sampling devices and detailed work diaries. All subjects were male, with an average age of 43 yr, and an average of 18 yr at their current trade. Average PM10 exposure on work days was 2.75 mg/m3 for boilermakers and 0.57 mg/m3 for utility workers. Three daily PEFR measurements (start-of-shift, end-of-shift, and bed-time) were analyzed simultaneously, using Huber linear regression. After adjustment for job title, welder status, age, height, smoking, and weld-years, for each mg/m3 increase in PM10, the estimated decline in PEFR was 13.2 L/min (p = 0.008) for end-of-shift, 9.9 L/min (p = 0.045) for bed-time, and 6.6 L/min (p = 0.26) for start-of-shift of the following day. This decline of the exposure effect over the 24-h period that follows was statistically significant (p = 0.004). No other factors were found to significantly modify the effect of exposure. Our results suggest that occupational exposure to fuel oil ash is associated with significant acute decrements in peak flow.
Maternal heavy alcohol use and toddler behavior problems: a fixed effects regression analysis.
Knudsen, Ann Kristin; Ystrom, Eivind; Skogen, Jens Christoffer; Torgersen, Leila
2015-10-01
Using data from the longitudinal Norwegian Mother and Child Cohort Study, the aims of the current study were to examine associations between postnatal maternal heavy alcohol use and toddler behavior problems, taking both observed and unobserved confounding factors into account by employing fixed effects regression models. Postnatal maternal heavy alcohol use (defined as drinking alcohol 4 or more times a week, or drinking 7 units or more per alcohol use episode) and toddler internalizing and externalizing behavior problems were assessed when the toddlers were aged 18 and 36 months. Maternal psychopathology, civil status and negative life events last year were included as time-variant covariates. Maternal heavy alcohol use was associated with toddler internalizing and externalizing behavior problems (p < 0.001) in the population when examined with generalized estimating equation models. The associations disappeared when observed and unobserved sources of confounding were taken into account in the fixed effects models [(p = 0.909 for externalizing behaviors (b = 0.002, SE = 0.021), p = 0.928 for internalizing behaviors (b = 0.002, SE = 0.023)], with an even further reduction of the estimates with the inclusion of time-variant confounders. No causal effect was found between postnatal maternal heavy alcohol use and toddler behavior problems. Increased levels of behavior problems among toddlers of heavy drinking mothers should therefore be attributed to other adverse characteristics associated with these mothers, toddlers and families. This should be taken into account when interventions aimed at at-risk families identified by maternal heavy alcohol use are planned and conducted.
Boruah, Deb K; Dhingani, Dhaval D; Achar, Sashidhar; Prakash, Arjun; Augustine, Antony; Sanyal, Shantiranjan; Gogoi, Manoj; Mahanta, Kangkana
2016-01-01
Objective: The aim of this study was to evaluate the magnetic resonance imaging (MRI) findings of caudal regression syndrome (CRS) and concomitant anomalies in pediatric patients. Materials and Methods: A hospital-based cross-sectional retrospective study was conducted. The study group comprised 21 pediatric patients presenting to the Departments of Radiodiagnosis and Pediatric Surgery in a tertiary care hospital from May 2011 to April 2016. All patients were initially evaluated clinically followed by MRI. Results: In our study, 21 pediatric patients were diagnosed with sacral agenesis/dysgenesis related to CRS. According to the Pang's classification, 2 (9.5%) patients were Type I, 5 (23.8%) patients were Type III, 7 (33.3%) patients were Type IV, and 7 (33.3%) patients were of Type V CRS. Clinically, 17 (81%) patients presented with urinary incontinence, 6 (28.6%) with fecal incontinence, 9 patients (42.9%) had poor gluteal musculatures and shallow intergluteal cleft, 7 (33.3%) patients had associated subcutaneous mass over spine, and 6 (28.6%) patients presented with distal leg muscle atrophy. MRI showed wedge-shaped conus termination in 5 (23.8%) patients and bulbous conus termination in 3 (14.3%) patients above the L1 vertebral level falling into Group 1 CRS while 7 (33.3%) patients had tethered cord and 6 (28.6%) patients had stretched conus falling into Group 2 CRS. Conclusion: MRI is the ideal modality for detailed evaluation of the status of the vertebra, spinal cord, intra- and extra-dural lesions and helps in early diagnosis, detailed preoperative MRI evaluation and assessing concomitant anomalies and guiding further management with early institution of treatment to maximize recovery. PMID:27833778
LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS
Almquist, Zack W.; Butts, Carter T.
2015-01-01
Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218
Duchesne, Thierry; Fortin, Daniel
2017-01-01
Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14–450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to
Naumann, H D; Tedeschi, L O; Fonseca, M A
2015-11-01
Methane (CH) is a potent greenhouse gas that is normally produced by microbial fermentation in the rumen and released to the environment mainly during eructation. Prediction of ruminal CH production is important for ruminant nutrition, especially for the determination of ME intake to assess the amount of total GE available for metabolism by an animal. Equations have been developed to predict ruminal CH production based on dietary constituents, but none have considered condensed tannins (CT), which are known to impact CH production by ruminants. The objective was to develop an equation to predict ruminal CH, accounting for CT effects. Methane production data were acquired from 48-h in vitro fermentation of a diverse group of warm-season perennial forage legumes containing different concentrations of CT over the course of 3 yr ( = 113). The following nonlinear exponential decay regression equation was developed: CH₄ = 113.6 × exp (-0.1751 x CT) - 2.18), [corrected] in which CH is expressed in grams per kilogram of fermentable organic matter and CT is in percentage of the DM. This equation predicted that CH production could be reduced by approximately 50% when CT is 3.9% DM. This equation is likely more accurate when screening CT-containing forages for their potential ability to mitigate in vitro CH production by ruminants when the CT concentration is greater than 3% DM. Therefore, despite the degree of variability in ruminal CH production, this equation could be used as a tool for screening CT-containing forages for their potential to inhibit ruminal CH. Future research should focus on the development of predictive equations when other potential reducers of ruminal CH are used in conjunction with CT.
Binary logistic regression analysis of hard palate dimensions for sexing human crania
Asif, Muhammed; Shetty, Radhakrishna; Avadhani, Ramakrishna
2016-01-01
Sex determination is the preliminary step in every forensic investigation and the hard palate assumes significance in cranial sexing in cases involving burns and explosions due to its resistant nature and secluded location. This study analyzes the sexing potential of incisive foramen to posterior nasal spine length, palatine process of maxilla length, horizontal plate of palatine bone length and transverse length between the greater palatine foramina. The study deviates from the conventional method of measuring the maxillo-alveolar length and breadth as the dimensions considered in this study are more heat resistant and useful in situations with damaged alveolar margins. The study involves 50 male and 50 female adult dry skulls of Indian ethnic group. The dimensions measured were statistically analyzed using Student's t test, binary logistic regression and receiver operating characteristic curve. It was observed that the incisive foramen to posterior nasal spine length is a definite sex marker with sex predictability of 87.2%. The palatine process of maxilla length with 66.8% sex predictability and the horizontal plate of palatine bone length with 71.9% sex predictability cannot be relied upon as definite sex markers. The transverse length between the greater palatine foramina is statistically insignificant in sexing crania (P=0.318). Considering a significant overlap of values in both the sexes the palatal dimensions singularly cannot be relied upon for sexing. Nevertheless, considering the high sex predictability of incisive foramen to posterior nasal spine length this dimension can definitely be used to supplement other sexing evidence available to precisely conclude the cranial sex. PMID:27382518
NASA Astrophysics Data System (ADS)
Kügler, S. D.; Polsterer, K.; Hoecker, M.
2015-04-01
Context. In astronomy, new approaches to process and analyze the exponentially increasing amount of data are inevitable. For spectra, such as in the Sloan Digital Sky Survey spectral database, usually templates of well-known classes are used for classification. In case the fitting of a template fails, wrong spectral properties (e.g. redshift) are derived. Validation of the derived properties is the key to understand the caveats of the template-based method. Aims: In this paper we present a method for statistically computing the redshift z based on a similarity approach. This allows us to determine redshifts in spectra for emission and absorption features without using any predefined model. Additionally, we show how to determine the redshift based on single features. As a consequence we are, for example, able to filter objects that show multiple redshift components. Methods: The redshift calculation is performed by comparing predefined regions in the spectra and individually applying a nearest neighbor regression model to each predefined emission and absorption region. Results: The choice of the model parameters controls the quality and the completeness of the redshifts. For ≈90% of the analyzed 16 000 spectra of our reference and test sample, a certain redshift can be computed that is comparable to the completeness of SDSS (96%). The redshift calculation yields a precision for every individually tested feature that is comparable to the overall precision of the redshifts of SDSS. Using the new method to compute redshifts, we could also identify 14 spectra with a significant shift between emission and absorption or between emission and emission lines. The results already show the immense power of this simple machine-learning approach for investigating huge databases such as the SDSS.
Ghasemi, Jahan B; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
NASA Astrophysics Data System (ADS)
Ghasemi, Jahan B.; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520 nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
Schmid, Matthias; Wickler, Florian; Maloney, Kelly O.; Mitchell, Richard; Fenske, Nora; Mayr, Andreas
2013-01-01
Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures. PMID:23626706
Uchida, Koji
2017-02-09
Cyclooxygenase-2 (COX-2), an inducible isoform responsible for high levels of prostaglandin (PG) production during inflammation and immune responses, mediate a variety of biological actions involved in vascular pathophysiology. COX-2 is induced by various stimuli, including proinflammatory cytokines, to result in PG synthesis associated with inflammation and carcinogenesis. 4-Hydroxy-2-nonenal (HNE) is one of a group of small molecules that can induce COX-2 expression. The mechanistic studies have revealed that the HNE-induced COX-2 expression results from the stabilization of COX-2 mRNA mediated by the p38 mitogen-activated protein kinase signaling pathway and uniquely requires a serum component, which is eventually identified to be modified low-density lipoproteins (LDLs), such as the oxidized form of LDLs. It has also been shown that HNE-induced COX-2 expression is mechanistically linked to the expression of transcription factor p53 and that the overexpression of COX-2 is associated with down-regulation of a proteasome subunit, leading to the enhanced accumulation of p53 and ubiquitinated proteins and to the enhanced sensitivity toward HNE. Thus, the overall mechanism and pathophysiological role of the COX-2 induction by HNE have become increasingly evident.
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes.
ERIC Educational Resources Information Center
Berenson, Mark L.
2013-01-01
There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient…
Olaya-Abril, Alfonso; Parras-Alcántara, Luis; Lozano-García, Beatriz; Obregón-Romero, Rafael
2017-03-15
Over time, the interest on soil studies has increased due to its role in carbon sequestration in terrestrial ecosystems, which could contribute to decreasing atmospheric CO2 rates. In many studies, independent variables were related to soil organic carbon (SOC) alone, however, the contribution degree of each variable with the experimentally determined SOC content were not considered. In this study, samples from 612 soil profiles were obtained in a natural protected (Red Natura 2000) of Sierra Morena (Mediterranean area, South Spain), considering only the topsoil 0-25cm, for better comparison between results. 24 independent variables were used to define it relationship with SOC content. Subsequently, using a multiple linear regression analysis, the effects of these variables on the SOC correlation was considered. Finally, the best parameters determined with the regression analysis were used in a climatic change scenario. The model indicated that SOC in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%) and annual average temperature (1.3%). When the current and future situations were compared, the SOC content in the study area was reduced a 35.4%, and a trend towards migration to higher latitude and altitude was observed.
NASA Astrophysics Data System (ADS)
Katpatal, Y. B.; Paranjpe, S. V.; Kadu, M.
2014-12-01
Effective Watershed management requires authentic data of surface runoff potential for which several methods and models are in use. Generally, non availability of field data calls for techniques based on remote observations. Soil Conservation Services Curve Number (SCS CN) method is an important method which utilizes information generated from remote sensing for estimation of runoff. Several attempts have been made to validate the runoff values generated from SCS CN method by comparing the results obtained from other methods. In the present study, runoff estimation through SCS CN method has been performed using IRS LISS IV data for the Venna Basin situated in the Central India. The field data was available for Venna Basin. The Land use/land cover and soil layers have been generated for the entire watershed using the satellite data and Geographic Information System (GIS). The Venna basin have been divided into intercepted catchment and free catchment. Run off values have been estimated using field data through regression analysis. The runoff values estimated using SCS CN method have been compared with yield values generated using data collected from the tank gauge stations and data from the discharge stations. The correlation helps in validation of the results obtained from the SCS CN method and its applicability in Indian conditions. Key Words: SCS CN Method, Regression Analysis, Land Use / Land cover, Runoff, Remote Sensing, GIS.
Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil
2015-12-07
High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.
Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen
2016-02-01
Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.
Leporace, Gustavo; Batista, Luiz Alberto; Muniz, Adriane M; Zeitoune, Gabriel; Luciano, Thiago; Metsavaht, Leonardo; Nadal, Jurandir
2012-01-01
The aim of this study was to compare the knee kinematics of anterior cruciate ligament reconstructed (ACL-R) and healthy subjects (CG) during gait and classify the status of normality. Ten healthy and six ACL-R subjects had their gait analyzed at 60 fps. 3D knee angles were calculated and inserted into three separate matrices used to perform the principal component (PC) analysis. The scores of PCs retained in each analysis were used to calculate the standard distances (SD) of each participant in relation to the center of the CG. The PC scores of the three planes were used in a logistic regression to define normality. In the sagittal plane there was no difference between groups. In the frontal and transverse planes ACL-R subjects showed higher SD values than CG. PCs identified that ACL-R subjects showed increased adduction, internal and external rotation. All these subjects had their gait classified as abnormal by logistic regression. Therefore, in the studied ACL-R subjects the gait pattern did not return to normal levels after surgery. This may lead to degenerative injuries, as osteoarthritis, in the future.
Haug, Thomas; Nordgreen, Tine; Öst, Lars Göran; Havik, Odd E
2012-07-01
Self-help treatments have the potential to increase the availability and affordability of evidence-based treatments for anxiety disorders. Although promising, previous research results are heterogeneous, indicating a need to identify factors that moderate treatment outcome. The present article reviews the literature on self-help treatment for anxiety disorders among adults, with a total sample of 56 articles with 82 comparisons. When self-help treatment was compared to wait-list or placebo, a meta-analysis indicated a moderate to large effect size (g=0.78). When self-help treatment was compared to face-to-face treatment, results indicated a small effect that favored the latter (g=-0.20). When self-help was compared to wait-list or placebo, subgroup analyses indicated that self-help treatment format, primary anxiety diagnosis and procedures for recruitment of subjects were related to treatment outcome in bivariate analyses, but only recruitment procedures remained significant in a multiple meta-regression analysis. When self-help was compared to face-to-face treatment, a multiple meta-regression indicated that the type of comparison group, treatment format and gender were significantly related to outcome. We conclude that self-help is effective in the treatment of anxiety disorders, and should be offered as part of stepped care treatment models in community services. Implications of the results and future directions are discussed.
Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil
2015-01-01
High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190
Smith, Fraser M.; Reynolds, John V. . E-mail: reynoldsjv@stjames.ie; Kay, Elaine W.; Crotty, Paul; Murphy, James O.; Hollywood, Donal; Gaffney, Eoin F.; Stephens, Richard B.; Kennedy, M. John
2006-02-01
Purpose: To determine the utility of COX-2 expression as a response predictor for patients with rectal cancer who are undergoing neoadjuvant radiochemotherapy (RCT). Methods and Materials: Pretreatment biopsies (PTB) from 49 patients who underwent RCT were included. COX-2 and proliferation in PTB were assessed by immunohistochemistry (IHC) and apoptosis was detected by TUNEL stain. Response to treatment was assessed by a 5-point tumor-regression grade (TRG) based on the ratio of residual tumor to fibrosis. Results: Good response (TRG 1 + 2), moderate response (TRG 3), and poor response (TRG 4 + 5) were seen in 21 patients (42%), 11 patients (22%), and 17 patients (34%), respectively. Patients with COX-2 overexpression in PTB were more likely to demonstrate moderate or poor response (TRG 3 + 4) to treatment than were those with normal COX-2 expression (p = 0.026, chi-square test). Similarly, poor response was more likely if patients had low levels of spontaneous apoptosis in PTBs (p = 0.0007, chi-square test). Conclusions: COX-2 overexpression and reduced apoptosis in PTB can predict poor response of rectal cancer to RCT. As COX-2 inhibitors are commercially available, their administration to patients who overexpress COX-2 warrants assessment in clinical trials in an attempt to increase overall response rates.
Stepwise Regression Analysis of MDOE Balance Calibration Data Acquired at DNW
NASA Technical Reports Server (NTRS)
DeLoach, RIchard; Philipsen, Iwan
2007-01-01
This paper reports a comparison of two experiment design methods applied in the calibration of a strain-gage balance. One features a 734-point test matrix in which loads are varied systematically according to a method commonly applied in aerospace research and known in the literature of experiment design as One Factor At a Time (OFAT) testing. Two variations of an alternative experiment design were also executed on the same balance, each with different features of an MDOE experiment design. The Modern Design of Experiments (MDOE) is an integrated process of experiment design, execution, and analysis applied at NASA's Langley Research Center to achieve significant reductions in cycle time, direct operating cost, and experimental uncertainty in aerospace research generally and in balance calibration experiments specifically. Personnel in the Instrumentation and Controls Department of the German Dutch Wind Tunnels (DNW) have applied MDOE methods to evaluate them in the calibration of a balance using an automated calibration machine. The data have been sent to Langley Research Center for analysis and comparison. This paper reports key findings from this analysis. The chief result is that a 100-point calibration exploiting MDOE principles delivered quality comparable to a 700+ point OFAT calibration with significantly reduced cycle time and attendant savings in direct and indirect costs. While the DNW test matrices implemented key MDOE principles and produced excellent results, additional MDOE concepts implemented in balance calibrations at Langley Research Center are also identified and described.
Shimada, M; Yamada, Y; Itoh, M; Yatagai, T
2001-09-01
Measurement of melanin and blood concentration in human skin is needed in the medical and the cosmetic fields because human skin colour is mainly determined by the colours of melanin and blood. It is difficult to measure these concentrations in human skin because skin has a multi-layered structure and scatters light strongly throughout the visible spectrum. The Monte Carlo simulation currently used for the analysis of skin colour requires long calculation times and knowledge of the specific optical properties of each skin layer. A regression analysis based on the modified Beer-Lambert law is presented as a method of measuring melanin and blood concentration in human skin in a shorter period of time and with fewer calculations. The accuracy of this method is assessed using Monte Carlo simulations.
NASA Astrophysics Data System (ADS)
Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.
2001-09-01
Measurement of melanin and blood concentration in human skin is needed in the medical and the cosmetic fields because human skin colour is mainly determined by the colours of melanin and blood. It is difficult to measure these concentrations in human skin because skin has a multi-layered structure and scatters light strongly throughout the visible spectrum. The Monte Carlo simulation currently used for the analysis of skin colour requires long calculation times and knowledge of the specific optical properties of each skin layer. A regression analysis based on the modified Beer-Lambert law is presented as a method of measuring melanin and blood concentration in human skin in a shorter period of time and with fewer calculations. The accuracy of this method is assessed using Monte Carlo simulations.
Immunohistochemical evaluation of COX-1 and COX-2 expression in keloid and hypertrophic scar.
Abdou, Asmaa G; Maraee, Alaa H; Saif, Hala F Abd-Elsattar
2014-04-01
Both keloids (KLs) and hypertrophic scars (HSs) are considered as dermal fibroproliferative diseases that differ clinically and histopathologically. Although several factors have been postulated in the etiopathogenesis of these conditions, there has been growing evidence to suggest the role of COXs in the pathogenesis of abnormal wound healing because of the reduction of formation of KL and HS in patients using nonsteroidal anti-inflammatory drugs and a COX-2 inhibitor. The aim of the present work is to evaluate the pattern and localization of COX-1 and COX-2 expression in KL and HS compared with surgical scars. COX-1 and COX-2 were analyzed on skin biopsies of 30 patients who presented with KL (15) and HS (15) and 10 normal surgical scars (controls). Both COX-1 and COX-2 were expressed not only in dermal components (fibroblasts, inflammatory cells, and endothelial cells) but also in keratinocytes of the overlying epidermis in the different studied scar lesions. The percentage of COX-1 expression increased progressively from surgical scar (40%) to HS (53.3%) to KL (100%) with a statistically significant difference (P = 0.002). COX-2 was expressed in 100% of surgical scars, 73.3% of HS and 86.7% of KL with the absence of significant differences (P > 0.05). The significant difference in COX-1 expression between HS and KL may refer to the presence of different pathways for the emergence of these diseases. The expression of COX-2 in all scars (normal or abnormal) indicates its active role as an inflammatory mediator. Keratinocytes play an active role in induction of scarring by up-regulation of inflammatory mediators, such as COX-1 and COX-2.
Levy, Jonathan I; Clougherty, Jane E; Baxter, Lisa K; Houseman, E Andres; Paciorek, Christopher J
2010-12-01
Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential concentrations of multiple traffic-related air pollutants within an urban area, based on a combination of central site monitoring data; geographic information system (GIS) covariates reflecting traffic and other outdoor sources; questionnaire data reflecting indoor sources and activities that affect ventilation rates; and factor-analytic methods to better infer source contributions. As part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and/or outdoor 3-to-4 day samples of nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter or = 2.5 pm (PM2.5) at 44 residences during multiple seasons of the year from 2003 through 2005. We performed reflectance analysis, x-ray fluorescence spectroscopy (XRF), and high-resolution inductively coupled plasma-mass spectrometry (ICP-MS) on particle filters to estimate the concentrations of elemental carbon (EC), trace elements, and water-soluble metals, respectively. We derived
Garcia, Mario; Li, Wen-Whai; Yang, Hongling; Amaya, Maria A.; Myers, Orrin; Burchiel, Scott W.; Berwick, Marianne; Pingitore, Nicholas E.
2012-01-01
The use of land-use regression (LUR) techniques for modeling small-scale variations of intraurban air pollution has been increasing in the last decade. The most appealing feature of LUR techniques is the economical monitoring requirements. In this study, principal component analysis (PCA) was employed to optimize an LUR model for PM2.5. The PM2.5 monitoring network consisted of 13 sites, which constrained the regression model to a maximum of one independent variable. An optimized surrogate of vehicle emissions was produced by PCA and employed as the predictor variable in the model. The vehicle emissions surrogate consisted of a linear combination of several traffic variables (e.g., vehicle miles traveled, speed, traffic demand, road length, and time) obtained from a road network used for traffic modeling. The vehicle-emissions surrogate produced by the PCA had a predictive capacity greater (R2 = .458) than the traffic variable, Traffic Demand summarized for a 1 km buffer, with best predictive capacity (R2 = .341). The PCA-based method employed in this study was effective at increasing the fit of an ordinary LUR model by optimizing the utilization of a PM2.5 dataset from small-n monitoring network. In general, the method used can contribute to LUR techniques in two major ways: 1) by improving the predictive power of the input variable, by substituting a principal component for a single variable and 2) by creating an orthogonal set of predictor variables, and thus fulfilling the no colinearity assumption of the linear regression methods. The proposed PCA method, should be universally applicable to LUR methods and will expand their economical attractiveness. PMID:22464030
Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes
NASA Astrophysics Data System (ADS)
Zhang, Rong; Zou, Yong; Zhou, Jie; Gao, Zhong-Ke; Guan, Shuguang
2017-01-01
Visibility graph (VG) and horizontal visibility graph (HVG) play a crucial role in modern complex network approaches to nonlinear time series analysis. However, depending on the underlying dynamic processes, it remains to characterize the exponents of presumably exponential degree distributions. It has been recently conjectured that there is a critical value of exponent λc = ln 3 / 2 , which separates chaotic from correlated stochastic processes. Here, we systematically apply (H)VG analysis to time series from autoregressive (AR) models, which confirms the hypothesis that an increased correlation length results in larger values of λ > λc. On the other hand, we numerically find a regime of negatively correlated process increments where λ < λc, which is in contrast to this hypothesis. Furthermore, by constructing graphs based on re-sampled time series, we find that network measures show non-trivial dependencies on the autocorrelation functions of the processes. We propose to choose the decorrelation time as the maximal re-sampling delay for the algorithm. Our results are detailed for time series from AR(1) and AR(2) processes.
Salem, Rany M.; O'Connor, Daniel T.
2010-01-01
Most, if not all, human phenotypes exhibit a temporal, dosage-dependent, or age effect. Despite this fact, it is rare that data are collected over time or in sequence in relevant studies of the determinants of these phenotypes. The costs and organizational sophistication necessary to collect repeated measurements or longitudinal data for a given phenotype are clearly impediments to this, but greater efforts in this area are needed if insights into human phenotypic expression are to be obtained. Appropriate data analysis methods for genetic association studies involving repeated or longitudinal measures are also needed. We consider the use of longitudinal profiles obtained from fitted functions on repeated data collections from a set of individuals whose similarities are contrasted between sets of individuals with different genotypes to test hypotheses about genetic influences on time-dependent phenotype expression. The proposed approach can accommodate uncertainty of the fitted functions, as well as weighting factors across the time points, and is easily extended to a wide variety of complex analysis settings. We showcase the proposed approach with data from a clinical study investigating human blood vessel response to tyramine. We also compare the proposed approach with standard analytic procedures and investigate its robustness and power via simulation studies. The proposed approach is found to be quite flexible and performs either as well or better than traditional statistical methods. PMID:20423962
NASA Technical Reports Server (NTRS)
Gohil, B. S.; Hariharan, T. A.; Sharma, A. K.; Pandey, P. C.
1982-01-01
The 19.35 GHz and 22.235 GHz passive microwave radiometers (SAMIR) on board the Indian satellite Bhaskara have provided very useful data. From these data has been demonstrated the feasibility of deriving atmospheric and ocean surface parameters such as water vapor content, liquid water content, rainfall rate and ocean surface winds. Different approaches have been tried for deriving the atmospheric water content. The statistical and empirical methods have been used by others for the analysis of the Nimbus data. A simulation technique has been attempted for the first time for 19.35 GHz and 22.235 GHz radiometer data. The results obtained from three different methods are compared with radiosonde data. A case study of a tropical depression has been undertaken to demonstrate the capability of Bhaskara SAMIR data to show the variation of total water vapor and liquid water contents.
Lesterhuis, W. Joost; Rinaldi, Catherine; Jones, Anya; Rozali, Esdy N.; Dick, Ian M.; Khong, Andrea; Boon, Louis; Robinson, Bruce W.; Nowak, Anna K.; Bosco, Anthony; Lake, Richard A.
2015-01-01
Cancer immunotherapy has shown impressive results, but most patients do not respond. We hypothesized that the effector response in the tumour could be visualized as a complex network of interacting gene products and that by mapping this network we could predict effective pharmacological interventions. Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade. Network analysis of gene expression profiling data from responding versus non-responding tumours was employed to identify modules associated with response. Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model. Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets. PMID:26193793
Jiang, Wei; Xu, Chao-Zhen; Jiang, Si-Zhi; Zhang, Tang-Duo; Wang, Shi-Zhen; Fang, Bai-Shan
2017-04-01
L-tert-Leucine (L-Tle) and its derivatives are extensively used as crucial building blocks for chiral auxiliaries, pharmaceutically active ingredients, and ligands. Combining with formate dehydrogenase (FDH) for regenerating the expensive coenzyme NADH, leucine dehydrogenase (LeuDH) is continually used for synthesizing L-Tle from α-keto acid. A multilevel factorial experimental design was executed for research of this system. In this work, an efficient optimization method for improving the productivity of L-Tle was developed. And the mathematical model between different fermentation conditions and L-Tle yield was also determined in the form of the equation by using uniform design and regression analysis. The multivariate regression equation was conveniently implemented in water, with a space time yield of 505.9 g L(-1) day(-1) and an enantiomeric excess value of >99 %. These results demonstrated that this method might become an ideal protocol for industrial production of chiral compounds and unnatural amino acids such as chiral drug intermediates.
Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui
2016-03-21
Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.
Jansson, Bruce S; Nyamathi, Adeline; Heidemann, Gretchen; Duan, Lei; Kaplan, Charles
2015-01-01
Although literature documents the need for hospital social workers, nurses, and medical residents to engage in patient advocacy, little information exists about what predicts the extent they do so. This study aims to identify predictors of health professionals' patient advocacy engagement with respect to a broad range of patients' problems. A cross-sectional research design was employed with a sample of 94 social workers, 97 nurses, and 104 medical residents recruited from eight hospitals in Los Angeles. Bivariate correlations explored whether seven scales (Patient Advocacy Eagerness, Ethical Commitment, Skills, Tangible Support, Organizational Receptivity, Belief Other Professionals Engage, and Belief the Hospital Empowers Patients) were associated with patient advocacy engagement, measured by the validated Patient Advocacy Engagement Scale. Regression analysis examined whether these scales, when controlling for sociodemographic and setting variables, predicted patient advocacy engagement. While all seven predictor scales were significantly associated with patient advocacy engagement in correlational analyses, only Eagerness, Skills, and Belief the Hospital Empowers Patients predicted patient advocacy engagement in regression analyses. Additionally, younger professionals engaged in higher levels of patient advocacy than older professionals, and social workers engaged in greater patient advocacy than nurses. Limitations and the utility of these findings for acute-care hospitals are discussed.
Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão
2016-02-15
Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years.
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.
Wang, Shuang; Zhang, Yuchen; Dai, Wenrui; Lauter, Kristin; Kim, Miran; Tang, Yuzhe; Xiong, Hongkai; Jiang, Xiaoqian
2016-01-01
Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual’s privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. Availability and implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ Contact: shw070@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26446135
NASA Astrophysics Data System (ADS)
Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui
2016-03-01
Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.
Mocking, R J T; Harmsen, I; Assies, J; Koeter, M W J; Ruhé, H G; Schene, A H
2016-03-15
Omega-3 polyunsaturated fatty acid (PUFA) supplementation has been proposed as (adjuvant) treatment for major depressive disorder (MDD). In the present meta-analysis, we pooled randomized placebo-controlled trials assessing the effects of omega-3 PUFA supplementation on depressive symptoms in MDD. Moreover, we performed meta-regression to test whether supplementation effects depended on eicosapentaenoic acid (EPA) or docosahexaenoic acid dose, their ratio, study duration, participants' age, percentage antidepressant users, baseline MDD symptom severity, publication year and study quality. To limit heterogeneity, we only included studies in adult patients with MDD assessed using standardized clinical interviews, and excluded studies that specifically studied perinatal/perimenopausal or comorbid MDD. Our PubMED/EMBASE search resulted in 1955 articles, from which we included 13 studies providing 1233 participants. After taking potential publication bias into account, meta-analysis showed an overall beneficial effect of omega-3 PUFAs on depressive symptoms in MDD (standardized mean difference=0.398 (0.114-0.682), P=0.006, random-effects model). As an explanation for significant heterogeneity (I(2)=73.36, P<0.001), meta-regression showed that higher EPA dose (β=0.00037 (0.00009-0.00065), P=0.009), higher percentage antidepressant users (β=0.0058 (0.00017-0.01144), P=0.044) and earlier publication year (β=-0.0735 (-0.143 to 0.004), P=0.04) were significantly associated with better outcome for PUFA supplementation. Additional sensitivity analyses were performed. In conclusion, present meta-analysis suggested a beneficial overall effect of omega-3 PUFA supplementation in MDD patients, especially for higher doses of EPA and in participants taking antidepressants. Future precision medicine trials should establish whether possible interactions between EPA and antidepressants could provide targets to improve antidepressant response and its prediction. Furthermore, potential
Koizumi, Itsuro; Yamamoto, Shoichiro; Maekawa, Koji
2006-10-01
Isolation by distance is usually tested by the correlation of genetic and geographic distances separating all pairwise populations' combinations. However, this method can be significantly biased by only a few highly diverged populations and lose the information of individual population. To detect outlier populations and investigate the relative strengths of gene flow and genetic drift for each population, we propose a decomposed pairwise regression analysis. This analysis was applied to the well-described one-dimensional stepping-stone system of stream-dwelling Dolly Varden charr (Salvelinus malma). When genetic and geographic distances were plotted for all pairs of 17 tributary populations, the correlation was significant but weak (r(2) = 0.184). Seven outlier populations were determined based on the systematic bias of the regression residuals, followed by Akaike's information criteria. The best model, 10 populations included, showed a strong pattern of isolation by distance (r(2) = 0.758), suggesting equilibrium between gene flow and genetic drift in these populations. Each outlier population was also analysed by plotting pairwise genetic and geographic distances against the 10 nonoutlier populations, and categorized into one of the three patterns: strong genetic drift, genetic drift with a limited gene flow and a high level of gene flow. These classifications were generally consistent with a priori predictions for each population (physical barrier, population size, anthropogenic impacts). Combined the genetic analysis with field observations, Dolly Varden in this river appeared to form a mainland-island or source-sink metapopulation structure. The generality of the method will merit many types of spatial genetic analyses.
Regression Analysis of Long-term Profile Ozone Data Set from BUV Instruments
NASA Technical Reports Server (NTRS)
Frith, Stacey; Taylor, Steve; DeLand, Matt; Ahn, Chang-Woo; Stolarski, Richard S.
2005-01-01
We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9,11, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUV instrument, which operated from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established from comparisons with SSBUV shuttle flight data. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. We use statistical time-series analysis of the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. The Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. We extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the statistics of the residuals from the fit for the Nimbus 4 period to those obtained from the 1978-2003 period over which the statistical model coefficients were estimated.
De la Cruz, Rolando; Meza, Cristian; Arribas-Gil, Ana; Carroll, Raymond J.
2016-01-01
Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification. PMID:27274601
Cao, J; Hosler, J; Shapleigh, J; Revzin, A; Ferguson-Miller, S
1992-12-05
The coxII/coxIII operon of Rhodobacter sphaeroides cytochrome c oxidase has been sequenced and characterized by insertional inactivation/complementation analysis. The organization of the genes in this locus (coxII.orf1.orf3.coxIII) is the same as that of the equivalent operon of Paracoccus denitrificans (ctaC.ctaB.ctaG.ctaE), but unlike that of other bacteria whose cytochrome oxidase genes have been characterized so far. The predicted amino acid sequence homology with eukaryotic oxidases is also higher for Rb. sphaeroides (and P. denitrificans) than for other bacterial versions of the enzyme. The inactivation of coxII results in loss of the characteristic cytochrome oxidase spectrum from membranes of the mutant strain. Full recovery requires introduction into the bacterium of the complete operon containing coxII.orf1.orf3.coxIII; partial complementation yielding a spectrally altered enzyme is achieved with a plasmid containing coxII or coxII.orf1.orf3. These results indicate that the peptides ORF1, ORF3, and COXIII are all required for assembly of native cytochrome c oxidase, suggesting an oxidase-specific assembly or chaperonin function for the ORFs in Rb. sphaeroides similar to that observed for the homologous gene products in yeast, COX10 and COX11.
NASA Astrophysics Data System (ADS)
Sethuramalingam, Prabhu; Vinayagam, Babu Kupusamy
2016-07-01
Carbon nanotube mixed grinding wheel is used in the grinding process to analyze the surface characteristics of AISI D2 tool steel material. Till now no work has been carried out using carbon nanotube based grinding wheel. Carbon nanotube based grinding wheel has excellent thermal conductivity and good mechanical properties which are used to improve the surface finish of the workpiece. In the present study, the multi response optimization of process parameters like surface roughness and metal removal rate of grinding process of single wall carbon nanotube (CNT) in mixed cutting fluids is undertaken using orthogonal array with grey relational analysis. Experiments are performed with designated grinding conditions obtained using the L9 orthogonal array. Based on the results of the grey relational analysis, a set of optimum grinding parameters is obtained. Using the analysis of variance approach the significant machining parameters are found. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared empirically, for conditions of with and without CNT grinding wheel in grinding process.
Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin
2013-01-01
To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people’s habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because “preemptive regulations” are more effective. For areas with high fatality rates (or high quantiles), “ex-post regulations” are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates. PMID:24084673
NASA Astrophysics Data System (ADS)
Ivanov, A.; Voynikova, D.; Gocheva-Ilieva, S.; Kulina, H.; Iliev, I.
2015-10-01
The monitoring and control of air quality in urban areas is important problem in many European countries. The main air pollutants are observed and a huge amount of data is collected during the last years. In Bulgaria, the air quality is surveyed by the official environmental agency and in many towns exceedances of harmful pollutants are detected. The aim of this study is to investigate the pollution from 9 air pollutants in the town of Dimitrovgrad, Bulgaria in the period of 5 years based on hourly data. Principal Component Analysis (PCA) is used to discover the patterns in the overall pollution and the contribution of the 9 pollutants. In addition the Generalized Path Seeker (GPS) regularized regression method is applied to find dependence of CO (carbon monoxide) with respect to other pollutants and 8 meteorological parameters. It is reported that the CO concentrations are in continuously repeated low level quantities very harmful for human health.
2014-01-01
Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738
Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui
2016-01-01
Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365
Sander, R.K.; Quagliano, J.R.; Fry, H.
1997-08-01
Until recently use of lasers for long path absorption measurements has relied on using differential absorption at two wavelengths to look for one species at a time in the atmosphere. With the advent of multi-line CO{sub 2} lasers it is now feasible to generate 30 to 40 lines in a rapid burst to look for spectra of all the chemical species that may be present. Measurements have been made under relatively constant meteorological conditions in a summertime desert environment with a multi-line tunable laser. Multivariate regression analysis of this data shows that the spectra can be accurately fit using a small number of spectral factors or eigenvectors of the time dependent spectral data matrix. The factors can be rationalized in terms of lidar system effects and atmospheric composition changes.
Bjelanovic, Milena; Sørheim, Oddvin; Slinde, Erik; Puolanne, Eero; Isaksson, Tomas; Egelandsdal, Bjørg
2013-11-01
Seventy-two samples of ground beef from M. semimembranosus of two 5 and two 1.5year old animals were prepared. Two types of fat tissues from either beef or pork were added to the ground beef. The samples were prepared to contain predominantly deoxymyoglobin (DMb), oxymyoglobin (OMb) and metmyoglobin (MMb) states on surfaces using selected methods based on chemical treatment (for MMb) and oxygen pressure packaging to induce the two other states. Reflectance spectra were measured on ground beef after three storage times. Partial least regression analysis was used to make calibration models of the desired myoglobin states. Validated models using leave-one-sample out cross validation gave, after correction and normalization, prediction errors of about 5%. Long term storage of ground beef was unsuitable for preparing pure MMb states due to gradual reduction of the pigment to DMb, presumably by bacteria.
NASA Technical Reports Server (NTRS)
Barrett, C. A.
1985-01-01
Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.
2016-01-01
In today's world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems. This study investigates the most important determinants of healthcare efficiency for OECD countries using second stage approach for Bayesian Stochastic Frontier Analysis (BSFA). There are two steps in this study. First we measure 29 OECD countries' healthcare efficiency by BSFA using the data from the OECD Health Database. At second stage, we expose the multiple relationships between the healthcare efficiency and characteristics of healthcare systems across OECD countries using Bayesian beta regression. PMID:27118987
NASA Astrophysics Data System (ADS)
Antón, M.; Cancillo, M. L.; Serrano, A.; García, J. A.
2005-01-01
This paper analyzes the relationship between ultraviolet erythemal radiation (UVER) measured in Badajoz (Spain) and ozone, cloudiness and aerosols. Initially, the values of transmissivity of UVER are related with three parameters (ozone amount, reflectivity and aerosol index) estimated by the satellite instrument TOMS. The relative importance and dependence of each variable is analyzed by means of a multiple regression analysis with an expression derived from the Lambert-Bouger-Beer law. The results indicate that the aerosol index is not a statistically significant factor for the initial expression. Then, a partial model with only ozone and reflectivity as regressors is proposed and coefficients are obtained using UVER measurements of year 2001. Finally the model is validated comparing its prediction for 2002 with UVER measurements at ground. The agreement between both data sets is reasonably good, suggesting that UVER estimations can be successfully derived from observations of other atmospheric variables, thus providing the basis to obtain spatial distributed maps of UV variations.
Nasseryan, Javad; Hajizadeh, Ebrahim; Rasekhi, Aliakbar; Ahangar, Hassan
2016-01-01
Background: The incidence of restenosis in patients suffering from coronary artery disease after undergoing angioplasty is of paramount importance. Accordingly, this study aimed to investigate factors affecting the time of the first incidence of restenosis in patients undergone angioplasty in the city of Zanjan, Iran. Methods: This retrospective cohort study was conducted on 421 patients who referred to Ayatollah Musavi hospital in Zanjan for angioplasty during 2009 to 2012. The time of the incidence of restenosis after angioplasty constituted the dependent variable of the study. Independent variables of the study included signs of diabetes, hypertension, hyperlipidemia, kidney disease, carotid stenosis, lung disease, anemia, angina history, and MI. The Cox regression model with the significance level of 0.05 was deployed for the statistical analysis. Results: According to the Cox regression model, hazard ratio of the first incidence of restenosis in patients with hypertension and angina was 22.8% and 29.5% less than other patients, respectively. However, hazard ratio of the first incidence of restenosis was 7.4 times more in patients suffering from carotid stenosis than other patients (p<0.05). Conclusion: The results of this study revealed that as time goes on, the risk of the incidence of restenosis in angioplasty patients increases such that patients’ survival decreases dramatically after a year. To determine the role of effective factors on the incidence of restenosis, conducting a prospective interventional study is highly recommended. PMID:28210606
Long, Nguyen Phuoc; Huy, Nguyen Tien; Trang, Nguyen Thi Huyen; Luan, Nguyen Thien; Anh, Nguyen Hoang; Nghi, Tran Diem; Hieu, Mai Van; Hirayama, Kenji; Karbwang, Juntra
2014-01-01
BACKGROUND: Ethics is one of the main pillars in the development of science. We performed a JoinPoint regression analysis to analyze the trends of ethical issue research over the past half century. The question is whether ethical issues are neglected despite their importance in modern research. METHOD: PubMed electronic library was used to retrieve publications of all fields and ethical issues. JoinPoint regression analysis was used to identify the significant time trends of publications of all fields and ethical issues, as well as the proportion of publications on ethical issues to all fields over the past half century. Annual percent changes (APC) were computed with their 95% confidence intervals, and a p-value < 0.05 was considered statistically significant. RESULTS: We found that publications of ethical issues increased during the period of 1965–1996 but slightly fell in recent years (from 1996 to 2013). When comparing the absolute number of ethics related articles (APEI) to all publications of all fields (APAF) on PubMed, the results showed that the proportion of APEI to APAF statistically increased during the periods of 1965–1974, 1974–1986, and 1986–1993, with APCs of 11.0, 2.1, and 8.8, respectively. However, the trend has gradually dropped since 1993 and shown a marked decrease from 2002 to 2013 with an annual percent change of –7.4%. CONCLUSIONS: Scientific productivity in ethical issues research on over the past half century rapidly increased during the first 30-year period but has recently been in decline. Since ethics is an important aspect of scientific research, we suggest that greater attention is needed in order to emphasize the role of ethics in modern research. PMID:25324690
Nelemans, S A; Branje, S J T; Hale, W W; Goossens, L; Koot, H M; Oldehinkel, A J; Meeus, W H J
2016-10-01
Adolescence is a critical period for the development of depressive symptoms. Lower quality of the parent-adolescent relationship has been consistently associated with higher adolescent depressive symptoms, but discrepancies in perceptions of parents and adolescents regarding the quality of their relationship may be particularly important to consider. In the present study, we therefore examined how discrepancies in parents' and adolescents' perceptions of the parent-adolescent relationship were associated with early adolescent depressive symptoms, both concurrently and longitudinally over a 1-year period. Our sample consisted of 497 Dutch adolescents (57 % boys, M age = 13.03 years), residing in the western and central regions of the Netherlands, and their mothers and fathers, who all completed several questionnaires on two occasions with a 1-year interval. Adolescents reported on depressive symptoms and all informants reported on levels of negative interaction in the parent-adolescent relationship. Results from polynomial regression analyses including interaction terms between informants' perceptions, which have recently been proposed as more valid tests of hypotheses involving informant discrepancies than difference scores, suggested the highest adolescent depressive symptoms when both the mother and the adolescent reported high negative interaction, and when the adolescent reported high but the father reported low negative interaction. This pattern of findings underscores the need for a more sophisticated methodology such as polynomial regression analysis including tests of moderation, rather than the use of difference scores, which can adequately address both congruence and discrepancies in perceptions of adolescents and mothers/fathers of the parent-adolescent relationship in detail. Such an analysis can contribute to a more comprehensive understanding of risk factors for early adolescent depressive symptoms.
Batson, Sarah; Sutton, Alex; Abrams, Keith
2016-01-01
Background Patients with atrial fibrillation are at a greater risk of stroke and therefore the main goal for treatment of patients with atrial fibrillation is to prevent stroke from occurring. There are a number of different stroke prevention treatments available to include warfarin and novel oral anticoagulants. Previous network meta-analyses of novel oral anticoagulants for stroke prevention in atrial fibrillation acknowledge the limitation of heterogeneity across the included trials but have not explored the impact of potentially important treatment modifying covariates. Objectives To explore potentially important treatment modifying covariates using network meta-regression analyses for stroke prevention in atrial fibrillation. Methods We performed a network meta-analysis for the outcome of ischaemic stroke and conducted an exploratory regression analysis considering potentially important treatment modifying covariates. These covariates included the proportion of patients with a previous stroke, proportion of males, mean age, the duration of study follow-up and the patients underlying risk of ischaemic stroke. Results None of the covariates explored impacted relative treatment effects relative to placebo. Notably, the exploration of ‘study follow-up’ as a covariate supported the assumption that difference in trial durations is unimportant in this indication despite the variation across trials in the network. Conclusion This study is limited by the quantity of data available. Further investigation is warranted, and, as justifying further trials may be difficult, it would be desirable to obtain individual patient level data (IPD) to facilitate an effort to relate treatment effects to IPD covariates in order to investigate heterogeneity. Observational data could also be examined to establish if there are potential trends elsewhere. The approach and methods presented have potentially wide applications within any indication as to highlight the potential benefit
Statistical methods for astronomical data with upper limits. II - Correlation and regression
NASA Technical Reports Server (NTRS)
Isobe, T.; Feigelson, E. D.; Nelson, P. I.
1986-01-01
Statistical methods for calculating correlations and regressions in bivariate censored data where the dependent variable can have upper or lower limits are presented. Cox's regression and the generalization of Kendall's rank correlation coefficient provide significant levels of correlations, and the EM algorithm, under the assumption of normally distributed errors, and its nonparametric analog using the Kaplan-Meier estimator, give estimates for the slope of a regression line. Monte Carlo simulations demonstrate that survival analysis is reliable in determining correlations between luminosities at different bands. Survival analysis is applied to CO emission in infrared galaxies, X-ray emission in radio galaxies, H-alpha emission in cooling cluster cores, and radio emission in Seyfert galaxies.
IL-20, an anti-angiogenic cytokine that inhibits COX-2 expression.
Heuzé-Vourc'h, Nathalie; Liu, Ming; Dalwadi, Harnisha; Baratelli, Felicita E; Zhu, Li; Goodglick, Lee; Põld, Mehis; Sharma, Sherven; Ramirez, Ruben D; Shay, Jerry W; Minna, John D; Strieter, Robert M; Dubinett, Steven M
2005-07-29
COX-2 overexpression and subsequent PGE(2) production are frequently associated with non-small cell lung cancer and are implicated in tumor-mediated angiogenesis. Here, we report for the first time that IL-20 downregulates COX-2 and PGE(2) in human bronchial epithelial and endothelial cells. Flow cytometry analysis suggests that IL-20-dependent inhibition of COX-2/PGE(2) occurs through the IL-22R1/IL-20R2 dimers. In addition, we report that IL-20 exerts anti-angiogenic effects, inhibiting experimental angiogenesis. IL-20-mediated inhibition of PMA-induced angiogenesis occurs through the COX-2 regulatory pathway. Altogether our findings revealed that IL-20 is a negative modulator of COX-2/PGE(2) and inhibits angiogenesis.
Brown, A M
2001-06-01
The objective of this present study was to introduce a simple, easily understood method for carrying out non-linear regression analysis based on user input functions. While it is relatively straightforward to fit data with simple functions such as linear or logarithmic functions, fitting data with more complicated non-linear functions is more difficult. Commercial specialist programmes are available that will carry out this analysis, but these programmes are expensive and are not intuitive to learn. An alternative method described here is to use the SOLVER function of the ubiquitous spreadsheet programme Microsoft Excel, which employs an iterative least squares fitting routine to produce the optimal goodness of fit between data and function. The intent of this paper is to lead the reader through an easily understood step-by-step guide to implementing this method, which can be applied to any function in the form y=f(x), and is well suited to fast, reliable analysis of data in all fields of biology.
Viscum album-Mediated COX-2 Inhibition Implicates Destabilization of COX-2 mRNA
Saha, Chaitrali; Hegde, Pushpa; Friboulet, Alain; Bayry, Jagadeesh; Kaveri, Srinivas V.
2015-01-01
Extensive use of Viscum album (VA) preparations in the complementary therapy of cancer and in several other human pathologies has led to an increasing number of cellular and molecular approaches to explore the mechanisms of action of VA. We have recently demonstrated that, VA preparations exert a potent anti-inflammatory effect by selectively down-regulating the COX-2-mediated cytokine-induced secretion of prostaglandin E2 (PGE2), one of the important molecular signatures of inflammatory reactions. In this study, we observed a significant down-regulation of COX-2 protein expression in VA-treated A549 cells however COX-2 mRNA levels were unaltered. Therefore, we hypothesized that VA induces destabilisation of COX-2 mRNA, thereby depleting the available functional COX-2 mRNA for the protein synthesis and for the subsequent secretion of PGE2. To address this question, we analyzed the molecular degradation of COX-2 protein and its corresponding mRNA in A549 cell line. Using cyclohexamide pulse chase experiment, we demonstrate that, COX-2 protein degradation is not affected by the treatment with VA whereas experiments on transcriptional blockade with actinomycin D, revealed a marked reduction in the half life of COX-2 mRNA due to its rapid degradation in the cells treated with VA compared to that in IL-1β-stimulated cells. These results thus demonstrate that VA-mediated inhibition of PGE2 implicates destabilization of COX-2 mRNA. PMID:25664986
NASA Astrophysics Data System (ADS)
Morandi, Maria T.; Daisey, Joan M.; Lioy, Paul J.
A modified factor analysis/multiple regression (FA/MR) receptor-oriented source apportionment model has been developed which permits application of FA/MR statistical methods when some of the tracers are not unique to an individual source type. The new method uses factor and regression analyses to apportion non-unique tracer ambient concentrations in situations where there are unique tracers for all sources contributing to the non-unique tracer except one, and ascribes the residual concentration to that source. This value is then used as the source tracer in the final FA/MR apportionment model for ambient paniculate matter. In addition, factor analyses results are complemented with examination of regression residuals in order to optimize the number of identifiable sources. The new method has been applied to identify and apportion the sources of inhalable particulate matter (IPM; D5015 μm), Pb and Fe at a site in Newark, NJ. The model indicated that sulfate/secondary aerosol contributed an average of 25.8 μ -3 (48%) to IPM concentrations, followed by soil resuspension (8.2 μ -3 or 15%), paint spraying/paint pigment (6.7/gmm -3or 13%), fuel oil burning/space heating (4.3 μ -3 or 8 %), industrial emissions (3.6 μm -3 or 7 %) and motor vehicle exhaust (2.7 μ -3 or 15 %). Contributions to ambient Pb concentrations were: motor vehicle exhaust (0.16μm -3or 36%), soil resuspension (0.10μm -3 or 24%), fuel oil burning/space heating (0.08μm -3or 18%), industrial emissions (0.07 μ -3 or 17 %), paint spraying/paint pigment (0.036 μm -3or 9 %) and zinc related sources (0.022 μ -3 or 5 %). Contributions to ambient Fe concentrations were: soil resuspension (0.43μ -3or 51%), paint spraying/paint pigment (0.28 μm -3or 33 %) and industrial emissions (0.15 μ -3or 18 %). The models were validated by comparing partial source profiles calculated from modeling results with the corresponding published source emissions composition.
Sharon Falcone Miller; Bruce G. Miller
2007-12-15
This paper compares the emissions factors for a suite of liquid biofuels (three animal fats, waste restaurant grease, pressed soybean oil, and a biodiesel produced from soybean oil) and four fossil fuels (i.e., natural gas, No. 2 fuel oil, No. 6 fuel oil, and pulverized coal) in Penn State's commercial water-tube boiler to assess their viability as fuels for green heat applications. The data were broken into two subsets, i.e., fossil fuels and biofuels. The regression model for the liquid biofuels (as a subset) did not perform well for all of the gases. In addition, the coefficient in the models showed the EPA method underestimating CO and NOx emissions. No relation could be studied for SO{sub 2} for the liquid biofuels as they contain no sulfur; however, the model showed a good relationship between the two methods for SO{sub 2} in the fossil fuels. AP-42 emissions factors for the fossil fuels were also compared to the mass balance emissions factors and EPA CFR Title 40 emissions factors. Overall, the AP-42 emissions factors for the fossil fuels did not compare well with the mass balance emissions factors or the EPA CFR Title 40 emissions factors. Regression analysis of the AP-42, EPA, and mass balance emissions factors for the fossil fuels showed a significant relationship only for CO{sub 2} and SO{sub 2}. However, the regression models underestimate the SO{sub 2} emissions by 33%. These tests illustrate the importance in performing material balances around boilers to obtain the most accurate emissions levels, especially when dealing with biofuels. The EPA emissions factors were very good at predicting the mass balance emissions factors for the fossil fuels and to a lesser degree the biofuels. While the AP-42 emissions factors and EPA CFR Title 40 emissions factors are easier to perform, especially in large, full-scale systems, this study illustrated the shortcomings of estimation techniques. 23 refs., 3 figs., 8 tabs.
Jamshidi, S.; Yadollahi, A.; Ahmadi, H.; Arab, M. M.; Eftekhari, M.
2016-01-01
Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating eight macronutrients (NO3-, NH4+, Ca2+, K+, Mg2+, PO42-, SO42-, and Cl−) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl), and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH4+ (301.7), and NO3-, NH4+ (64), SO42- (54.1), K+ (40.4), and NO3- (35.1) in OHF and Ca2+ (23.7), NH4+ (10.7), NO3- (9.1), NH4+ (317.6), and NH4+ (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl, and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO3-, 5.7 NH4+, 2.7 Ca2+, 31.5 K+, 3.3 Mg2+, 2.6 PO42-, 5.6 SO42-, and 3.5 Cl− could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO3-, 13.1 NH4+, 5.5 Ca2+, 35.7 K+, 1.5 Mg2+, 2.1 PO42-, 3.6 SO42-, and 3 Cl−. PMID:27066013
Strong, Mark; Oakley, Jeremy E; Brennan, Alan; Breeze, Penny
2015-07-01
Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method.
Liu, Yan; Salvendy, Gavriel
2009-05-01
This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.
NASA Technical Reports Server (NTRS)
Scarpace, F. L.; Voss, A. W.
1973-01-01
Dye densities of multi-layered films are determined by applying a regression analysis to the spectral response of the composite transparency. The amount of dye in each layer is determined by fitting the sum of the individual dye layer densities to the measured dye densities. From this, dye content constants are calculated. Methods of calculating equivalent exposures are discussed. Equivalent exposures are a constant amount of energy over a limited band-width that will give the same dye content constants as the real incident energy. Methods of using these equivalent exposures for analysis of photographic data are presented.
NASA Astrophysics Data System (ADS)
Armaghani, Danial Jahed; Mahdiyar, Amir; Hasanipanah, Mahdi; Faradonbeh, Roohollah Shirani; Khandelwal, Manoj; Amnieh, Hassan Bakhshandeh
2016-09-01
Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.
NASA Astrophysics Data System (ADS)
Tan, Ailing; Zhao, Yong; Wang, Siyuan
2016-10-01
Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter σ of gasoline, diesel and kerosene model were 48.1418 and 0.1067, 53.2820 and 0.1095, 59.1689 and 0.1000 respectively. The decision coefficient R2 of the prediction model were 0.9983, 0.9907 and 0.9942 respectively. RMSEP values were 0.0753, 0.1539 and 0.0789 respectively. For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176±0.0636 μL/mL, -0.0084+/-0.1941 μL/mL, and 0.00338+/-0.0726 μL/mL respectively. The results showed that each component's concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.
Tvete, Ingunn Fride; Natvig, Bent; Gåsemyr, Jørund; Meland, Nils; Røine, Marianne; Klemp, Marianne
2015-01-01
Rheumatoid arthritis patients have been treated with disease modifying anti-rheumatic drugs (DMARDs) and the newer biologic drugs. We sought to compare and rank the biologics with respect to efficacy. We performed a literature search identifying 54 publications encompassing 9 biologics. We conducted a multiple treatment comparison regression analysis letting the number experiencing a 50% improvement on the ACR score be dependent upon dose level and disease duration for assessing the comparable relative effect between biologics and placebo or DMARD. The analysis embraced all treatment and comparator arms over all publications. Hence, all measured effects of any biologic agent contributed to the comparison of all biologic agents relative to each other either given alone or combined with DMARD. We found the drug effect to be dependent on dose level, but not on disease duration, and the impact of a high versus low dose level was the same for all drugs (higher doses indicated a higher frequency of ACR50 scores). The ranking of the drugs when given without DMARD was certolizumab (ranked highest), etanercept, tocilizumab/ abatacept and adalimumab. The ranking of the drugs when given with DMARD was certolizumab (ranked highest), tocilizumab, anakinra, rituximab, golimumab/ infliximab/ abatacept, adalimumab/ etanercept. Still, all drugs were effective. All biologic agents were effective compared to placebo, with certolizumab the most effective and adalimumab (without DMARD treatment) and adalimumab/ etanercept (combined with DMARD treatment) the least effective. The drugs were in general more effective, except for etanercept, when given together with DMARDs. PMID:26356639
Hung, Bui The; Long, Nguyen Phuoc; Hung, Le Phi; Luan, Nguyen Thien; Anh, Nguyen Hoang; Nghi, Tran Diem; Van Hieu, Mai; Trang, Nguyen Thi Huyen; Rafidinarivo, Herizo Fabien; Anh, Nguyen Ky; Hawkes, David; Huy, Nguyen Tien; Hirayama, Kenji
2015-01-01
Background Evidence-based medicine (EBM) has developed as the dominant paradigm of assessment of evidence that is used in clinical practice. Since its development, EBM has been applied to integrate the best available research into diagnosis and treatment with the purpose of improving patient care. In the EBM era, a hierarchy of evidence has been proposed, including various types of research methods, such as meta-analysis (MA), systematic review (SRV), randomized controlled trial (RCT), case report (CR), practice guideline (PGL), and so on. Although there are numerous studies examining the impact and importance of specific cases of EBM in clinical practice, there is a lack of research quantitatively measuring publication trends in the growth and development of EBM. Therefore, a bibliometric analysis was constructed to determine the scientific productivity of EBM research over decades. Methods NCBI PubMed database was used to search, retrieve and classify publications according to research method and year of publication. Joinpoint regression analysis was undertaken to analyze trends in research productivity and the prevalence of individual research methods. Findings Analysis indicates that MA and SRV, which are classified as the highest ranking of evidence in the EBM, accounted for a relatively small but auspicious number of publications. For most research methods, the annual percent change (APC) indicates a consistent increase in publication frequency. MA, SRV and RCT show the highest rate of publication growth in the past twenty years. Only controlled clinical trials (CCT) shows a non-significant reduction in publications over the past ten years. Conclusions Higher quality research methods, such as MA, SRV and RCT, are showing continuous publication growth, which suggests an acknowledgement of the value of these methods. This study provides the first quantitative assessment of research method publication trends in EBM. PMID:25849641
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Logistic Regression: Concept and Application
ERIC Educational Resources Information Center
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung
2014-01-01
In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene-gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.
Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui
2016-01-01
In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct
Mielenz, Norbert; Spilke, Joachim; Krejcova, Hana; Schüler, Lutz
2006-10-01
Random regression models are widely used in the field of animal breeding for the genetic evaluation of daily milk yields from different test days. These models are capable of handling different environmental effects on the respective test day, and they describe the characteristics of the course of the lactation period by using suitable covariates with fixed and random regression coefficients. As the numerically expensive estimation of parameters is already part of advanced computer software, modifications of random regression models will considerably grow in importance for statistical evaluations of nutrition and behaviour experiments with animals. Random regression models belong to the large class of linear mixed models. Thus, when choosing a model, or more precisely, when selecting a suitable covariance structure of the random effects, the information criteria of Akaike and Schwarz can be used. In this study, the fitting of random regression models for a statistical analysis of a feeding experiment with dairy cows is illustrated under application of the program package SAS. For each of the feeding groups, lactation curves modelled by covariates with fixed regression coefficients are estimated simultaneously. With the help of the fixed regression coefficients, differences between the groups are estimated and then tested for significance. The covariance structure of the random and subject-specific effects and the serial correlation matrix are selected by using information criteria and by estimating correlations between repeated measurements. For the verification of the selected model and the alternative models, mean values and standard deviations estimated with ordinary least square residuals are used.
Charvat, Hadrien; Goto, Atsushi; Goto, Maki; Inoue, Machiko; Heianza, Yoriko; Arase, Yasuji; Sone, Hirohito; Nakagami, Tomoko; Song, Xin; Qiao, Qing; Tuomilehto, Jaakko; Tsugane, Shoichiro; Noda, Mitsuhiko; Inoue, Manami
2015-01-01
Aims/Introduction To provide age- and sex-specific trends, age-standardized trends, and projections of diabetes prevalence through the year 2030 in the Japanese adult population. Materials and Methods In the present meta-regression analysis, we included 161,087 adults from six studies and nine national health surveys carried out between 1988 and 2011 in Japan. We assessed the prevalence of diabetes using a recorded history of diabetes or, for the population of individuals without known diabetes, either a glycated hemoglobin level of ≥6.5% (48 mmol/mol) or the 1999 World Health Organization criteria (i.e., a fasting plasma glucose level of ≥126 mg/dL and/or 2-h glucose level of ≥200 mg/dL in the 75-g oral glucose tolerance test). Results For both sexes, prevalence appeared to remain unchanged over the years in all age categories except for men aged 70 years or older, in whom a significant increase in prevalence with time was observed. Age-standardized diabetes prevalence estimates based on the Japanese population of the corresponding year showed marked increasing trends: diabetes prevalence was 6.1% among women (95% confidence interval [CI] 5.5–6.7), 9.9% (95% CI 9.2–10.6) among men, and 7.9% (95% CI 7.5–8.4) among the total population in 2010, and was expected to rise by 2030 to 6.7% (95% CI 5.2–9.2), 13.1% (95% CI 10.9–16.7) and 9.8% (95% CI 8.5–12.0), respectively. In contrast, the age-standardized diabetes prevalence using a fixed population appeared to remain unchanged. Conclusions This large-scale meta-regression analysis shows that a substantial increase in diabetes prevalence is expected in Japan during the next few decades, mainly as a result of the aging of the adult population. PMID:26417410
AtCOX17, an Arabidopsis homolog of the yeast copper chaperone COX17.
Balandin, Teresa; Castresana, Carmen
2002-08-01
We have identified a new plant gene, AtCOX17, encoding a protein that shares sequence similarity to COX17, a Cu-binding protein from yeast (Saccharomyces cerevisiae) and vertebrates that mediates the delivery of Cu to the mitochondria for the assembly of a functional cytochrome oxidase complex. The newly characterized Arabidopsis protein has six Cys residues at positions corresponding to those known to coordinate Cu binding in the yeast homolog. Moreover, we show that the Arabidopsis COX17 cDNA complements a COX17 mutant of yeast restoring the respiratory deficiency associated with that mutation. These two lines of evidence indicate that the plant protein identified here is a functional equivalent of yeast COX17 and might serve as a Cu delivery protein for the plant mitochondria. COX17 was identified by investigating the hypersensitive response-like necrotic response provoked in tobacco (Nicotiana tabacum) leaves after harpin inoculation. AtCOX17 expression was activated by high concentrations of Cu, bacterial inoculation, salicylic acid treatment, and treatments that generated NO and hydrogen peroxide. All of the conditions inducing COX17 are known to inhibit mitochondrial respiration and to produce an increase of reactive oxygen species, suggesting that gene induction occurs in response to stress situations that interfere with mitochondrial function.
Targeting Estrogen-Induced COX-2 Activity in Lymphangioleiomyomatosis (LAM)
2014-12-01
Lymphangioleiomyomatosis (LAM), prostaglandin biosynthesis, cyclooxygenase-2 (COX-2), COX-2 inhibitors, xenograft tumors, bioluminescent imaging...Lymphangioleiomyomatosis (LAM), prostaglandin biosynthesis, cyclooxygenase-2 (COX-2), COX-2 inhibitors, xenograft tumors, bioluminescent imaging...TSC2- null cells. We found that aspirin treatment for three weeks decreased the intensity of bioluminescence , Page 5 of 8 and decreased the
Westreich, Daniel; Cole, Stephen R; Schisterman, Enrique F; Platt, Robert W
2012-08-30
Motivated by a previously published study of HIV treatment, we simulated data subject to time-varying confounding affected by prior treatment to examine some finite-sample properties of marginal structural Cox proportional hazards models. We compared (a) unadjusted, (b) regression-adjusted, (c) unstabilized, and (d) stabilized marginal structural (inverse probability-of-treatment [IPT] weighted) model estimators of effect in terms of bias, standard error, root mean squared error (MSE), and 95% confidence limit coverage over a range of research scenarios, including relatively small sample sizes and 10 study assessments. In the base-case scenario resembling the motivating example, where the true hazard ratio was 0.5, both IPT-weighted analyses were unbiased, whereas crude and adjusted analyses showed substantial bias towards and across the null. Stabilized IPT-weighted analyses remained unbiased across a range of scenarios, including relatively small sample size; however, the standard error was generally smaller in crude and adjusted models. In many cases, unstabilized weighted analysis showed a substantial increase in standard error compared with other approaches. Root MSE was smallest in the IPT-weighted analyses for the base-case scenario. In situations where time-varying confounding affected by prior treatment was absent, IPT-weighted analyses were less precise and therefore had greater root MSE compared with adjusted analyses. The 95% confidence limit coverage was close to nominal for all stabilized IPT-weighted but poor in crude, adjusted, and unstabilized IPT-weighted analysis. Under realistic scenarios, marginal structural Cox proportional hazards models performed according to expectations based on large-sample theory and provided accurate estimates of the hazard ratio.
Abdulhag, Ulla Najwa; Soiferman, Devorah; Schueler-Furman, Ora; Miller, Chaya; Shaag, Avraham; Elpeleg, Orly; Edvardson, Simon; Saada, Ann
2015-01-01
Isolated cytochrome c oxidase (COX) deficiency is a prevalent cause of mitochondrial disease and is mostly caused by nuclear-encoded mutations in assembly factors while rarely by mutations in structural subunits. We hereby report a case of isolated COX deficiency manifesting with encephalomyopathy, hydrocephalus and hypertropic cardiomyopathy due to a missense p.R20C mutation in the COX6B1 gene, which encodes an integral, nuclear-encoded COX subunit. This novel mutation was predicted to be severe in silico. In accord, enzymatic activity was undetectable in muscle and fibroblasts, was severely decreased in lymphocytes and the COX6B1 protein was barely detectable in patient's muscle mitochondria. Complementation with the wild-type cDNA by a lentiviral construct restored COX activity, and mitochondrial function was improved by 5-aminoimidazole-4-carboxamide ribonucleotide, resveratrol and ascorbate in the patient's fibroblasts. We suggest that genetic analysis of COX6B1should be included in the investigation of isolated COX deficiency, including patients with cardiac defects. Initial measurement of COX activity in lymphocytes may be useful as it might circumvent the need for invasive muscle biopsy. The evaluation of ascorbate supplementation to patients with mutated COX6B1 is warranted. PMID:24781756
Huang, Jian; Zhang, Di; Xie, Fuqiang; Lin, Degui
2015-01-01
Increasing evidence suggests that cancer stem cells (CSCs) are responsible for tumor initiation and maintenance. Additionally, it is becoming apparent that cyclooxygenase (COX) signaling is associated with canine mammary tumor development. The goals of the present study were to investigate COX-2 expression patterns and their effect on CSC-mediated tumor initiation in primary canine mammary tissues and tumorsphere models using immunohistochemistry. Patterns of COX-2, CD44, octamer-binding transcription factor (Oct)-3/4, and epidermal growth factor receptor (EGFR) expression were examined in malignant mammary tumor (MMT) samples and analyzed in terms of clinicopathological characteristics. COX-2 and Oct-3/4 expression was higher in MMTs compared to other histological samples with heterogeneous patterns. In MMTs, COX-2 expression correlated with tumor malignancy features. Significant associations between COX-2, CD44, and EGFR were observed in low-differentiated MMTs. Comparative analysis showed that the levels of COX-2, CD44, and Oct-3/4 expression varied significantly among TSs of three histological grades. Enhanced COX-2 staining was consistently observed in TSs. Similar levels of staining intensity were found for CD44 and Oct-3/4, but EGFR expression was weak. Our findings indicate the potential role of COX-2 in CSC-mediated tumor initiation, and suggest that COX-2 inhibition may help treat canine mammary tumors by targeting CSCs.
Zhao, Jingli; Li, Shuling; Wang, Lijuan; Jiang, Li; Yang, Runqing; Cui, Yuehua
2017-01-01
Genomic imprinting underlying growth and development traits has been recognized, with a focus on the form of absolute or pure growth. However, little is known about the effect of genomic imprinting on relative growth. In this study, we proposed a random regression model to estimate genome-wide imprinting effects on the relative growth of multiple tissues and organs to body weight in mice. Joint static allometry scaling equation as sub-model is nested within the genetic effects of markers and polygenic effects caused by a pedigree. Both chromosome-wide and genome-wide statistical tests were conducted to identify imprinted quantitative trait nucleotides (QTNs) associated with relative growth of individual tissues and organs to body weight. Real data analysis showed that three of six analysed tissues and organs are significantly associated with body weight in terms of phenotypic relative growth. At the chromosome-wide level, a total 122 QTNs were associated with allometries of kidney, spleen and liver weights to body weight, 36 of which were imprinted with different imprinting fashions. Further, only two imprinted QTNs responsible for relative growth of spleen and liver were verified by genome-wide test. Our approach provides a general framework for statistical inference of genomic imprinting underlying allometry scaling in animals. PMID:28338098
Perez, Ivan; Chavez, Allison K.; Ponce, Dario
2016-01-01
Background: The Ricketts' posteroanterior (PA) cephalometry seems to be the most widely used and it has not been tested by multivariate statistics for sex determination. Objective: The objective was to determine the applicability of Ricketts' PA cephalometry for sex determination using the logistic regression analysis. Materials and Methods: The logistic models were estimated at distinct age cutoffs (all ages, 11 years, 13 years, and 15 years) in a database from 1,296 Hispano American Peruvians between 5 years and 44 years of age. Results: The logistic models were composed by six cephalometric measurements; the accuracy achieved by resubstitution varied between 60% and 70% and all the variables, with one exception, exhibited a direct relationship with the probability of being classified as male; the nasal width exhibited an indirect relationship. Conclusion: The maxillary and facial widths were present in all models and may represent a sexual dimorphism indicator. The accuracy found was lower than the literature and the Ricketts' PA cephalometry may not be adequate for sex determination. The indirect relationship of the nasal width in models with data from patients of 12 years of age or less may be a trait related to age or a characteristic in the studied population, which could be better studied and confirmed. PMID:27555732
Shabri, Ani; Samsudin, Ruhaidah
2014-01-01
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666
Wilson, Asa B; Kerr, Bernard J; Bastian, Nathaniel D; Fulton, Lawrence V
2012-01-01
From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital--the critical access hospital (CAH). The conversion to CAH and the associated cost-based reimbursement scheme significantly slowed the closure rate of rural hospitals. This work investigates which methods can ensure the long-term viability of small hospitals. This article uses a two-step design to focus on a hypothesized relationship between technical efficiency of CAHs and a recently developed set of financial monitors for these entities. The goal is to identify the financial performance measures associated with efficiency. The first step uses data envelopment analysis (DEA) to differentiate efficient from inefficient facilities within a data set of 183 CAHs. Determining DEA efficiency is an a priori categorization of hospitals in the data set as efficient or inefficient. In the second step, DEA efficiency is the categorical dependent variable (efficient = 0, inefficient = 1) in the subsequent binary logistic regression (LR) model. A set of six financial monitors selected from the array of 20 measures were the LR independent variables. We use a binary LR to test the null hypothesis that recently developed CAH financial indicators had no predictive value for categorizing a CAH as efficient or inefficient, (i.e., there is no relationship between DEA efficiency and fiscal performance).
Martina, R; Kay, R; van Maanen, R; Ridder, A
2015-01-01
Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well.