Microbial Transformation of Esters of Chlorinated Carboxylic Acids

PubMed Central

Paris, D. F.; Wolfe, N. L.; Steen, W. C.

1984-01-01

Two groups of compounds were selected for microbial transformation studies. In the first group were carboxylic acid esters having a fixed aromatic moiety and an increasing length of the alkyl component. Ethyl esters of chlorine-substituted carboxylic acids were in the second group. Microorganisms from environmental waters and a pure culture of Pseudomonas putida U were used. The bacterial populations were monitored by plate counts, and disappearance of the parent compound was followed by gas-liquid chromatography as a function of time. The products of microbial hydrolysis were the respective carboxylic acids. Octanol-water partition coefficients (Kow) for the compounds were measured. These values spanned three orders of magnitude, whereas microbial transformation rate constants (kb) varied only 50-fold. The microbial rate constants of the carboxylic acid esters with a fixed aromatic moiety increased with an increasing length of alkyl substituents. The regression coefficient for the linear relationships between log kb and log Kow was high for group 1 compounds, indicating that these parameters correlated well. The regression coefficient for the linear relationships for group 2 compounds, however, was low, indicating that these parameters correlated poorly. PMID:16346459

Tests of Sunspot Number Sequences: 3. Effects of Regression Procedures on the Calibration of Historic Sunspot Data

NASA Astrophysics Data System (ADS)

Lockwood, M.; Owens, M. J.; Barnard, L.; Usoskin, I. G.

2016-11-01

We use sunspot-group observations from the Royal Greenwich Observatory (RGO) to investigate the effects of intercalibrating data from observers with different visual acuities. The tests are made by counting the number of groups [RB] above a variable cut-off threshold of observed total whole spot area (uncorrected for foreshortening) to simulate what a lower-acuity observer would have seen. The synthesised annual means of RB are then re-scaled to the full observed RGO group number [RA] using a variety of regression techniques. It is found that a very high correlation between RA and RB (r_{AB} > 0.98) does not prevent large errors in the intercalibration (for example sunspot-maximum values can be over 30 % too large even for such levels of r_{AB}). In generating the backbone sunspot number [R_{BB}], Svalgaard and Schatten ( Solar Phys., 2016) force regression fits to pass through the scatter-plot origin, which generates unreliable fits (the residuals do not form a normal distribution) and causes sunspot-cycle amplitudes to be exaggerated in the intercalibrated data. It is demonstrated that the use of Quantile-Quantile ("Q-Q") plots to test for a normal distribution is a useful indicator of erroneous and misleading regression fits. Ordinary least-squares linear fits, not forced to pass through the origin, are sometimes reliable (although the optimum method used is shown to be different when matching peak and average sunspot-group numbers). However, other fits are only reliable if non-linear regression is used. From these results it is entirely possible that the inflation of solar-cycle amplitudes in the backbone group sunspot number as one goes back in time, relative to related solar-terrestrial parameters, is entirely caused by the use of inappropriate and non-robust regression techniques to calibrate the sunspot data.

Female married illiteracy as the most important continual determinant of total fertility rate among districts of Empowered Action Group States of India: Evidence from Annual Health Survey 2011–12

PubMed Central

Kumar, Rajesh; Dogra, Vishal; Rani, Khushbu; Sahu, Kanti

2017-01-01

Background: District level determinants of total fertility rate in Empowered Action Group states of India can help in ongoing population stabilization programs in India. Objective: Present study intends to assess the role of district level determinants in predicting total fertility rate among districts of the Empowered Action Group states of India. Material and Methods: Data from Annual Health Survey (2011-12) was analysed using STATA and R software packages. Multiple linear regression models were built and evaluated using Akaike Information Criterion. For further understanding, recursive partitioning was used to prepare a regression tree. Results: Female married illiteracy positively associated with total fertility rate and explained more than half (53%) of variance. Under multiple linear regression model, married illiteracy, infant mortality rate, Ante natal care registration, household size, median age of live birth and sex ratio explained 70% of total variance in total fertility rate. In regression tree, female married illiteracy was the root node and splits at 42% determined TFR <= 2.7. The next left side branch was again married illiteracy with splits at 23% to determine TFR <= 2.1. Conclusion: We conclude that female married illiteracy is one of the most important determinants explaining total fertility rate among the districts of an Empowered Action Group states. Focus on female literacy is required to stabilize the population growth in long run. PMID:29416999

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

Investigative clinical study on prostate cancer part III: exploring total PSA and free testosterone distributions and linear correlations in groups and subgroups of operated prostate cancer patients according to the total PSA/FT ratio.

PubMed

Porcaro, Antonio B; Petrozziello, Aldo; Romano, Mario; Sava, Teodoro; Ghimenton, Claudio; Caruso, Beatrice; Migliorini, Filippo; Zecchini Antoniolli, Stefano; Rubilotta, Emanuele; Lacola, Vincenzo; Monaco, Carmelo; Comunale, Luigi

2010-01-01

Prostate cancer is an interesting tumor for endocrine investigation. The prostate-specific antigen/free testosterone (PSA/FT) ratio has been shown to be effective in clustering patients in prognostic groups as follows: low risk (PSA/FT ≤0.20), intermediate risk (PSA/FT >0.20 and ≤0.40) and high risk (PSA/FT >0.40 and ≤1.5). In the present study we explored the total PSA and FT distributions, and linear regression of FT predicting PSA in the different groups (PSA/FT, pT and pG) and subgroups (pT and pG) of patients according to the prognostic PSA/FT ratio. The study included 128 operated prostate cancer patients. Pretreatment simultaneous serum samples were obtained for measuring free testosterone (FT) and total PSA levels. Patients were grouped according to the total PSA/FT ratio prognostic clusters (≤0.20, >0.20 and ≤0.40, >0.40), pT (2, 3a and 3b+4) and pathological Gleason score (pG) (≤6, = 7 >3 + 4, ≥7 >4 + 3). The pT and pG sets were subgrouped according to the prognostic PSA/FT ratio. Linear regression analysis of FT predicting total PSA was computed according to the different PSA/FT prognostic clusters for the: (1) total sample population, (2) pT and pG groups, (3) intraprostatic (pT2) and extraprostatic disease (pT3a/3b/4), and (4) low-intermediate grade (pG ≤6) and high-grade (pG ≥7) prostate cancer. Analysis of variance always showed highly significant different PSA distributions for (1) the different PSA/FT, pT and pG groups; and (2) the pT and pG prognostic subgroups. Significant FT distributions were detected for the (1) PSA/FT and pT groups; and (2) the pT2, pT3a and pG ≤6 prognostic PSA/FT subgroups. Correlation, variance and linear regression analysis of FT predicting total PSA was significant for (1) the PSA/FT prognostic clusters, (2) all the pT2 and pT3a subgroups, and (3) the pT3b/4 subgroup with PSA/FT >0.20 and ≤0.40, and (4) all the pG subsets. Linear regression analysis showed that the slopes of the predicting variable (FT) were always highly significant for patients with (1) intraprostate and extraprostate disease, and (2) low-grade and high-grade prostate cancer. According to the prognostic PSA/FT ratio, significantly lower levels of FT are detected in prostate cancer patients with extensive and high-grade disease. Also, significant linear correlations of FT predicting PSA are assessed in the different groups and subgroups of patients clustered according to the prognostic PSA/FT ratio. Confirmatory studies are needed. Copyright © 2010 S. Karger AG, Basel.

[Comparison of arterial stiffness in non-hypertensive and hypertensive population of various age groups].

PubMed

Zhang, Y J; Wu, S L; Li, H Y; Zhao, Q H; Ning, C H; Zhang, R Y; Yu, J X; Li, W; Chen, S H; Gao, J S

2018-01-24

Objective: To investigate the impact of blood pressure and age on arterial stiffness in general population. Methods: Participants who took part in 2010, 2012 and 2014 Kailuan health examination were included. Data of brachial ankle pulse wave velocity (baPWV) examination were analyzed. According to the WHO criteria of age, participants were divided into 3 age groups: 18-44 years group ( n= 11 608), 45-59 years group ( n= 12 757), above 60 years group ( n= 5 002). Participants were further divided into hypertension group and non-hypertension group according to the diagnostic criteria for hypertension (2010 Chinese guidelines for the managemengt of hypertension). Multiple linear regression analysis was used to analyze the association between systolic blood pressure (SBP) with baPWV in the total participants and then stratified by age groups. Multivariate logistic regression model was used to analyze the influence of blood pressure on arterial stiffness (baPWV≥1 400 cm/s) of various groups. Results: (1)The baseline characteristics of all participants: 35 350 participants completed 2010, 2012 and 2014 Kailuan examinations and took part in baPWV examination. 2 237 participants without blood pressure measurement values were excluded, 1 569 participants with history of peripheral artery disease were excluded, we also excluded 1 016 participants with history of cardiac-cerebral vascular disease. Data from 29 367 participants were analyzed. The age was (48.0±12.4) years old, 21 305 were males (72.5%). (2) Distribution of baPWV in various age groups: baPWV increased with aging. In non-hypertension population, baPWV in 18-44 years group, 45-59 years group, above 60 years group were as follows: 1 299.3, 1 428.7 and 1 704.6 cm/s, respectively. For hypertension participants, the respective values of baPWV were: 1 498.4, 1 640.7 and 1 921.4 cm/s. BaPWV was significantly higher in hypertension group than non-hypertension group of respective age groups ( P< 0.05). (3) Multiple linear regression analysis defined risk factors of baPWV: Multivariate linear regression analysis showed that baPWV was positively correlated with SBP( t= 39.30, P< 0.001), and same results were found in the sub-age groups ( t -value was 37.72, 27.30, 9.15, all P< 0.001, respectively) after adjustment for other confounding factors, including age, sex, pulse pressure(PP), body mass index (BMI), fasting blood glucose (FBG), total cholesterol (TC), smoking, drinking, physical exercise, antihypertensive medications, lipid-lowering medication. (4) Multivariate logistic regression analysis of baPWV-related factors: After adjustment for other confounding factors, including age, sex, PP, BMI, FBG, TC, smoking, drinking, physical exercise, antihypertensive medication, lipid-lowering medication, multivariate logistic regression analysis showed that risks for increased arterial stiffness in hypertension group were higher than those in non-hypertension group, the OR in participants with hypertension was 2.54 (2.35-2.74) in the total participants, and same results were also found in sub-age groups, the OR s were 3.22(2.86-3.63), 2.48(2.23-2.76), and 1.91(1.42-2.56), respectively, in each sub-age group. Conclusion: SBP is positively related to arterial stiffness in different age groups, and hypertension is a risk factor for increased arterial stiffness in different age groups. Clinical Trial Registry Chinese Clinical Trial Registry, ChiCTR-TNC-11001489.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

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

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

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. 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. 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. 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. 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.

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

PubMed

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

2015-01-01

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

PREDICTION OF THE ACUTE TOXICITY OF ORGANIC COMPOUNDS TO THE FATHEAD MINNOW (PIMEPHALES PROMALAS) USING A GROUP CONTRIBUTION METHOD

EPA Science Inventory

A group contribution method has been developed to correlate the acute toxicity (96 h LC50) to the fathead minnow (Pimephales promelas) for 379 organic chemicals. Multilinear regression and computational neural networks (CNNs) were used for model building. The multilinear linear m...

Advanced microwave soil moisture studies. [Big Sioux River Basin, Iowa

NASA Technical Reports Server (NTRS)

Dalsted, K. J.; Harlan, J. C.

1983-01-01

Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.

Does Group-Level Commitment Predict Employee Well-Being?: A Prospective Analysis.

PubMed

Clausen, Thomas; Christensen, Karl Bang; Nielsen, Karina

2015-11-01

To investigate the links between group-level affective organizational commitment (AOC) and individual-level psychological well-being, self-reported sickness absence, and sleep disturbances. A total of 5085 care workers from 301 workgroups in the Danish eldercare services participated in both waves of the study (T1 [2005] and T2 [2006]). The three outcomes were analyzed using linear multilevel regression analysis, multilevel Poisson regression analysis, and multilevel logistic regression analysis, respectively. Group-level AOC (T1) significantly predicted individual-level psychological well-being, self-reported sickness absence, and sleep disturbances (T2). The association between group-level AOC (T1) and psychological well-being (T2) was fully mediated by individual-level AOC (T1), and the associations between group-level AOC (T1) and self-reported sickness absence and sleep disturbances (T2) were partially mediated by individual-level AOC (T1). Group-level AOC is an important predictor of employee well-being in contemporary health care organizations.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Circulating leptin and adiponectin are associated with insulin resistance in healthy postmenopausal women with hot flashes

PubMed Central

Huang, Wan-Yu; Chang, Chia-Chu; Chen, Dar-Ren; Kor, Chew-Teng; Chen, Ting-Yu; Wu, Hung-Ming

2017-01-01

Introduction Hot flashes have been postulated to be linked to the development of metabolic disorders. This study aimed to evaluate the relationship between hot flashes, adipocyte-derived hormones, and insulin resistance in healthy, non-obese postmenopausal women. Participants and design In this cross-sectional study, a total of 151 women aged 45–60 years were stratified into one of three groups according to hot-flash status over the past three months: never experienced hot flashes (Group N), mild-to-moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, fasting levels of circulating glucose, lipid profiles, plasma insulin, and adipocyte-derived hormones. Multiple linear regression analysis was used to evaluate the associations of hot flashes with adipocyte-derived hormones, and with insulin resistance. Settings The study was performed in a hospital medical center. Results The mean (standard deviation) of body-mass index was 22.8(2.7) for Group N, 22.6(2.6) for Group M, and 23.5(2.4) for Group S, respectively. Women in Group S displayed statistically significantly higher levels of leptin, fasting glucose, and insulin, and lower levels of adiponectin than those in Groups M and N. Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with higher leptin levels, lower adiponectin levels, and higher leptin-to-adiponectin ratio. Univariate linear regression analysis revealed that hot-flash severity was strongly associated with a higher HOMA-IR index (% difference, 58.03%; 95% confidence interval, 31.00–90.64; p < 0.001). The association between hot flashes and HOMA-IR index was attenuated after adjusting for leptin or adiponectin and was no longer significant after simultaneously adjusting for leptin and adiponectin. Conclusion The present study provides evidence that hot flashes are associated with insulin resistance in postmenopausal women. It further suggests that hot flash association with insulin resistance is dependent on the combination of leptin and adiponectin variables. PMID:28448547

Categorized or Continuous? Strength of an Association--And Linear Regression

ERIC Educational Resources Information Center

Drummond, Gordon B.; Vowler, Sarah L.

2012-01-01

In this article, the authors consider the possibility that groups could be different, because of the different conditions of a factor. This is as far as the analysis can extend: the consideration is restricted to groups characterized by the different category of the factor being considered. In many biological experiments, the factor considered may…

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Extension of the Peters–Belson method to estimate health disparities among multiple groups using logistic regression with survey data

PubMed Central

Li, Y.; Graubard, B. I.; Huang, P.; Gastwirth, J. L.

2015-01-01

Determining the extent of a disparity, if any, between groups of people, for example, race or gender, is of interest in many fields, including public health for medical treatment and prevention of disease. An observed difference in the mean outcome between an advantaged group (AG) and disadvantaged group (DG) can be due to differences in the distribution of relevant covariates. The Peters–Belson (PB) method fits a regression model with covariates to the AG to predict, for each DG member, their outcome measure as if they had been from the AG. The difference between the mean predicted and the mean observed outcomes of DG members is the (unexplained) disparity of interest. We focus on applying the PB method to estimate the disparity based on binary/multinomial/proportional odds logistic regression models using data collected from complex surveys with more than one DG. Estimators of the unexplained disparity, an analytic variance–covariance estimator that is based on the Taylor linearization variance–covariance estimation method, as well as a Wald test for testing a joint null hypothesis of zero for unexplained disparities between two or more minority groups and a majority group, are provided. Simulation studies with data selected from simple random sampling and cluster sampling, as well as the analyses of disparity in body mass index in the National Health and Nutrition Examination Survey 1999–2004, are conducted. Empirical results indicate that the Taylor linearization variance–covariance estimation is accurate and that the proposed Wald test maintains the nominal level. PMID:25382235

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

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

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

Efficacy of Social Media Adoption on Client Growth for Independent Management Consultants

DTIC Science & Technology

2017-02-01

design , a linear multiple regression with three predictor variables and one dependent variable per testing were used. Under those circumstances...regression test was used to compare the social media adoption of two groups on a single measure to determine if there was a statistical difference...number and types of social media platforms used and their influence on client growth was examined in this research design that used a descriptive

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Effect of Linear Low-Intensity Extracorporeal Shockwave Therapy for Erectile Dysfunction-12-Month Follow-Up of a Randomized, Double-Blinded, Sham-Controlled Study.

PubMed

Fojecki, Grzegorz Lukasz; Tiessen, Stefan; Osther, Palle Jørn Sloth

2018-03-01

Short-term data on the effect of low-intensity extracorporeal shockwave therapy (Li-ESWT) on erectile dysfunction (ED) have been inconsistent. The suggested mechanisms of action of Li-ESWT on ED include stimulation of cell proliferation, tissue regeneration, and angiogenesis, which can be processes with a long generation time. Therefore, long-term data on the effect of Li-ESWT on ED are strongly warranted. To assess the outcome at 6 and 12 months of linear Li-ESWT on ED from a previously published randomized, double-blinded, sham-controlled trial. Subjects with ED (N = 126) who scored lower than 25 points in the erectile function domain of the International Index of Erectile Function (IIEF-EF) were eligible for the study. They were allocated to 1 of 2 groups: 5 weekly sessions of sham treatment (group A) or linear Li-ESWT (group B). After a 4-week break, the 2 groups received active treatment once a week for 5 weeks. At baseline and 6 and 12 months, subjects were evaluated by the IIEF-EF, the Erectile Hardness Scale (EHS), and the Sexual Quality of Life in Men. The primary outcome measure was an increase of at least 5 points in the IIEF-EF (ΔIIEF-EF score). The secondary outcome measure was an increase in the EHS score to at least 3 in men with a score no higher than 2 at baseline. Data were analyzed by linear and logistic regressions. Linear regression of the ΔIIEF-EF score from baseline to 12 months included 95 patients (dropout rate = 25%). Adjusted for the IIEF-EF score at baseline, the difference between groups B and A was -1.30 (95% CI = -4.37 to 1.77, P = .4). The success rate based on the main outcome parameter (ΔIIEF-EF score ≥ 5) was 54% in group A vs 47% in group B (odds ratio = 0.67, P = .28). Improvement based on changes in the EHS score in groups A and B was 34% and 24%, respectively (odds ratio = 0.47, P = .82). Exposure to 2 cycles of linear Li-ESWT for ED is not superior to 1 cycle at 6- and 12-month follow-ups. Fojecki GL, Tiessen S, Osther PJS. Effect of Linear Low-Intensity Extracorporeal Shockwave Therapy for Erectile Dysfunction-12-Month Follow-Up of a Randomized, Double-Blinded, Sham-Controlled Study. Sex Med 2018;6:1-7. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

[Stature estimation for Sichuan Han nationality female based on X-ray technology with measurement of lumbar vertebrae].

PubMed

Qing, Si-han; Chang, Yun-feng; Dong, Xiao-ai; Li, Yuan; Chen, Xiao-gang; Shu, Yong-kang; Deng, Zhen-hua

2013-10-01

To establish the mathematical models of stature estimation for Sichuan Han female with measurement of lumbar vertebrae by X-ray to provide essential data for forensic anthropology research. The samples, 206 Sichuan Han females, were divided into three groups including group A, B and C according to the ages. Group A (206 samples) consisted of all ages, group B (116 samples) were 20-45 years old and 90 samples over 45 years old were group C. All the samples were examined lumbar vertebrae through CR technology, including the parameters of five centrums (L1-L5) as anterior border, posterior border and central heights (x1-x15), total central height of lumbar spine (x16), and the real height of every sample. The linear regression analysis was produced using the parameters to establish the mathematical models of stature estimation. Sixty-two trained subjects were tested to verify the accuracy of the mathematical models. The established mathematical models by hypothesis test of linear regression equation model were statistically significant (P<0.05). The standard errors of the equation were 2.982-5.004 cm, while correlation coefficients were 0.370-0.779 and multiple correlation coefficients were 0.533-0.834. The return tests of the highest correlation coefficient and multiple correlation coefficient of each group showed that the highest accuracy of the multiple regression equation, y = 100.33 + 1.489 x3 - 0.548 x6 + 0.772 x9 + 0.058 x12 + 0.645 x15, in group A were 80.6% (+/- lSE) and 100% (+/- 2SE). The established mathematical models in this study could be applied for the stature estimation for Sichuan Han females.

Determination and importance of temperature dependence of retention coefficient (RPHPLC) in QSAR model of nitrazepams' partition coefficient in bile acid micelles.

PubMed

Posa, Mihalj; Pilipović, Ana; Lalić, Mladena; Popović, Jovan

2011-02-15

Linear dependence between temperature (t) and retention coefficient (k, reversed phase HPLC) of bile acids is obtained. Parameters (a, intercept and b, slope) of the linear function k=f(t) highly correlate with bile acids' structures. Investigated bile acids form linear congeneric groups on a principal component (calculated from k=f(t)) score plot that are in accordance with conformations of the hydroxyl and oxo groups in a bile acid steroid skeleton. Partition coefficient (K(p)) of nitrazepam in bile acids' micelles is investigated. Nitrazepam molecules incorporated in micelles show modified bioavailability (depo effect, higher permeability, etc.). Using multiple linear regression method QSAR models of nitrazepams' partition coefficient, K(p) are derived on the temperatures of 25°C and 37°C. For deriving linear regression models on both temperatures experimentally obtained lipophilicity parameters are included (PC1 from data k=f(t)) and in silico descriptors of the shape of a molecule while on the higher temperature molecular polarisation is introduced. This indicates the fact that the incorporation mechanism of nitrazepam in BA micelles changes on the higher temperatures. QSAR models are derived using partial least squares method as well. Experimental parameters k=f(t) are shown to be significant predictive variables. Both QSAR models are validated using cross validation and internal validation method. PLS models have slightly higher predictive capability than MLR models. Copyright © 2010 Elsevier B.V. All rights reserved.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Prevalence and risk factors of non-carious cervical lesions related to occupational exposure to acid mists.

PubMed

Bomfim, Rafael Aiello; Crosato, Edgard; Mazzilli, Luiz Eugênio Nigro; Frias, Antonio Carlos

2015-01-01

This study evaluates the prevalence and risk factors of non-carious cervical lesions (NCCLs) in a Brazilian population of workers exposed and non-exposed to acid mists and chemical products. One hundred workers (46 exposed and 54 non-exposed) were evaluated in a Centro de Referência em Saúde do Trabalhador - CEREST (Worker's Health Reference Center). The workers responded to questionnaires regarding their personal information and about alcohol consumption and tobacco use. A clinical examination was conducted to evaluate the presence of NCCLs, according to WHO parameters. Statistical analyses were performed by unconditional logistic regression and multiple linear regression, with the critical level of p < 0.05. NCCLs were significantly associated with age groups (18-34, 35-44, 45-68 years). The unconditional logistic regression showed that the presence of NCCLs was better explained by age group (OR = 4.04; CI 95% 1.77-9.22) and occupational exposure to acid mists and chemical products (OR = 3.84; CI 95% 1.10-13.49), whereas the linear multiple regression revealed that NCCLs were better explained by years of smoking (p = 0.01) and age group (p = 0.04). The prevalence of NCCLs in the study population was particularly high (76.84%), and the risk factors for NCCLs were age, exposure to acid mists and smoking habit. Controlling risk factors through preventive and educative measures, allied to the use of personal protective equipment to prevent the occupational exposure to acid mists, may contribute to minimizing the prevalence of NCCLs.

Personal Well-being and Stress Symptoms in Wives of Iranian Martyrs, Prisoners of wars and Disabled Veterans

PubMed Central

Sharif, Nasim

2010-01-01

Objective This study was conducted to compare the personal well-being among the wives of Iranian veterans living in the city of Qom. Method A sample of 300 was randomly selected from a database containing the addresses of veteran's families at Iran's Veterans Foundation in Qom (Bonyad-e-Shahid va Omoore Isargaran). The veterans' wives were divided into three groups: wives of martyrs (killed veterans), wives of prisoners of war, and wives of disabled veterans. The Persian translation of Personal Well-being Index and Stress Symptoms Checklist (SSC) were administered for data collection. Four women chose not to respond to Personal Well-being Index. Data were then analyzed using linear multivariate regression (stepwise method), analysis of variance, and by computing the correlation between variables. Results Results showed a negative correlation between well-being and stress symptoms. However, each group demonstrated different levels of stress symptoms. Furthermore, multivariate linear regression in the 3 groups showed that overall satisfaction of life and personal well-being (total score and its domains) could be predicted by different symptoms. Conclusion Each group experienced different challenges and thus different stress symptoms. Therefore, although they all need help, each group needs to be helped in a different way. PMID:22952487

Association between the Type of Workplace and Lung Function in Copper Miners

PubMed Central

Gruszczyński, Leszek; Wojakowska, Anna; Ścieszka, Marek; Turczyn, Barbara; Schmidt, Edward

2016-01-01

The aim of the analysis was to retrospectively assess changes in lung function in copper miners depending on the type of workplace. In the groups of 225 operators, 188 welders, and 475 representatives of other jobs, spirometry was performed at the start of employment and subsequently after 10, 20, and 25 years of work. Spirometry Longitudinal Data Analysis software was used to estimate changes in group means for FEV1 and FVC. Multiple linear regression analysis was used to assess an association between workplace and lung function. Lung function assessed on the basis of calculation of longitudinal FEV1 (FVC) decline was similar in all studied groups. However, multiple linear regression model used in cross-sectional analysis revealed an association between workplace and lung function. In the group of welders, FEF75 was lower in comparison to operators and other miners as early as after 10 years of work. Simultaneously, in smoking welders, the FEV1/FVC ratio was lower than in nonsmokers (p < 0,05). The interactions between type of workplace and smoking (p < 0,05) in their effect on FVC, FEV1, PEF, and FEF50 were shown. Among underground working copper miners, the group of smoking welders is especially threatened by impairment of lung ventilatory function. PMID:27274987

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

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

PubMed

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

2016-01-01

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

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Ultra-endurance sports have no negative impact on indices of arterial stiffness.

PubMed

Radtke, Thomas; Schmidt-Trucksäss, Arno; Brugger, Nicolas; Schäfer, Daniela; Saner, Hugo; Wilhelm, Matthias

2014-01-01

Marathon running has been linked with higher arterial stiffness. Blood pressure is a major contributor to pulse wave velocity (PWV). We examined indices of arterial stiffness with a blood pressure-independent method in marathon runners and ultra-endurance athletes. Male normotensive amateur runners were allocated to three groups according to former participation in competitions: group I (recreational athletes), group II (marathon runners) and group III (ultra-endurance athletes). Indices of arterial stiffness were measured with a non-invasive device (VaSera VS-1500N, Fukuda Denshi, Japan) to determine the cardio-ankle vascular index (CAVI, primary endpoint) and brachial-ankle PWV (baPWV). Lifetime training hours were calculated. Cumulative competitions were expressed as marathon equivalents. Linear regression analysis was performed to determine predictors for CAVI and baPWV. Measurements of arterial stiffness were performed in 51 subjects (mean age 44.6 ± 1.2 years): group I (n = 16), group II (n = 19) and group III (n = 16). No between-group differences existed in age, anthropometric characteristics and resting BP. CAVI and baPWV were comparable between all groups (P = 0.604 and P = 0.947, respectively). In linear regression analysis, age was the only independent predictor for CAVI (R(2) = 0.239, β = 0.455, P = 0.001). Systolic BP was significantly associated with baPWV (R(2) = 0.225, β = 0.403, P = 0.004). In middle-aged normotensive athletes marathon running and ultra-endurance sports had no negative impact on arterial stiffness.

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Is It the Intervention or the Students? Using Linear Regression to Control for Student Characteristics in Undergraduate STEM Education Research

PubMed Central

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance. PMID:24591502

Is it the intervention or the students? using linear regression to control for student characteristics in undergraduate STEM education research.

PubMed

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance.

The Association of Sitting Time With Sarcopenia Status and Physical Performance at Baseline and 18-Month Follow-Up in the Residential Aged Care Setting.

PubMed

Reid, Natasha; Keogh, Justin W; Swinton, Paul; Gardiner, Paul A; Henwood, Timothy R

2018-06-18

This study investigated the association of sitting time with sarcopenia and physical performance in residential aged care residents at baseline and 18-month follow-up. Measures included the International Physical Activity Questionnaire (sitting time), European Working Group definition of sarcopenia, and the short physical performance battery (physical performance). Logistic regression and linear regression analyses were used to investigate associations. For each hour of sitting, the unadjusted odds ratio of sarcopenia was 1.16 (95% confidence interval [0.98, 1.37]). Linear regression showed that each hour of sitting was significantly associated with a 0.2-unit lower score for performance. Associations of baseline sitting with follow-up sarcopenia status and performance were nonsignificant. Cross-sectionally, increased sitting time in residential aged care may be detrimentally associated with sarcopenia and physical performance. Based on current reablement models of care, future studies should investigate if reducing sedentary time improves performance among adults in end of life care.

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

PubMed Central

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

2012-01-01

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

Social Inequality and Labor Force Participation.

ERIC Educational Resources Information Center

King, Jonathan

The labor force participation rates of whites, blacks, and Spanish-Americans, grouped by sex, are explained in a linear regression model fitted with 1970 U. S. Census data on Standard Metropolitan Statistical Area (SMSA). The explanatory variables are: average age, average years of education, vocational training rate, disabled rate, unemployment…

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

[Ultrasonic measurements of fetal thalamus, caudate nucleus and lenticular nucleus in prenatal diagnosis].

PubMed

Yang, Ruiqi; Wang, Fei; Zhang, Jialing; Zhu, Chonglei; Fan, Limei

2015-05-19

To establish the reference values of thalamus, caudate nucleus and lenticular nucleus diameters through fetal thalamic transverse section. A total of 265 fetuses at our hospital were randomly selected from November 2012 to August 2014. And the transverse and length diameters of thalamus, caudate nucleus and lenticular nucleus were measured. SPSS 19.0 statistical software was used to calculate the regression curve of fetal diameter changes and gestational weeks of pregnancy. P < 0.05 was considered as having statistical significance. The linear regression equation of fetal thalamic length diameter and gestational week was: Y = 0.051X+0.201, R = 0.876, linear regression equation of thalamic transverse diameter and fetal gestational week was: Y = 0.031X+0.229, R = 0.817, linear regression equation of fetal head of caudate nucleus length diameter and gestational age was: Y = 0.033X+0.101, R = 0.722, linear regression equation of fetal head of caudate nucleus transverse diameter and gestational week was: R = 0.025 - 0.046, R = 0.711, linear regression equation of fetal lentiform nucleus length diameter and gestational week was: Y = 0.046+0.229, R = 0.765, linear regression equation of fetal lentiform nucleus diameter and gestational week was: Y = 0.025 - 0.05, R = 0.772. Ultrasonic measurement of diameter of fetal thalamus caudate nucleus, and lenticular nucleus through thalamic transverse section is simple and convenient. And measurements increase with fetal gestational weeks and there is linear regression relationship between them.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

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

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

The Relationship of Diameter at Breast Height and Crown Diameter for Four Species Groups in Hardin County, Tennessee

Treesearch

Lawrence R. Gering; Dennis M. May

1995-01-01

A set of simple linear regression models for predicting diameter at breast height (dbh) from crown diamter and a set of similar models for predicting crown diamter from dbh were developed for four species groups in Harding County, TN. Data were obtained from 557 trees measured during hte 1989 USDA Southern Forest Experiment Station survey of the forest of Tennessee,...

Education, Genetic Ancestry, and Blood Pressure in African Americans and Whites

PubMed Central

Gravlee, Clarence C.; Mulligan, Connie J.

2012-01-01

Objectives. We assessed the relative roles of education and genetic ancestry in predicting blood pressure (BP) within African Americans and explored the association between education and BP across racial groups. Methods. We used t tests and linear regressions to examine the associations of genetic ancestry, estimated from a genomewide set of autosomal markers, and education with BP variation among African Americans in the Family Blood Pressure Program. We also performed linear regressions in self-identified African Americans and Whites to explore the association of education with BP across racial groups. Results. Education, but not genetic ancestry, significantly predicted BP variation in the African American subsample (b = −0.51 mm Hg per year additional education; P = .001). Although education was inversely associated with BP in the total population, within-group analyses showed that education remained a significant predictor of BP only among the African Americans. We found a significant interaction (b = 3.20; P = .006) between education and self-identified race in predicting BP. Conclusions. Racial disparities in BP may be better explained by differences in education than by genetic ancestry. Future studies of ancestry and disease should include measures of the social environment. PMID:22698014

Education, genetic ancestry, and blood pressure in African Americans and Whites.

PubMed

Non, Amy L; Gravlee, Clarence C; Mulligan, Connie J

2012-08-01

We assessed the relative roles of education and genetic ancestry in predicting blood pressure (BP) within African Americans and explored the association between education and BP across racial groups. We used t tests and linear regressions to examine the associations of genetic ancestry, estimated from a genomewide set of autosomal markers, and education with BP variation among African Americans in the Family Blood Pressure Program. We also performed linear regressions in self-identified African Americans and Whites to explore the association of education with BP across racial groups. Education, but not genetic ancestry, significantly predicted BP variation in the African American subsample (b=-0.51 mm Hg per year additional education; P=.001). Although education was inversely associated with BP in the total population, within-group analyses showed that education remained a significant predictor of BP only among the African Americans. We found a significant interaction (b=3.20; P=.006) between education and self-identified race in predicting BP. Racial disparities in BP may be better explained by differences in education than by genetic ancestry. Future studies of ancestry and disease should include measures of the social environment.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Complexity analysis of spontaneous brain activity in mood disorders: A magnetoencephalography study of bipolar disorder and major depression.

PubMed

Fernández, Alberto; Al-Timemy, Ali H; Ferre, Francisco; Rubio, Gabriel; Escudero, Javier

2018-04-26

The lack of a biomarker for Bipolar Disorder (BD) causes problems in the differential diagnosis with other mood disorders such as major depression (MD), and misdiagnosis frequently occurs. Bearing this in mind, we investigated non-linear magnetoencephalography (MEG) patterns in BD and MD. Lempel-Ziv Complexity (LZC) was used to evaluate the resting-state MEG activity in a cross-sectional sample of 60 subjects, including 20 patients with MD, 16 patients with BD type-I, and 24 control (CON) subjects. Particular attention was paid to the role of age. The results were aggregated by scalp region. Overall, MD patients showed significantly higher LZC scores than BD patients and CONs. Linear regression analyses demonstrated distinct tendencies of complexity progression as a function of age, with BD patients showing a divergent tendency as compared with MD and CON groups. Logistic regressions confirmed such distinct relationship with age, which allowed the classification of diagnostic groups. The patterns of neural complexity in BD and MD showed not only quantitative differences in their non-linear MEG characteristics but also divergent trajectories of progression as a function of age. Moreover, neural complexity patterns in BD patients resembled those previously observed in schizophrenia, thus supporting preceding evidence of common neuropathological processes. Copyright © 2018 Elsevier Inc. All rights reserved.

Glomerular structural-functional relationship models of diabetic nephropathy are robust in type 1 diabetic patients.

PubMed

Mauer, Michael; Caramori, Maria Luiza; Fioretto, Paola; Najafian, Behzad

2015-06-01

Studies of structural-functional relationships have improved understanding of the natural history of diabetic nephropathy (DN). However, in order to consider structural end points for clinical trials, the robustness of the resultant models needs to be verified. This study examined whether structural-functional relationship models derived from a large cohort of type 1 diabetic (T1D) patients with a wide range of renal function are robust. The predictability of models derived from multiple regression analysis and piecewise linear regression analysis was also compared. T1D patients (n = 161) with research renal biopsies were divided into two equal groups matched for albumin excretion rate (AER). Models to explain AER and glomerular filtration rate (GFR) by classical DN lesions in one group (T1D-model, or T1D-M) were applied to the other group (T1D-test, or T1D-T) and regression analyses were performed. T1D-M-derived models explained 70 and 63% of AER variance and 32 and 21% of GFR variance in T1D-M and T1D-T, respectively, supporting the substantial robustness of the models. Piecewise linear regression analyses substantially improved predictability of the models with 83% of AER variance and 66% of GFR variance explained by classical DN glomerular lesions alone. These studies demonstrate that DN structural-functional relationship models are robust, and if appropriate models are used, glomerular lesions alone explain a major proportion of AER and GFR variance in T1D patients. © The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Smooth individual level covariates adjustment in disease mapping.

PubMed

Huque, Md Hamidul; Anderson, Craig; Walton, Richard; Woolford, Samuel; Ryan, Louise

2018-05-01

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

Comparison of exercise capacity in COPD and other etiologies of chronic respiratory failure requiring non-invasive mechanical ventilation at home: retrospective analysis of 1-year follow-up.

PubMed

Salturk, Cuneyt; Karakurt, Zuhal; Takir, Huriye Berk; Balci, Merih; Kargin, Feyza; Mocin, Ozlem Yazıcıoglu; Gungor, Gokay; Ozmen, Ipek; Oztas, Selahattin; Yalcinsoy, Murat; Evin, Ruya; Ozturk, Murat; Adiguzel, Nalan

2015-01-01

The objective of this study was to compare the change in 6-minute walking distance (6MWD) in 1 year as an indicator of exercise capacity among patients undergoing home non-invasive mechanical ventilation (NIMV) due to chronic hypercapnic respiratory failure (CHRF) caused by different etiologies. This retrospective cohort study was conducted in a tertiary pulmonary disease hospital in patients who had completed 1-year follow-up under home NIMV because of CHRF with different etiologies (ie, chronic obstructive pulmonary disease [COPD], obesity hypoventilation syndrome [OHS], kyphoscoliosis [KS], and diffuse parenchymal lung disease [DPLD]), between January 2011 and January 2012. The results of arterial blood gas (ABG) analyses and spirometry, and 6MWD measurements with 12-month interval were recorded from the patient files, in addition to demographics, comorbidities, and body mass indices. The groups were compared in terms of 6MWD via analysis of variance (ANOVA) and multiple linear regression (MLR) analysis (independent variables: analysis age, sex, baseline 6MWD, baseline forced expiratory volume in 1 second, and baseline partial carbon dioxide pressure, in reference to COPD group). A total of 105 patients with a mean age (± standard deviation) of 61±12 years of whom 37 had COPD, 34 had OHS, 20 had KS, and 14 had DPLD were included in statistical analysis. There were no significant differences between groups in the baseline and delta values of ABG and spirometry findings. Both univariate ANOVA and MLR showed that the OHS group had the lowest baseline 6MWD and the highest decrease in 1 year (linear regression coefficient -24.48; 95% CI -48.74 to -0.21, P=0.048); while the KS group had the best baseline values and the biggest improvement under home NIMV (linear regression coefficient 26.94; 95% CI -3.79 to 57.66, P=0.085). The 6MWD measurements revealed improvement in exercise capacity test in CHRF patients receiving home NIMV treatment on long-term depends on etiological diagnoses.

Disturbances of automatic gait control mechanisms in higher level gait disorder.

PubMed

Danoudis, Mary; Ganesvaran, Ganga; Iansek, Robert

2016-07-01

The underlying mechanisms responsible for the gait changes in frontal gait disorder (FGD), a form of higher level gait disorders, are poorly understood. We investigated the relationship between stride length and cadence (SLCrel) in people with FGD (n=15) in comparison to healthy older adults (n=21) to improve our understanding of the changes to gait in FGD. Gait data was captured using an electronic walkway system as participants walked at five self-selected speed conditions: preferred, very slow, slow, fast and very fast. Linear regression was used to determine the strength of the relationship (R(2)), slope and intercept. In the FGD group 9 participants had a strong SLCrel (linear group) (R(2)>0.8) and 6 a weak relationship (R(2)<0.8) (nonlinear group). The linear FGD group did not differ to healthy control for slope (p>0.05) but did have a lower intercept (p<0.001). The linear FGD group modulated gait speed by adjusting stride length and cadence similar to controls whereas the nonlinear FGD participants adjusted stride length but not cadence similar to controls. The non-linear FGD group had greater disturbance to their gait, poorer postural control and greater fear of falling compared to the linear FGD group. Investigation of the SLCrel resulted in new insights into the underlying mechanisms responsible for the gait changes found in FGD. The findings suggest stride length regulation was disrupted in milder FGD but as the disorder worsened, cadence control also became disordered resulting in a break down in the relationship between stride length and cadence. Copyright © 2016 Elsevier B.V. All rights reserved.

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs.

PubMed

Mussin, Nadiar; Sumo, Marco; Lee, Kwang-Woong; Choi, YoungRok; Choi, Jin Yong; Ahn, Sung-Woo; Yoon, Kyung Chul; Kim, Hyo-Sin; Hong, Suk Kyun; Yi, Nam-Joon; Suh, Kyung-Suk

2017-04-01

Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs . Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R 2 = 0.719), than for the Rapidia group (R 2 = 0.688). Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation.

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

PubMed

Marill, Keith A

2004-01-01

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

Anti-mullerian hormone is higher in seizure-free women with epilepsy compared to those with ongoing seizures.

PubMed

Harden, Cynthia L; Pennell, Page B; French, Jacqueline A; Davis, Anne; Lau, Connie; Llewellyn, Nichelle; Kaufman, Benjamin; Bagiella, Emilia; Kirshenbaum, Ariel

2016-11-01

To determine if anti-mullerian hormone (AMH), a neuroactive peptide hormone and a measure of ovarian reserve, is different between women with epilepsy (WWE) and healthy controls (HC) seeking pregnancy and to evaluate epilepsy-related factors associated with AMH concentrations. Subjects were participants in Women with Epilepsy: Pregnancy Outcomes and Deliveries (WEPOD), a multi-center prospective, observational cohort study evaluating fecundity in WWE compared to HC, ages 18-40 years. WWE were divided into a Sz+ group or a Sz- group, dependent on whether they had seizures within the 9 months prior to enrollment. Serum was collected, and AMH concentrations were measured as an exploratory analysis. Linear and logistic regression models were used to assess associations and control for covariates. Serum AMH concentrations were measured in 72 out of 90 enrolled WWE and 97 out of 109 HC; the remaining subjects became pregnant before serum was obtained. Thirty WWE were in the Sz+ group and 40 in the Sz- group (retrospective seizure information was missing for two). All AMH concentrations were within the range, however, the normal inverse correlation between age and AMH was present in the HC and in the Sz- groups, but was lacking in the Sz+ group. Mean AMH concentration was higher in the Sz- group (3982pg/ml (SD+/-2452)) compared to the Sz+ group of WWE (2776pg/ml (SD+/-2308)) and HCs (3241 (SD±2647)). All values were within the expected range for age. In WWE, by linear regression, after controlling for age and BMI, seizure occurrence remained associated with AMH (p=0.025). In the prospective phase of the study, AMH concentrations were also associated with seizure occurrence during the menstrual cycle in which the serum sample was obtained (p=0.012). Antiepileptic drugs and other epilepsy factors were not associated with AMH concentrations. When analyzing the Sz- WWE group and the HC group by linear regression with AMH as the dependent variable, after controlling for age and BMI, the association with AMH was also present (p=0.017). AMH concentrations of the Sz+ group and HCs did not differ. In this exploratory analysis, seizure freedom was associated with higher AMH concentrations compared to women with ongoing seizures and to HCs. Future studies should further investigate the mechanism of the association of AMH with seizure occurrence, whether AMH could have a direct seizure-protective neuroactive hormone effect, as well as implications of AMH concentrations as a biomarker for ovarian reserve in women with epilepsy. Copyright © 2016 Elsevier B.V. All rights reserved.

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

The isoform A of reticulon-4 (Nogo-A) in cerebrospinal fluid of primary brain tumor patients: influencing factors.

PubMed

Koper, Olga Martyna; Kamińska, Joanna; Milewska, Anna; Sawicki, Karol; Mariak, Zenon; Kemona, Halina; Matowicka-Karna, Joanna

2018-05-18

The influence of isoform A of reticulon-4 (Nogo-A), also known as neurite outgrowth inhibitor, on primary brain tumor development was reported. Therefore the aim was the evaluation of Nogo-A concentrations in cerebrospinal fluid (CSF) and serum of brain tumor patients compared with non-tumoral individuals. All serum results, except for two cases, obtained both in brain tumors and non-tumoral individuals, were below the lower limit of ELISA detection. Cerebrospinal fluid Nogo-A concentrations were significantly lower in primary brain tumor patients compared to non-tumoral individuals. The univariate linear regression analysis found that if white blood cell count increases by 1 × 10 3 /μL, the mean cerebrospinal fluid Nogo-A concentration value decreases 1.12 times. In the model of multiple linear regression analysis predictor variables influencing cerebrospinal fluid Nogo-A concentrations included: diagnosis, sex, and sodium level. The mean cerebrospinal fluid Nogo-A concentration value was 1.9 times higher for women in comparison to men. In the astrocytic brain tumor group higher sodium level occurs with lower cerebrospinal fluid Nogo-A concentrations. We found the opposite situation in non-tumoral individuals. Univariate linear regression analysis revealed, that cerebrospinal fluid Nogo-A concentrations change in relation to white blood cell count. In the created model of multiple linear regression analysis we found, that within predictor variables influencing CSF Nogo-A concentrations were diagnosis, sex, and sodium level. Results may be relevant to the search for cerebrospinal fluid biomarkers and potential therapeutic targets in primary brain tumor patients. Nogo-A concentrations were tested by means of enzyme-linked immunosorbent assay (ELISA).

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

LAS bioconcentration is isomer specific

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

Tolls, J.; Haller, M.; Graaf, I. de

1995-12-31

The authors measured parent compound specific bioconcentration data for linear alkylbenzene sulfonates in Pimephales promelas. They did so by using cold, custom synthesized sulfophenyl alkanes. They observed that, within homologous series of isomers, the uptake rate constants (k{sub 1}) and the bioconcentration factor (BCF) increase with increasing number of carbon atoms in the alkyl chain (n{sub C-atoms}). In contrast, the elimination rate constant k{sub 2} appears to be independent of the alkyl chain length. Regressions of log BCF vs n{sub C-atoms} yielded different slopes for the homologous groups of the 5- and the 2-sulfophenyl alkane isomers. Regression of all logmore » BCF-data vs log 1/CMC yielded a good description of the data. However, when regressing the data for both homologous series separately again very different slopes are obtained. The results therefore indicate that hydrophobicity-bioconcentration relationships may be different for different homologous groups of sulfophenyl alkanes.« less

A Regression Framework for Effect Size Assessments in Longitudinal Modeling of Group Differences

PubMed Central

Feingold, Alan

2013-01-01

The use of growth modeling analysis (GMA)--particularly multilevel analysis and latent growth modeling--to test the significance of intervention effects has increased exponentially in prevention science, clinical psychology, and psychiatry over the past 15 years. Model-based effect sizes for differences in means between two independent groups in GMA can be expressed in the same metric (Cohen’s d) commonly used in classical analysis and meta-analysis. This article first reviews conceptual issues regarding calculation of d for findings from GMA and then introduces an integrative framework for effect size assessments that subsumes GMA. The new approach uses the structure of the linear regression model, from which effect sizes for findings from diverse cross-sectional and longitudinal analyses can be calculated with familiar statistics, such as the regression coefficient, the standard deviation of the dependent measure, and study duration. PMID:23956615

Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities

USGS Publications Warehouse

Duross, Christopher; Olig, Susan; Schwartz, David

2015-01-01

Prior to calculating time-independent and -dependent earthquake probabilities for faults in the Wasatch Front region, the Working Group on Utah Earthquake Probabilities (WGUEP) updated a seismic-source model for the region (Wong and others, 2014) and evaluated 19 historical regressions on earthquake magnitude (M). These regressions relate M to fault parameters for historical surface-faulting earthquakes, including linear fault length (e.g., surface-rupture length [SRL] or segment length), average displacement, maximum displacement, rupture area, seismic moment (Mo ), and slip rate. These regressions show that significant epistemic uncertainties complicate the determination of characteristic magnitude for fault sources in the Basin and Range Province (BRP). For example, we found that M estimates (as a function of SRL) span about 0.3–0.4 units (figure 1) owing to differences in the fault parameter used; age, quality, and size of historical earthquake databases; and fault type and region considered.

The association between prolonged sleep onset latency and heart rate dynamics among young sleep-onset insomniacs and good sleepers.

PubMed

Tsai, Hsin-Jung; Kuo, Terry B J; Lin, Yu-Cheng; Yang, Cheryl C H

2015-12-30

A blunting of heart rate (HR) reduction during sleep has been reported to be associated with increased all-cause mortality. An increased incident of cardiovascular events has been observed in patients with insomnia but the relationship between nighttime HR and insomnia remains unclear. Here we investigated the HR patterns during the sleep onset period and its association with the length of sleep onset latency (SOL). Nineteen sleep-onset insomniacs (SOI) and 14 good sleepers had their sleep analyzed. Linear regression and nonlinear Hilbert-Huang transform (HHT) of the HR slope were performed in order to analyze HR dynamics during the sleep onset period. A significant depression in HR fluctuation was identified among the SOI group during the sleep onset period when linear regression and HHT analysis were applied. The magnitude of the HR reduction was associated with both polysomnography-defined and subjective SOL; moreover, we found that the linear regression and HHT slopes of the HR showed great sensitivity with respect to sleep quality. Our findings indicate that HR dynamics during the sleep onset period are sensitive to sleep initiation difficulty and respond to the SOL, which indicates that the presence of autonomic dysfunction would seem to affect the progress of falling asleep. Copyright © 2015. Published by Elsevier Ireland Ltd.

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

[From clinical judgment to linear regression model.

PubMed

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

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Linearity versus Nonlinearity of Offspring-Parent Regression: An Experimental Study of Drosophila Melanogaster

PubMed Central

Gimelfarb, A.; Willis, J. H.

1994-01-01

An experiment was conducted to investigate the offspring-parent regression for three quantitative traits (weight, abdominal bristles and wing length) in Drosophila melanogaster. Linear and polynomial models were fitted for the regressions of a character in offspring on both parents. It is demonstrated that responses by the characters to selection predicted by the nonlinear regressions may differ substantially from those predicted by the linear regressions. This is true even, and especially, if selection is weak. The realized heritability for a character under selection is shown to be determined not only by the offspring-parent regression but also by the distribution of the character and by the form and strength of selection. PMID:7828818

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

PubMed

Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C

2014-03-01

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

A regular yoga intervention for staff nurse sleep quality and work stress: a randomised controlled trial.

PubMed

Fang, Ronghua; Li, Xia

2015-12-01

Although many studies have assessed the efficacy of yoga in older individuals, minimal research has focused on how nurses use yoga to improve sleep quality and to reduce work stress after work hours. We used the Pittsburgh Sleep Quality Index in Chinese and the Questionnaire on Medical Worker's Stress in Chinese to determine the impact of yoga on the quality of sleep and work stress of staff nurses employed by a general hospital in China. Disturbances in the circadian rhythm interrupt an individual's pattern of sleep. Convenient sampling method. One hundred and twenty nurses were randomised into two groups: a yoga group and a non-yoga group. The yoga group performed yoga more than two times every week for 50-60 minutes each time after work hours. The NG group did not participate in yoga. After six months, self-reported sleep quality and work stress were compared between the two groups, and then we used linear regression to confirm the independent factors related to sleep quality. Nurses in the yoga group had better sleep quality and lower work stress compared with nurses in the non-yoga group. The linear regression model indicated that nursing experience, age and yoga intervention were significantly related to sleep quality. Regular yoga can improve sleep quality and reduce work stress in staff nurses. This study provides evidence that hospital management should pay attention to nurse sleep quality and work stress, thereby taking corresponding measures to reduce work pressure and improve health outcomes. © 2015 John Wiley & Sons Ltd.

Effects of Various Cryotherapy Applications on Postoperative Pain in Molar Teeth with Symptomatic Apical Periodontitis: A Preliminary Randomized Prospective Clinical Trial.

PubMed

Gundogdu, Eyup Candas; Arslan, Hakan

2018-03-01

The purpose of the study was to evaluate the effects of intracanal, intraoral, and extraoral cryotherapy on postoperative pain in molar teeth with symptomatic apical periodontitis. A total of 100 patients were randomly distributed into 4 groups: control (without cryotherapy application), intracanal cryotherapy application, intraoral cryotherapy application, and extraoral cryotherapy application. The postoperative pain of the patients was recorded at the first, third, fifth, and seventh days. The data were statistically analyzed by using linear regression, χ 2 , one-way analysis of variance, Tukey post hoc, and Kruskal-Wallis H tests (P = .05). There were no statistically significant differences among the groups in terms of demographic data (P > .05). The preoperative pain levels and preoperative visual analogue scale (VAS) scores of pain on percussion were similar among the groups (P > .05). The linear regression analysis demonstrated that group variable had the most significant effect on postoperative pain at day 1 (P < .001) among the other variables (group, age, gender, tooth number, preoperative pain levels, and VAS scores of pain on percussion). When compared with the control group, all the cryotherapy groups exhibited less percussion pain and less postoperative pain at the first, third, fifth, and seventh days (P < .05). Within the study limitations, all the cryotherapy applications (intracanal, intraoral, and extraoral) resulted in lower postoperative pain levels and lower VAS scores of pain on percussion versus those of the control group. Copyright © 2017 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Continuous infusion of low-dose unfractionated heparin after aneurysmal subarachnoid hemorrhage: a preliminary study of cognitive outcomes.

PubMed

James, Robert F; Khattar, Nicolas K; Aljuboori, Zaid S; Page, Paul S; Shao, Elaine Y; Carter, Lacey M; Meyer, Kimberly S; Daniels, Michael W; Craycroft, John; Gaughen, John R; Chaudry, M Imran; Rai, Shesh N; Everhart, D Erik; Simard, J Marc

2018-05-11

OBJECTIVE Cognitive dysfunction occurs in up to 70% of aneurysmal subarachnoid hemorrhage (aSAH) survivors. Low-dose intravenous heparin (LDIVH) infusion using the Maryland protocol was recently shown to reduce clinical vasospasm and vasospasm-related infarction. In this study, the Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive changes in aSAH patients treated with the Maryland LDIVH protocol compared with controls. METHODS A retrospective analysis of all patients treated for aSAH between July 2009 and April 2014 was conducted. Beginning in 2012, aSAH patients were treated with LDIVH in the postprocedural period. The MoCA was administered to all aSAH survivors prospectively during routine follow-up visits, at least 3 months after aSAH, by trained staff blinded to treatment status. Mean MoCA scores were compared between groups, and regression analyses were performed for relevant factors. RESULTS No significant differences in baseline characteristics were observed between groups. The mean MoCA score for the LDIVH group (n = 25) was 26.4 compared with 22.7 in controls (n = 22) (p = 0.013). Serious cognitive impairment (MoCA ≤ 20) was observed in 32% of controls compared with 0% in the LDIVH group (p = 0.008). Linear regression analysis demonstrated that only LDIVH was associated with a positive influence on MoCA scores (β = 3.68, p =0.019), whereas anterior communicating artery aneurysms and fevers were negatively associated with MoCA scores. Multivariable linear regression analysis resulted in all 3 factors maintaining significance. There were no treatment complications. CONCLUSIONS This preliminary study suggests that the Maryland LDIVH protocol may improve cognitive outcomes in aSAH patients. A randomized controlled trial is needed to determine the safety and potential benefit of unfractionated heparin in aSAH patients.

Oxygen Costs of the Incremental Shuttle Walk Test in Cardiac Rehabilitation Participants: An Historical and Contemporary Analysis.

PubMed

Buckley, John P; Cardoso, Fernando M F; Birkett, Stefan T; Sandercock, Gavin R H

2016-12-01

The incremental shuttle walk test (ISWT) is a standardised assessment for cardiac rehabilitation. Three studies have reported oxygen costs (VO 2 )/metabolic equivalents (METs) of the ISWT. In spite of classic representations from these studies graphically showing curvilinear VO 2 responses to incremented walking speeds, linear regression techniques (also used by the American College of Sports Medicine [ACSM]) have been used to estimate VO 2 . The two main aims of this study were to (i) resolve currently reported discrepancies in the ISWT VO 2 -walking speed relationship, and (ii) derive an appropriate VO 2 versus walking speed regression equation. VO 2 was measured continuously during an ISWT in 32 coronary heart disease [cardiac] rehabilitation (CHD-CR) participants and 30 age-matched controls. Both CHD-CR and control group VO 2 responses were curvilinear in nature. For CHD-CR VO 2  = 4.4e 0.23 × walkingspeed (km/h) . The integrated area under the curve (iAUC) VO 2 across nine ISWT stages was greater in the CHD-CR group versus the control group (p < 0.001): CHD-CR = 423 (±86) ml·kg -1 ·min -1 ·km·h -1 ; control = 316 (±52) ml·kg -1 ·min -1 ·km·h -1 . CHD-CR group vs. control VO 2 was up to 30 % greater at higher ISWT stages. The curvilinear nature of VO 2 responses during the ISWT concur with classic studies reported over 100 years. VO 2 estimates for walking using linear regression models (including the ACSM) clearly underestimate values in healthy and CHD-CR participants, and this study provides a resolution to this when the ISWT is used for CHD-CR populations.

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

Control Variate Selection for Multiresponse Simulation.

DTIC Science & Technology

1987-05-01

M. H. Knuter, Applied Linear Regression Mfodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F., Probability, Allyn and Bacon...1982. Neter, J., V. Wasserman, and M. H. Knuter, Applied Linear Regression .fodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F...Aspects of J%,ultivariate Statistical Theory, John Wiley and Sons, New York, New York, 1982. dY Neter, J., W. Wasserman, and M. H. Knuter, Applied Linear Regression Mfodels

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Tumour regression of uveal melanoma after ruthenium-106 brachytherapy or stereotactic radiotherapy with gamma knife or linear accelerator.

PubMed

Georgopoulos, Michael; Zehetmayer, Martin; Ruhswurm, Irene; Toma-Bstaendig, Sabine; Ségur-Eltz, Nikolaus; Sacu, Stefan; Menapace, Rupert

2003-01-01

This study assesses differences in relative tumour regression and internal acoustic reflectivity after 3 methods of radiotherapy for uveal melanoma: (1) brachytherapy with ruthenium-106 radioactive plaques (RU), (2) fractionated high-dose gamma knife stereotactic irradiation in 2-3 fractions (GK) or (3) fractionated linear-accelerator-based stereotactic teletherapy in 5 fractions (Linac). Ultrasound measurements of tumour thickness and internal reflectivity were performed with standardised A scan pre-operatively and 3, 6, 9, 12, 18, 24 and 36 months postoperatively. Of 211 patients included in the study, 111 had a complete 3-year follow-up (RU: 41, GK: 37, Linac: 33). Differences in tumour thickness and internal reflectivity were assessed with analysis of variance, and post hoc multiple comparisons were calculated with Tukey's honestly significant difference test. Local tumour control was excellent with all 3 methods (>93%). At 36 months, relative tumour height reduction was 69, 50 and 30% after RU, GK and Linac, respectively. In all 3 treatment groups, internal reflectivity increased from about 30% initially to 60-70% 3 years after treatment. Brachytherapy with ruthenium-106 plaques results in a faster tumour regression as compared to teletherapy with gamma knife or Linac. Internal reflectivity increases comparably in all 3 groups. Besides tumour growth arrest, increasing internal reflectivity is considered as an important factor indicating successful treatment. Copyright 2003 S. Karger AG, Basel

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

PubMed Central

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

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Psychiatric Characteristics of the Cardiac Outpatients with Chest Pain.

PubMed

Lee, Jea-Geun; Choi, Joon Hyouk; Kim, Song-Yi; Kim, Ki-Seok; Joo, Seung-Jae

2016-03-01

A cardiologist's evaluation of psychiatric symptoms in patients with chest pain is rare. This study aimed to determine the psychiatric characteristics of patients with and without coronary artery disease (CAD) and explore their relationship with the intensity of chest pain. Out of 139 consecutive patients referred to the cardiology outpatient department, 31 with atypical chest pain (heartburn, acid regurgitation, dyspnea, and palpitation) were excluded and 108 were enrolled for the present study. The enrolled patients underwent complete numerical rating scale of chest pain and the symptom checklist for minor psychiatric disorders at the time of first outpatient visit. The non-CAD group consisted of patients with a normal stress test, coronary computed tomography angiogram, or coronary angiogram, and the CAD group included those with an abnormal coronary angiogram. Nineteen patients (17.6%) were diagnosed with CAD. No differences in the psychiatric characteristics were observed between the groups. "Feeling tense", "self-reproach", and "trouble falling asleep" were more frequently observed in the non-CAD (p=0.007; p=0.046; p=0.044) group. In a multiple linear regression analysis with a stepwise selection, somatization without chest pain in the non-CAD group and hypochondriasis in the CAD group were linearly associated with the intensity of chest pain (β=0.108, R(2)=0.092, p=0.004; β= -0.525, R(2)=0.290, p=0.010). No differences in psychiatric characteristics were observed between the groups. The intensity of chest pain was linearly associated with somatization without chest pain in the non-CAD group and inversely linearly associated with hypochondriasis in the CAD group.

Applicability of Cameriere's and Drusini's age estimation methods to a sample of Turkish adults.

PubMed

Hatice, Boyacioglu Dogru; Nihal, Avcu; Nursel, Akkaya; Humeyra Ozge, Yilanci; Goksuluk, Dincer

2017-10-01

The aim of this study was to investigate the applicability of Drusini's and Cameriere's methods to a sample of Turkish people. Panoramic images of 200 individuals were allocated into two groups as study and test groups and examined by two observers. Tooth coronal indexes (TCI), which is the ratio between coronal pulp cavity height and crown height, were calculated in the mandibular first and second premolars and molars. Pulp/tooth area ratios (ARs) were calculated in the maxillary and mandibular canine teeth. Study group measurements were used to derive a regression model. Test group measurements were used to evaluate the accuracy of the regression model. Pearson's correlation coefficients and regression analysis were used. The correlations between TCIs and age were -0.230, -0.301, -0.344 and -0.257 for mandibular first premolar, second premolar, first molar and second molar, respectively. Those for the maxillary canine (MX) and mandibular canine (MN) ARs were -0.716 and -0.514, respectively. The MX ARs were used to build the linear regression model that explained 51.2% of the total variation, with a standard error of 9.23 years. The mean error of the estimates in test group was 8 years and age of 64% of the individuals were estimated with an error of <±10 years which is acceptable in forensic age prediction. The low correlation coefficients between age and TCI indicate that Drusini's method was not applicable to the estimation of age in a Turkish population. Using Cameriere's method, we derived a regression model.

Effect of mobile phone use on metal ion release from fixed orthodontic appliances.

PubMed

Saghiri, Mohammad Ali; Orangi, Jafar; Asatourian, Armen; Mehriar, Peiman; Sheibani, Nader

2015-06-01

The aim of this study was to evaluate the effect of exposure to radiofrequency electromagnetic fields emitted by mobile phones on the level of nickel in saliva. Fifty healthy patients with fixed orthodontic appliances were asked not to use their cell phones for a week, and their saliva samples were taken at the end of the week (control group). The patients recorded their time of mobile phone usage during the next week and returned for a second saliva collection (experimental group). Samples at both times were taken between 8:00 and 10:00 pm, and the nickel levels were measured. Two-tailed paired-samples t test, linear regression, independent t test, and 1-way analysis of variance were used for data analysis. The 2-tailed paired-samples t test showed significant differences between the levels of nickel in the control and experimental groups (t [49] = 9.967; P <0.001). The linear regression test showed a significant relationship between mobile phone usage time and the nickel release (F [1, 48] = 60.263; P <0.001; R(2) = 0.577). Mobile phone usage has a time-dependent influence on the concentration of nickel in the saliva of patients with orthodontic appliances. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Visual Field Outcomes for the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT).

PubMed

Wall, Michael; Johnson, Chris A; Cello, Kimberly E; Zamba, K D; McDermott, Michael P; Keltner, John L

2016-03-01

The Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) showed that acetazolamide provided a modest, significant improvement in mean deviation (MD). Here, we further analyze visual field changes over the 6-month study period. Of 165 subjects with mild visual loss in the IIHTT, 125 had perimetry at baseline and 6 months. We evaluated pointwise linear regression of visual sensitivity versus time to classify test locations in the worst MD (study) eye as improving or not; pointwise changes from baseline to month 6 in decibels; and clinical consensus of change from baseline to 6 months. The average study eye had 36 of 52 test locations with improving sensitivity over 6 months using pointwise linear regression, but differences between the acetazolamide and placebo groups were not significant. Pointwise results mostly improved in both treatment groups with the magnitude of the mean change within groups greatest and statistically significant around the blind spot and the nasal area, especially in the acetazolamide group. The consensus classification of visual field change from baseline to 6 months in the study eye yielded percentages (acetazolamide, placebo) of 7.2% and 17.5% worse, 35.1% and 31.7% with no change, and 56.1% and 50.8% improved; group differences were not statistically significant. In the IIHTT, compared to the placebo group, the acetazolamide group had a significant pointwise improvement in visual field function, particularly in the nasal and pericecal areas; the latter is likely due to reduction in blind spot size related to improvement in papilledema. (ClinicalTrials.gov number, NCT01003639.).

Health-Related Quality of Life in Children and Adolescents with Hereditary Bleeding Disorders and in Children and Adolescents with Stroke: Cross-Sectional Comparison to Siblings and Peers

PubMed Central

Neuner, Bruno; von Mackensen, Sylvia; Holzhauer, Susanne; Funk, Stephanie; Klamroth, Robert; Kurnik, Karin; Krümpel, Anne; Halimeh, Susan; Reinke, Sarah; Frühwald, Michael; Nowak-Göttl, Ulrike

2016-01-01

Objectives. To investigate self-reported health-related quality of life (HrQoL) in children and adolescents with chronic medical conditions compared with siblings/peers. Methods. Group 1 (6 treatment centers) consisted of 74 children/adolescents aged 8–16 years with hereditary bleeding disorders (HBD), 12 siblings, and 34 peers. Group 2 (one treatment center) consisted of 70 children/adolescents with stroke/transient ischemic attack, 14 siblings, and 72 peers. HrQoL was assessed with the “revised KINDer Lebensqualitätsfragebogen” (KINDL-R) questionnaire. Multivariate analyses within groups were done by one-way ANOVA and post hoc pairwise single comparisons by Student's t-tests. Adjusted pairwise comparisons were done by hierarchical linear regressions with individuals nested within treatment centers (group 1) and by linear regressions (group 2), respectively. Results. No differences were found in multivariate analyses of self-reported HrQoL in group 1, while in group 2 differences occurred in overall wellbeing and all subdimensions. These differences were due to differences between patients and peers. After adjusting for age, gender, number of siblings, and treatment center these differences persisted regarding self-worth (p = .0040) and friend-related wellbeing (p < .001). Conclusions. In children with HBD, HrQoL was comparable to siblings and peers. In children with stroke/TIA HrQoL was comparable to siblings while peers, independently of relevant confounder, showed better self-worth and friend-related wellbeing. PMID:27294108

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs

PubMed Central

Mussin, Nadiar; Sumo, Marco; Choi, YoungRok; Choi, Jin Yong; Ahn, Sung-Woo; Yoon, Kyung Chul; Kim, Hyo-Sin; Hong, Suk Kyun; Yi, Nam-Joon; Suh, Kyung-Suk

2017-01-01

Purpose Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs. Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Methods Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Results Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R2 = 0.719), than for the Rapidia group (R2 = 0.688). Conclusion Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation. PMID:28382294

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

Using a Linear Regression Method to Detect Outliers in IRT Common Item Equating

ERIC Educational Resources Information Center

He, Yong; Cui, Zhongmin; Fang, Yu; Chen, Hanwei

2013-01-01

Common test items play an important role in equating alternate test forms under the common item nonequivalent groups design. When the item response theory (IRT) method is applied in equating, inconsistent item parameter estimates among common items can lead to large bias in equated scores. It is prudent to evaluate inconsistency in parameter…

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Prediction of intramuscular fat content and major fatty acid groups of lamb M. longissimus lumborum using Raman spectroscopy.

PubMed

Fowler, Stephanie M; Ponnampalam, Eric N; Schmidt, Heinar; Wynn, Peter; Hopkins, David L

2015-12-01

A hand held Raman spectroscopic device was used to predict intramuscular fat (IMF) levels and the major fatty acid (FA) groups of fresh intact ovine M. longissimus lumborum (LL). IMF levels were determined using the Soxhlet method, while FA analysis was conducted using a rapid (KOH in water, methanol and sulphuric acid in water) extraction procedure. IMF levels and FA values were regressed against Raman spectra using partial least squares regression and against each other using linear regression. The results indicate that there is potential to predict PUFA (R(2)=0.93) and MUFA (R(2)=0.54) as well as SFA values that had been adjusted for IMF content (R(2)=0.54). However, this potential was significantly reduced when correlations between predicted and observed values were determined by cross validation (R(2)cv=0.21-0.00). Overall, the prediction of major FA groups using Raman spectra was more precise (relative reductions in error of 0.3-40.8%) compared to the null models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Linear growth trajectories in Zimbabwean infants12

PubMed Central

Gough, Ethan K; Moodie, Erica EM; Prendergast, Andrew J; Ntozini, Robert; Moulton, Lawrence H; Humphrey, Jean H; Manges, Amee R

2016-01-01

Background: Undernutrition in early life underlies 45% of child deaths globally. Stunting malnutrition (suboptimal linear growth) also has long-term negative effects on childhood development. Linear growth deficits accrue in the first 1000 d of life. Understanding the patterns and timing of linear growth faltering or recovery during this period is critical to inform interventions to improve infant nutritional status. Objective: We aimed to identify the pattern and determinants of linear growth trajectories from birth through 24 mo of age in a cohort of Zimbabwean infants. Design: We performed a secondary analysis of longitudinal data from a subset of 3338 HIV-unexposed infants in the Zimbabwe Vitamin A for Mothers and Babies trial. We used k-means clustering for longitudinal data to identify linear growth trajectories and multinomial logistic regression to identify covariates that were associated with each trajectory group. Results: For the entire population, the mean length-for-age z score declined from −0.6 to −1.4 between birth and 24 mo of age. Within the population, 4 growth patterns were identified that were each characterized by worsening linear growth restriction but varied in the timing and severity of growth declines. In our multivariable model, 1-U increments in maternal height and education and infant birth weight and length were associated with greater relative odds of membership in the least–growth restricted groups (A and B) and reduced odds of membership in the more–growth restricted groups (C and D). Male infant sex was associated with reduced odds of membership in groups A and B but with increased odds of membership in groups C and D. Conclusion: In this population, all children were experiencing growth restriction but differences in magnitude were influenced by maternal height and education and infant sex, birth weight, and birth length, which suggest that key determinants of linear growth may already be established by the time of birth. This trial was registered at clinicaltrials.gov as NCT00198718. PMID:27806980

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

[Factors influencing glucose metabolism in young obese subjects with obstructive sleep apnea hypopnea syndrome].

PubMed

Gu, C J; Li, Q Y; Li, M; Zhou, J; Du, J; Yi, H H; Feng, J; Zhou, L N; Wang, Q

2016-05-17

To explore the factors influencing glucose metabolism in young obese subjects with obstructive sleep apnea hypopnea syndrome (OSAHS). A total of 106 young obese subjects[18-44 years old, body mass index (BMI) ≥30 kg/m(2)]were enrolled and divided into two groups based on full-night polysomnography (PSG), OSAHS group[apnea hypopnea index (AHI) ≥5 events/h]and non-OSAHS group (AHI<5 events/h). Oral glucose tolerance-insulin releasing test (OGTT-IRT) was performed and serum glycosylated hemoglobin A1 (HbA1c) levels were measured after an overnight fast. Homeostasis model assessment-IR (HOMA-IR), Matsuda insulin sensitivity index (MI), homeostasis model assessment-β (HOMA-β), the early phase insulinogenic index (ΔI(30)/ΔG(30)), total area under the curve of insulin in 180 minutes (AUC-I180) and oral disposition index (DIo) were calculated to evaluate insulin resistance and pancreatic β cell function. Stepwise multiple linear regressions were conducted to determine the independent linear correlation of glucose measurements with PSG parameters. Prevalence of diabetes was higher in OSAHS than in non-OSAHS group (22.0% vs 4.3%, P=0.009). OGTT 0, 30, 60 min glucose and HbA1c levels were higher in OSAHS group than those in non-OASHS group (all P<0.05). DIo were lower in OSAHS group than those in non-OASHS group (P=0.024), HOMA-IR, MI, HOMA-β, ΔI(30)/ΔG(30), and AUC-I(180) were similar between two groups (all P>0.05). In stepwise multiple linear regressions, OGTT 0, 30 and 60 min glucose were positively correlated with oxygen desaturation index (ODI) (β=0.243, 0.273 and 0.371 respectively, all P<0.05). HOMA-β was negatively correlated with AHI (β=-0.243, P=0.011). DIo was negatively correlated with ODI (β=-0.234, P=0.031). OSAHS worsens glucose metabolism and compensatory pancreatic β-cell function in young obese subjects, which could probably be attributed to sleep apnea related oxygen desaturation during sleep.

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

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

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

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

PubMed

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

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The effects of cognitive – linguistic variables and language experience on behavioural and kinematic performances in nonword learning

PubMed Central

Sasisekaran, Jayanthi; Weisberg, Sanford

2013-01-01

The aim of the present study was to investigate the effect of cognitive – linguistic variables and language experience on behavioral and kinematic measures of nonword learning in young adults. Group 1 consisted of thirteen participants who spoke American English as the first and only language. Group 2 consisted of seven participants with varying levels of proficiency in a second language. Logistic regression of the percent of correct productions revealed short-term memory to be a significant contributor. The bilingual group showed better performance compared to the monolinguals. Linear regression of the kinematic data revealed that the short – term memory variable contributed significantly to movement coordination. Differences were not observed between the bilingual and the monolingual speakers in kinematic performance. Nonword properties including syllable length and complexity influenced both behavioral and kinematic performance. The findings supported the observation that nonword repetition is multiply determined in adults. PMID:22476630

Red-cockaded Woodpecker Picoides borealis Microhabitat Characteristics and Reproductive Success in a Loblolly-Shortleaf Pine Forest

USGS Publications Warehouse

Wood, Douglas R.; Burger, L. Wesley; Vilella, Francisco

2014-01-01

We investigated the relationship between red-cockaded woodpecker (Picoides borealis) reproductive success and microhabitat characteristics in a southeastern loblolly (Pinus taeda) and shortleaf (P. echinata) pine forest. From 1997 to 1999, we recorded reproductive success parameters of 41 red-cockaded woodpecker groups at the Bienville National Forest, Mississippi. Microhabitat characteristics were measured for each group during the nesting season. Logistic regression identified understory vegetation height and small nesting season home range size as predictors of red-cockaded woodpecker nest attempts. Linear regression models identified several variables as predictors of red-cockaded woodpecker reproductive success including group density, reduced hardwood component, small nesting season home range size, and shorter foraging distances. Red-cockaded woodpecker reproductive success was correlated with habitat and behavioral characteristics that emphasize high quality habitat. By providing high quality foraging habitat during the nesting season, red-cockaded woodpeckers can successfully reproduce within small home ranges.

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

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

Food Group Intakes as Determinants of Iodine Status among US Adult Population.

PubMed

Lee, Kyung Won; Shin, Dayeon; Cho, Mi Sook; Song, Won O

2016-05-26

Adequate intake of iodine is essential for proper thyroid function. Although dietary reference intakes for iodine have been established, iodine intake cannot be estimated due to the lack of data on iodine contents in foods. We aimed to determine if food group intakes can predict iodine status assessed by urinary iodine concentration (UIC) from spot urine samples of 5967 US adults in the National Health and Nutrition Examination Survey (NHANES) 2007-2012. From an in-person 24-h dietary recall, all foods consumed were aggregated into 12 main food groups using the individual food code of the US Department of Agriculture (USDA); dairy products, meat/poultry, fish/seaweed, eggs, legumes/nuts/seeds, breads, other grain products, fruits, vegetables, fats/oils, sugars/sweets, and beverages. Chi-square test, Spearman correlation, and multiple linear regression analyses were conducted to investigate the predictability of food group intakes in iodine status assessed by UIC. From the multiple linear regressions, the consumption of dairy products, eggs, and breads, and iodine-containing supplement use were positively associated with UIC, whereas beverage consumption was negatively associated with UIC. Among various food group intakes, dairy product intake was the most important determinant of iodine status in both US men and women. Subpopulation groups with a high risk of iodine deficiency may need nutritional education regarding the consumption of dairy products, eggs, and breads to maintain an adequate iodine status. Efforts toward a better understanding of iodine content in each food and a continued monitoring of iodine status within US adults are both warranted.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

The Sooner the Better? How Symptom Interval Correlates With Outcome in Children and Adolescents With Solid Tumors: Regression Tree Analysis of the Findings of a Prospective Study.

PubMed

Ferrari, Andrea; Lo Vullo, Salvatore; Giardiello, Daniele; Veneroni, Laura; Magni, Chiara; Clerici, Carlo Alfredo; Chiaravalli, Stefano; Casanova, Michela; Luksch, Roberto; Terenziani, Monica; Spreafico, Filippo; Meazza, Cristina; Catania, Serena; Schiavello, Elisabetta; Biassoni, Veronica; Podda, Marta; Bergamaschi, Luca; Puma, Nadia; Massimino, Maura; Mariani, Luigi

2016-03-01

The potential impact of diagnostic delays on patients' outcomes is a debated issue in pediatric oncology and discordant results have been published so far. We attempted to tackle this issue by analyzing a prospective series of 351 consecutive children and adolescents with solid malignancies using innovative statistical tools. To address the nonlinear complexity of the association between symptom interval and overall survival (OS), a regression tree algorithm was constructed with sequential binary splitting rules and used to identify homogeneous patient groups vis-à-vis functional relationship between diagnostic delay and OS. Three different groups were identified: group A, with localized disease and good prognosis (5-year OS 85.4%); group B, with locally or regionally advanced, or metastatic disease and intermediate prognosis (5-year OS 72.9%), including neuroblastoma, Wilms tumor, non-rhabdomyosarcoma soft tissue sarcoma, and germ cell tumor; and group C, with locally or regionally advanced, or metastatic disease and poor prognosis (5-year OS 45%), including brain tumors, rhabdomyosarcoma, and bone sarcoma. The functional relationship between symptom interval and mortality risk differed between the three subgroups, there being no association in group A (hazard ratio [HR]: 0.96), a positive linear association in group B (HR: 1.48), and a negative linear association in group C (HR: 0.61). Our analysis suggests that at least a subset of patients can benefit from an earlier diagnosis in terms of survival. For others, intrinsic aggressiveness may mask the potential effect of diagnostic delays. Based on these findings, early diagnosis should remain a goal for pediatric cancer patients. © 2015 Wiley Periodicals, Inc.

[Association between urinary microalbumin-to-creatinine ratio and brachial-ankle pulse wave velocity in hypertensive patients].

PubMed

Zhu, Hang; Xue, Hao; Wang, Guangyi; Fu, Zhenhong; Liu, Jie; Shi, Yajun

2015-04-01

To explore the association between urinary microalbumin-to-creatinine ratio (ACR) and brachial-ankle pulse wave velocity (baPWV) in hypertensive patients. A total of 877 primary hypertension patients were enrolled in this trial from September 2009 to December 2012, and were randomly recruited and patients were divided into normal ACR group (ACR < 30 mg/g, n = 723), micro-albuminuria group (30 mg/g ≤ ACR < 300 mg/g, n = 136) and macro-albuminuria group (ACR ≥ 300 mg/g, n = 18). baPWV was measure by automatic pulse wave velocity measuring system. The baPWV values in patients of micro-albuminuria group and macro-albuminuria group were significantly higher than in the normal ACR group (all P < 0.05). The baPWV value of macro-albuminuria group was significantly higher than in the micro-albuminuria group (P < 0.05). Linear correlation analysis revealed that ACR was positively correlated with baPWV (r = 0.413, P < 0.01). Multiple linear regression analysis showed that ACR independently correlated with baPWV in patients with primary hypertension (β = 0.29, R(2) = 0.112, P < 0.01) after adjusting for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, blood glucose, total cholesterol, low density lipoprotein, high density lipoprotein and triglyceride. Using ACR < 30 mg/g and ACR ≥ 30 mg/g as dichotomous variable, binary logistic regression analysis showed that ACR ≥ 30 mg/g was also a risk factor of the ascending baPWV in primary hypertension patients (OR: 1.73, 95% CI: 1.62-2.98) after adjusting the traditional cardiovascular risk factors. ACR is positively correlated to baPWV in primary hypertension patients, and the ascending baPWV is a risk factor of early renal dysfunction in primary hypertension patients.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Differences in Risk Factors for Rotator Cuff Tears between Elderly Patients and Young Patients.

PubMed

Watanabe, Akihisa; Ono, Qana; Nishigami, Tomohiko; Hirooka, Takahiko; Machida, Hirohisa

2018-02-01

It has been unclear whether the risk factors for rotator cuff tears are the same at all ages or differ between young and older populations. In this study, we examined the risk factors for rotator cuff tears using classification and regression tree analysis as methods of nonlinear regression analysis. There were 65 patients in the rotator cuff tears group and 45 patients in the intact rotator cuff group. Classification and regression tree analysis was performed to predict rotator cuff tears. The target factor was rotator cuff tears; explanatory variables were age, sex, trauma, and critical shoulder angle≥35°. In the results of classification and regression tree analysis, the tree was divided at age 64. For patients aged≥64, the tree was divided at trauma. For patients aged<64, the tree was divided at critical shoulder angle≥35°. The odds ratio for critical shoulder angle≥35° was significant for all ages (5.89), and for patients aged<64 (10.3) while trauma was only a significant factor for patients aged≥64 (5.13). Age, trauma, and critical shoulder angle≥35° were related to rotator cuff tears in this study. However, these risk factors showed different trends according to age group, not a linear relationship.

Analysis of the Effects of the Commander’s Battle Positioning on Unit Combat Performance

DTIC Science & Technology

1991-03-01

Analysis ......... .. 58 Logistic Regression Analysis ......... .. 61 Canonical Correlation Analysis ........ .. 62 Descriminant Analysis...entails classifying objects into two or more distinct groups, or responses. Dillon defines descriminant analysis as "deriving linear combinations of the...object given it’s predictor variables. The second objective is, through analysis of the parameters of the descriminant functions, determine those

Solutions for Determining the Significance Region Using the Johnson-Neyman Type Procedure in Generalized Linear (Mixed) Models

ERIC Educational Resources Information Center

Lazar, Ann A.; Zerbe, Gary O.

2011-01-01

Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the "significance region," or the values of the covariate where the curves significantly differ. In analysis of covariance (ANCOVA),…

Locomotive syndrome is associated not only with physical capacity but also degree of depression.

PubMed

Ikemoto, Tatsunori; Inoue, Masayuki; Nakata, Masatoshi; Miyagawa, Hirofumi; Shimo, Kazuhiro; Wakabayashi, Toshiko; Arai, Young-Chang P; Ushida, Takahiro

2016-05-01

Reports of locomotive syndrome (LS) have recently been increasing. Although physical performance measures for LS have been well investigated to date, studies including psychiatric assessment are still scarce. Hence, the aim of this study was to investigate both physical and mental parameters in relation to presence and severity of LS using a 25-question geriatric locomotive function scale (GLFS-25) questionnaire. 150 elderly people aged over 60 years who were members of our physical-fitness center and displayed well-being were enrolled in this study. Firstly, using the previously determined GLFS-25 cutoff value (=16 points), subjects were divided into two groups accordingly: an LS and non-LS group in order to compare each parameter (age, grip strength, timed-up-and-go test (TUG), one-leg standing with eye open, back muscle and leg muscle strength, degree of depression and cognitive impairment) between the groups using the Mann-Whitney U-test followed by multiple logistic regression analysis. Secondly, a multiple linear regression was conducted to determine which variables showed the strongest correlation with severity of LS. We confirmed 110 people for non-LS (73%) and 40 people for LS using the GLFS-25 cutoff value. Comparative analysis between LS and non-LS revealed significant differences in parameters in age, grip strength, TUG, one-leg standing, back muscle strength and degree of depression (p < 0.006, after Bonferroni correction). Multiple logistic regression revealed that functional decline in grip strength, TUG and one-leg standing and degree of depression were significantly associated with LS. On the other hand, we observed that the significant contributors towards the GLFS-25 score were TUG and degree of depression in multiple linear regression analysis. The results indicate that LS is associated with not only the capacity of physical performance but also the degree of depression although most participants fell under the criteria of LS. Copyright © 2016 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

The relationship between compressive strength and flexural strength of pavement geopolymer grouting material

NASA Astrophysics Data System (ADS)

Zhang, L.; Han, X. X.; Ge, J.; Wang, C. H.

2018-01-01

To determine the relationship between compressive strength and flexural strength of pavement geopolymer grouting material, 20 groups of geopolymer grouting materials were prepared, the compressive strength and flexural strength were determined by mechanical properties test. On the basis of excluding the abnormal values through boxplot, the results show that, the compressive strength test results were normal, but there were two mild outliers in 7days flexural strength test. The compressive strength and flexural strength were linearly fitted by SPSS, six regression models were obtained by linear fitting of compressive strength and flexural strength. The linear relationship between compressive strength and flexural strength can be better expressed by the cubic curve model, and the correlation coefficient was 0.842.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

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.

Psychiatric Characteristics of the Cardiac Outpatients with Chest Pain

PubMed Central

Lee, Jea-Geun; Kim, Song-Yi; Kim, Ki-Seok; Joo, Seung-Jae

2016-01-01

Background and Objectives A cardiologist's evaluation of psychiatric symptoms in patients with chest pain is rare. This study aimed to determine the psychiatric characteristics of patients with and without coronary artery disease (CAD) and explore their relationship with the intensity of chest pain. Subjects and Methods Out of 139 consecutive patients referred to the cardiology outpatient department, 31 with atypical chest pain (heartburn, acid regurgitation, dyspnea, and palpitation) were excluded and 108 were enrolled for the present study. The enrolled patients underwent complete numerical rating scale of chest pain and the symptom checklist for minor psychiatric disorders at the time of first outpatient visit. The non-CAD group consisted of patients with a normal stress test, coronary computed tomography angiogram, or coronary angiogram, and the CAD group included those with an abnormal coronary angiogram. Results Nineteen patients (17.6%) were diagnosed with CAD. No differences in the psychiatric characteristics were observed between the groups. "Feeling tense", "self-reproach", and "trouble falling asleep" were more frequently observed in the non-CAD (p=0.007; p=0.046; p=0.044) group. In a multiple linear regression analysis with a stepwise selection, somatization without chest pain in the non-CAD group and hypochondriasis in the CAD group were linearly associated with the intensity of chest pain (β=0.108, R2=0.092, p=0.004; β= -0.525, R2=0.290, p=0.010). Conclusion No differences in psychiatric characteristics were observed between the groups. The intensity of chest pain was linearly associated with somatization without chest pain in the non-CAD group and inversely linearly associated with hypochondriasis in the CAD group. PMID:27014347

An Analysis of COLA (Cost of Living Adjustment) Allocation within the United States Coast Guard.

DTIC Science & Technology

1983-09-01

books Applied Linear Regression [Ref. 39], and Statistical Methods in Research and Production [Ref. 40], or any other book on regression. In the event...Indexes, Master’s Thesis, Air Force Institute of Technology, Wright-Patterson AFB, 1976. 39. Weisberg, Stanford, Applied Linear Regression , Wiley, 1980. 40

Testing hypotheses for differences between linear regression lines

Treesearch

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Teaching the Concept of Breakdown Point in Simple Linear Regression.

ERIC Educational Resources Information Center

Chan, Wai-Sum

2001-01-01

Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

Estimating monotonic rates from biological data using local linear regression.

PubMed

Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

2017-03-01

Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

Quality of life after intra-arterial treatment for acute ischemic stroke in the MR CLEAN trial-Update.

PubMed

Schreuders, Jennifer; van den Berg, Lucie A; Fransen, Puck Ss; Berkhemer, Olvert A; Beumer, Debbie; Lingsma, Hester F; van Oostenbrugge, Robert J; van Zwam, Wim H; Majoie, Charles Blm; van der Lugt, Aad; de Kort, Paul Lm; Roos, Yvo Bwem; Dippel, Diederik Wj

2017-10-01

Background Health-related quality of life measured with the EuroQol Group 5-Dimension Self-Report Questionnaire was one of the secondary outcomes in the Multicenter Randomized Clinical trial of Endovascular treatment for Acute ischemic stroke in the Netherlands (MR CLEAN). We reported no statistically significant difference in EuroQol Group 5-Dimension Self-Report Questionnaire score between the intervention and control groups, but deaths were not included. Aims Reanalyze the effect of intra-arterial treatment for large vessel occlusion in acute ischemic stroke patients on health-related quality of life in more detail. We now include patients who died during follow-up. Methods The EuroQol Group 5-Dimension Self-Report Questionnaire questionnaires were obtained 90 days after treatment. We used the Dutch tariff to derive a utility index from the EuroQol Group 5-Dimension Self-Report Questionnaire score. Treatment effect was estimated with the Mann-Whitney U test and linear regression. The effect of treatment on the distribution of EuroQol Group 5-Dimension Self-Report Questionnaire dimension scores was assessed with ordinal logistic regression. Results We obtained EuroQol Group 5-Dimension Self-Report Questionnaire scores from 457 (91.7%) of the 500 patients, including 108 who died before follow-up. Median EuroQol Group 5-Dimension Self-Report Questionnaire score in the intervention group was 0.57, and 0.39 in the control group (p = 0.03). Treatment effect estimated with linear regression was 0.07 (95%CI: -0.001 to 0.143). Treatment specifically affected EuroQol Group 5-Dimension Self-Report Questionnaire dimensions "mobility" (OR: 0.43, 95%CI: 0.29-0.66), "self-care" (OR: 0.60, 95%CI: 0.41-0.89), and "usual activities" (OR: 0.53, 95%CI: 0.36-0.79). Conclusion Treatment had a limited effect on quality of life, as measured with the EuroQol Group 5-Dimension Self-Report Questionnaire. Nevertheless, patients with acute ischemic stroke caused by an intracranial occlusion in the anterior circulation, who had intra-arterial treatment, experience better health-related quality of life than patients without intra-arterial treatment. Trial Registration URL: http://www.isrctn.com/ISRCTN10888758 Unique identifier: ISRCTN10888758.

Estimation of stature from radiologic anthropometry of the lumbar vertebral dimensions in Chinese.

PubMed

Zhang, Kui; Chang, Yun-feng; Fan, Fei; Deng, Zhen-hua

2015-11-01

The recent study was to assess the relationship between the radiologic anthropometry of the lumbar vertebral dimensions and stature in Chinese and to develop regression formulae to estimate stature from these dimensions. A total of 412 normal, healthy volunteers, comprising 206 males and 206 females, were recruited. The linear regression analysis were performed to assess the correlation between the stature and lengths of various segments of the lumbar vertebral column. Among the regression equations created for single variable, the predictive value was greatest for the reconstruction of stature from the lumbar segment in both sexes and subgroup analysis. When individual vertebral body was used, the heights of posterior vertebral body of L3 gave the most accurate results for male group, the heights of central vertebral body of L1 provided the most accurate results for female group and female group with age above 45 years, the heights of central vertebral body of L3 gave the most accurate results for the groups with age from 20-45 years for both sexes and the male group with age above 45 years. The heights of anterior vertebral body of L5 gave the less accurate results except for the heights of anterior vertebral body of L4 provided the less accurate result for the male group with age above 45 years. As expected, multiple regression equations were more successful than equations derived from a single variable. The research observations suggest lumbar vertebral dimensions to be useful in stature estimation among Chinese population. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

Functional mixture regression.

PubMed

Yao, Fang; Fu, Yuejiao; Lee, Thomas C M

2011-04-01

In functional linear models (FLMs), the relationship between the scalar response and the functional predictor process is often assumed to be identical for all subjects. Motivated by both practical and methodological considerations, we relax this assumption and propose a new class of functional regression models that allow the regression structure to vary for different groups of subjects. By projecting the predictor process onto its eigenspace, the new functional regression model is simplified to a framework that is similar to classical mixture regression models. This leads to the proposed approach named as functional mixture regression (FMR). The estimation of FMR can be readily carried out using existing software implemented for functional principal component analysis and mixture regression. The practical necessity and performance of FMR are illustrated through applications to a longevity analysis of female medflies and a human growth study. Theoretical investigations concerning the consistent estimation and prediction properties of FMR along with simulation experiments illustrating its empirical properties are presented in the supplementary material available at Biostatistics online. Corresponding results demonstrate that the proposed approach could potentially achieve substantial gains over traditional FLMs.

Prostate-specific antigen lowering effect of metabolic syndrome is influenced by prostate volume.

PubMed

Choi, Woo Suk; Heo, Nam Ju; Paick, Jae-Seung; Son, Hwancheol

2016-04-01

To investigate the influence of metabolic syndrome on prostate-specific antigen levels by considering prostate volume and plasma volume. We retrospectively analyzed 4111 men who underwent routine check-ups including prostate-specific antigen and transrectal ultrasonography. The definition of metabolic syndrome was based on the modified Adult Treatment Panel III criteria. Prostate-specific antigen mass density (prostate-specific antigen × plasma volume / prostate volume) was calculated for adjusting plasma volume and prostate volume. We compared prostate-specific antigen and prostate-specific antigen mass density levels of participants with metabolic syndrome (metabolic syndrome group, n = 1242) and without metabolic syndrome (non-prostate-specific antigen metabolic syndrome group, n = 2869). To evaluate the impact of metabolic syndrome on prostate-specific antigen, linear regression analysis for the natural logarithm of prostate-specific antigen was used. Patients in the metabolic syndrome group had significantly older age (P < 0.001), larger prostate volume (P < 0.001), higher plasma volume (P < 0.001) and lower mean serum prostate-specific antigen (non-metabolic syndrome group vs metabolic syndrome group; 1.22 ± 0.91 vs 1.15 ± 0.76 ng/mL, P = 0.006). Prostate-specific antigen mass density in the metabolic syndrome group was still significantly lower than that in the metabolic syndrome group (0.124 ± 0.084 vs 0.115 ± 0.071 μg/mL, P = 0.001). After adjusting for age, prostate volume and plasma volume using linear regression model, the presence of metabolic syndrome was a significant independent factor for lower prostate-specific antigen (prostate-specific antigen decrease by 4.1%, P = 0.046). Prostate-specific antigen levels in patients with metabolic syndrome seem to be lower, and this finding might be affected by the prostate volume. © 2016 The Japanese Urological Association.

Effect of Malmquist bias on correlation studies with IRAS data base

NASA Technical Reports Server (NTRS)

Verter, Frances

1993-01-01

The relationships between galaxy properties in the sample of Trinchieri et al. (1989) are reexamined with corrections for Malmquist bias. The linear correlations are tested and linear regressions are fit for log-log plots of L(FIR), L(H-alpha), and L(B) as well as ratios of these quantities. The linear correlations for Malmquist bias are corrected using the method of Verter (1988), in which each galaxy observation is weighted by the inverse of its sampling volume. The linear regressions are corrected for Malmquist bias by a new method invented here in which each galaxy observation is weighted by its sampling volume. The results of correlation and regressions among the sample are significantly changed in the anticipated sense that the corrected correlation confidences are lower and the corrected slopes of the linear regressions are lower. The elimination of Malmquist bias eliminates the nonlinear rise in luminosity that has caused some authors to hypothesize additional components of FIR emission.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Trends in asthma mortality in the 0- to 4-year and 5- to 34-year age groups in Brazil

PubMed Central

Graudenz, Gustavo Silveira; Carneiro, Dominique Piacenti; Vieira, Rodolfo de Paula

2017-01-01

ABSTRACT Objective: To provide an update on trends in asthma mortality in Brazil for two age groups: 0-4 years and 5-34 years. Methods: Data on mortality from asthma, as defined in the International Classification of Diseases, were obtained for the 1980-2014 period from the Mortality Database maintained by the Information Technology Department of the Brazilian Unified Health Care System. To analyze time trends in standardized asthma mortality rates, we conducted an ecological time-series study, using regression models for the 0- to 4-year and 5- to 34-year age groups. Results: There was a linear trend toward a decrease in asthma mortality in both age groups, whereas there was a third-order polynomial fit in the general population. Conclusions: Although asthma mortality showed a consistent, linear decrease in individuals ≤ 34 years of age, the rate of decline was greater in the 0- to 4-year age group. The 5- to 34-year group also showed a linear decline in mortality, and the rate of that decline increased after the year 2004, when treatment with inhaled corticosteroids became more widely available. The linear decrease in asthma mortality found in both age groups contrasts with the nonlinear trend observed in the general population of Brazil. The introduction of inhaled corticosteroid use through public policies to control asthma coincided with a significant decrease in asthma mortality rates in both subsets of individuals over 5 years of age. The causes of this decline in asthma-related mortality in younger age groups continue to constitute a matter of debate. PMID:28380185

Analyzing Multilevel Data: Comparing Findings from Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2013-01-01

This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student…

Culture media influenced laboratory outcomes but not neonatal birth weight in assisted reproductive technology.

PubMed

Yin, Tai-lang; Zhang, Yi; Li, Sai-jiao; Zhao, Meng; Ding, Jin-li; Xu, Wang-ming; Yang, Jing

2015-12-01

Whether the type of culture media utilized in assisted reproductive technology has impacts on laboratory outcomes and birth weight of newborns in in-vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) was investigated. A total of 673 patients undergoing IVF/ICSI and giving birth to live singletons after fresh embryo transfer on day 3 from Jan. 1, 2010 to Dec. 31, 2012 were included. Three types of culture media were used during this period: Quinn's Advantage (QA), Single Step Medium (SSM), and Continuous Single Culture medium (CSC). Fertilization rate (FR), normal fertilization rate (NFR), cleavage rate (CR), normal cleavage rate (NCR), good-quality embryo rate (GQER) and neonatal birth weight were compared using one-way ANOVA and χ (2) tests. Multiple linear regression analysis was performed to determine the impact of culture media on laboratory outcomes and birth weight. In IVF cycles, GQER was significantly decreased in SSM medium group as compared with QA or CSC media groups (63.6% vs. 69.0% in QA; vs. 71.3% in CSC, P=0.011). In ICSI cycles, FR, NFR and CR were significantly lower in CSC medium group than in other two media groups. No significant difference was observed in neonatal birthweight among the three groups (P=0.759). Multiple linear regression analyses confirmed that the type of culture medium was correlated with FR, NFR, CR and GQER, but not with neonatal birth weight. The type of culture media had potential influences on laboratory outcomes but did not exhibit an impact on the birth weight of singletons in ART.

Catatonic Stupor in Schizophrenic Disorders and Subsequent Medical Complications and Mortality.

PubMed

Funayama, Michitaka; Takata, Taketo; Koreki, Akihiro; Ogino, Satoyuki; Mimura, Masaru

2018-05-01

Although catatonia can occur secondary to a general medical condition, catatonia itself has been known to lead to various medical compolications. Although case reports on the association of catatonia with subsequent medical complications have been documented, no comprehensive large-scale study has been performed. To investigate specific medical complications after catatonia, we conducted a retrospective cohort study of specific medical complications of schizophrenia patients with catatonia. The 1719 schizophrenia inpatients in our study were categorized into two groups: the catatonia group, i.e., those who exhibited catatonic stupor while they were hospitalized, and the noncatatonia group, i.e., those who never exhibited catatonic stupor. Differences between the two groups in the occurrence of subsequent medical complications were examined using linear and logistic regression analyses, and models were adjusted for potentially confounding factors. The catatonia group had an increased risk for mortality (odds ratio = 4.8, 95% confidence interval = 2.0-10.6, p < .01) and certain specific medical complications, i.e., pneumonia, urinary tract infection, sepsis, disseminated intravascular coagulation, rhabdomyolysis, dehydration, deep venous thrombosis, pulmonary embolism, urinary retention, decubitus, arrhythmia, renal failure, neuroleptic malignant syndrome, hypernatremia, and liver dysfunction (all p values < .01, except for deep venous thrombosis, p = .04 in the multiple linear regression analysis). Catatonic stupor in schizophrenia substantially raises the risk for specific medical complications and mortality. Hyperactivity of the sympathetic nervous system, dehydration, and immobility, which are frequently involved in catatonia, might contribute to these specific medical complications. In catatonia, meticulous care for both mental and medical conditions should be taken to reduce the risk of adverse medical consequences.

Reference values for airway resistance in newborns, infants and preschoolers from a Latin American population.

PubMed

Gochicoa, Laura G; Thomé-Ortiz, Laura P; Furuya, María E Y; Canto, Raquel; Ruiz-García, Martha E; Zúñiga-Vázquez, Guillermo; Martínez-Ramírez, Filiberto; Vargas, Mario H

2012-05-01

Several studies have determined reference values for airway resistance measured by the interrupter technique (Rint) in paediatric populations, but only one has been done on Latin American children, and no studies have been performed on Mexican children. Moreover, these previous studies mostly included children aged 3 years and older; therefore, information regarding Rint reference values for newborns and infants is scarce. Rint measurements were performed on preschool children attending eight kindergartens (Group 1) and also on sedated newborns, infants and preschool children admitted to a tertiary-level paediatric hospital due to non-cardiopulmonary disorders (Group 2). In both groups, Rint values were inversely associated with age, weight and height, but the strongest association was with height. The linear regression equation for Group 1 (n = 209, height 86-129 cm) was Rint = 2.153 - 0.012 × height (cm) (standard deviation of residuals 0.181 kPa/L/s). The linear regression equation for Group 2 (n = 55, height 52-113 cm) was Rint = 4.575 - 0.035 × height (cm) (standard deviation of residuals 0.567 kPa/L/s). Girls tended to have slightly higher Rint values than boys, a difference that diminished with increasing height. In this study, Rint reference values applicable to Mexican children were determined, and these values are probably also applicable to other paediatric populations with similar Spanish-Amerindian ancestries. There was an inverse relationship between Rint and height, with relatively large between-subject variability. © 2012 The Authors. Respirology © 2012 Asian Pacific Society of Respirology.

Association between Social Activities and Cognitive Function among the Elderly in China: A Cross-Sectional Study.

PubMed

Fu, Chang; Li, Zhen; Mao, Zongfu

2018-01-30

Participation in social activities is one of important factors for older adults' health. The present study aims to examine the cross-sectional association between social activities and cognitive function among Chinese elderly. A total of 8966 individuals aged 60 and older from the 2015 China Health and Retirement Longitudinal Study were obtained for this study. Telephone interviews of cognitive status, episodic memory, and visuospatial abilities were assessed by questionnaire. We used the sum of all three of the above measures to represent the respondent's cognitive status as a whole. Types and frequencies of participation in social groups were used to measure social activities. Multiple linear regression analysis was used to explore the relationship between social activities and cognitive function. After adjustment for demographics, smoking, drinking, depression, hypertension, diabetes, basic activities of daily living, instrumental activities of daily living, and self-rated health, multiple linear regression analysis revealed that interaction with friends, participating in hobby groups, and sports groups were associated with better cognitive function among both men and women ( p < 0.05); doing volunteer work was associated with better cognitive function among women but not among men ( p < 0.05). These findings suggest that there is a cross-sectional association between participation in social activities and cognitive function among Chinese elderly. Longitudinal studies are needed to examine the effects of social activities on cognitive function.

Association between Social Activities and Cognitive Function among the Elderly in China: A Cross-Sectional Study

PubMed Central

Fu, Chang; Li, Zhen; Mao, Zongfu

2018-01-01

Participation in social activities is one of important factors for older adults’ health. The present study aims to examine the cross-sectional association between social activities and cognitive function among Chinese elderly. A total of 8966 individuals aged 60 and older from the 2015 China Health and Retirement Longitudinal Study were obtained for this study. Telephone interviews of cognitive status, episodic memory, and visuospatial abilities were assessed by questionnaire. We used the sum of all three of the above measures to represent the respondent’s cognitive status as a whole. Types and frequencies of participation in social groups were used to measure social activities. Multiple linear regression analysis was used to explore the relationship between social activities and cognitive function. After adjustment for demographics, smoking, drinking, depression, hypertension, diabetes, basic activities of daily living, instrumental activities of daily living, and self-rated health, multiple linear regression analysis revealed that interaction with friends, participating in hobby groups, and sports groups were associated with better cognitive function among both men and women (p < 0.05); doing volunteer work was associated with better cognitive function among women but not among men (p < 0.05). These findings suggest that there is a cross-sectional association between participation in social activities and cognitive function among Chinese elderly. Longitudinal studies are needed to examine the effects of social activities on cognitive function. PMID:29385773

Relationship between Body Mass Index (BMI) and body fat percentage, estimated by bioelectrical impedance, in a group of Sri Lankan adults: a cross sectional study.

PubMed

Ranasinghe, Chathuranga; Gamage, Prasanna; Katulanda, Prasad; Andraweera, Nalinda; Thilakarathne, Sithira; Tharanga, Praveen

2013-09-03

Body Mass Index (BMI) is used as a useful population-level measure of overweight and obesity. It is used as the same for both sexes and for all ages of adults. The relationship between BMI and body fat percentage (BF %) has been studied in various ethnic groups to estimate the capacity of BMI to predict adiposity. We aimed to study the BMI-BF% relationship, in a group of South Asian adults who have a different body composition compared to presently studied ethnic groups. We examined the influence of age, gender in this relationship and assessed its' linearity or curvilinearity. A cross sectional study was conducted, where adults of 18-83 years were grouped into young (18-39 years) middle aged (40-59 years) and elderly (>60 years). BF% was estimated from bioelectrical impedance analysis. Pearsons' correlation coefficient(r) was calculated to see the relationship between BMI-BF% in the different age groups. Multiple regression analysis was performed to determine the effect of age and gender in the relationship and polynomial regression was carried out to see its' linearity. The relationships between age-BMI, age-BF % were separately assessed. Out of 1114 participants, 49.1% were males. The study sample represented a wide range of BMI values (14.8-41.1 kg/m2,Mean 23.8 ± 4.2 kg/m2). A significant positive correlation was observed between BMI-BF%, in males (r =0.75, p < 0.01; SEE = 4.17) and in females (r = 0.82, p < 0.01; SEE = 3.54) of all ages. Effect of age and gender in the BMI-BF% relationship was significant (p < 0.001); with more effect from gender. Regression line found to be curvilinear in nature at higher BMI values where females (p < 0.000) having a better fit of the curve compared to males (p < 0.05). In both genders, with increase of age, BMI seemed to increase in curvilinear fashion, whereas BF% increased in a linear fashion. BMI strongly correlate with BF % estimated by bioelectrical impedance, in this sub population of South Asian adults. This relationship was curvilinear in nature and was significantly influenced by age and gender. Our findings support the importance of taking age and gender in to consideration when using BMI to predict body fat percentage/obesity, in a population.

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Classical Testing in Functional Linear Models.

PubMed

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.

Classical Testing in Functional Linear Models

PubMed Central

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

PubMed

Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

2013-09-01

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well. Copyright © 2013 Elsevier B.V. All rights reserved.

A Linear Regression and Markov Chain Model for the Arabian Horse Registry

DTIC Science & Technology

1993-04-01

as a tax deduction? Yes No T-4367 68 26. Regardless of previous equine tax deductions, do you consider your current horse activities to be... (Mark one...E L T-4367 A Linear Regression and Markov Chain Model For the Arabian Horse Registry Accesion For NTIS CRA&I UT 7 4:iC=D 5 D-IC JA" LI J:13tjlC,3 lO...the Arabian Horse Registry, which needed to forecast its future registration of purebred Arabian horses . A linear regression model was utilized to

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.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes.

PubMed

Saha, Sanjib; Gerdtham, Ulf-G; Siddiqui, Faiza; Bennet, Louise

2018-02-27

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes

PubMed Central

Siddiqui, Faiza

2018-01-01

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations. PMID:29495529

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-07-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach was justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatland sites in Finland and a tundra site in Siberia. The flux measurements were performed using transparent chambers on vegetated surfaces and opaque chambers on bare peat surfaces. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes and even lower for longer closure times. The degree of underestimation increased with increasing CO2 flux strength and is dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

The radiodensity of cerebrospinal fluid and vitreous humor as indicator of the time since death.

PubMed

Koopmanschap, Desirée H J L M; Bayat, Alireza R; Kubat, Bela; de Bakker, Henri M; Prokop, Mathias W M; Klein, Willemijn M

2016-09-01

After death, a series of changes occur naturally in the human body in a fairly regular pattern. These postmortem changes are detectable on postmortem CT scans (PMCT) and may be useful in estimating the postmortem interval (PMI). The purpose of our study is to correlate the PMCT radiodensities of the cerebrospinal fluid (CSF) and vitreous humor (VH) to the PMI. Three patient groups were included: group A consisted of 5 donated cadavers, group B, 100 in-hospital deceased patients, and group C, 12 out-of-hospital forensic cadavers. Group A were scanned every hour for a maximum of 36 h postmortem, and the tympanic temperature was measured prior to each scan. Groups B and C were scanned once after death (PMI range 0.2-63.8 h). Radiodensities of the VH and CSF were measured in Hounsfield units. Correlation between density and PMI was determined using linear regression and the influence of temperature was assessed by a multivariate regression model. Results from group A were validated in groups B and C. Group A showed increasing radiodensity of the CSF and VH over time (r (2) CSF, 0.65). PMI overruled the influence of temperature (r = 0.99 and p = 0.000). Groups B and C showed more diversity, with CSF and VH radiodensities below the mean regression line of Group A. The formula of this upper limit indicated the maximum PMI and was correct for >95 % of the cadavers. The results of group A showed a significant correlation between CSF radiodensity and PMI. The radiodensities in groups B and C were higher than in group A, therefore the maximum PMI can be estimated with the upper 95 % confidence interval of the correlation line of group A.

[Correlation of retinol binding protein 4 with 
metabolic indexes of glucose and 
lipid, bile cholesterol saturation index].

PubMed

Wang, Wen; Li, Nianfeng

2015-06-01

To measure retinol binding protein 4 (RBP4) levels in serum and bile and to analyze their relationship with insulin resistance, dyslipidemia or cholesterol saturation index (CSI).
 A total of 60 patients with gallstone were divided into a diabetes group (n=30) and a control group (n=30). The concentrations of RBP4 in serum and bile were detected by enzyme-linked immunosorbent assay (ELISA). Enzyme colorimetric method was used to measure the concentration of biliary cholesterol, bile acid and phospholipid. Biliary CSI was calculated by Carey table. Partial correlation and multiple linear regression analysis were used to evaluate the correlation between the RBP4 levels in serum or bile and the above indexes.
 The RBP4 concentrations in serum and bile in the diabetes group were significantly elevated compared with those in the control group (both P<0.01). There was no significant difference in the serum total bile acid (TBA), serum triglyceride (TG), serum high-density lipoprotein (HDL), bile TBA, bile total cholesterol (TC) , bile phospholipids and bile CSI between the 2 groups (all P>0.05); but the serum TC, low density lipoprotein (LDL), fasting blood glucose (FBG), fasting insulin (FINS), and homeostasis model assessment for insulin resistance (HOMA-IR) in the diabetes group were significantly increased compared to those in the control group (all P<0.05). The partial correlation analysis, which was adjusted by age, showed that the bile RBP4 was positively correlated with body mass index (BMI), waist circumference (WC), FINS, FBG, TC, LDL and HOMA-IR (r=0.283, 0.405, 0.685, 0.667, 0.553, 0.424 and 0.735, respectively), and the serum RBP4 was also positively correlated with the WC, FINS, FBG, TC, LDL and HOMA-IR (r=0.317, 0.734, 0.609, 0.528, 0.386 and 0.751, respectively). Stepwise multivariate linear regression analysis suggested that the HOMA-IR, BMI and WC were independently correlated with the level of bile RBP4 (multiple regression equation: Ybile RBP4=2.372XHOMA-IR+0.420XBMI+0.178XWC-26.813), and the serum RBP4 level was correlated with the HOMA-IR and WC independently (multiple regression equation: Yserum RBP4=2.832XHOMA-IR +0.235XWC-20.128). Multiple regression equations showed that HOMA-IR was the strongest correlation factor with RBP4.
 RBP4 concentrations in serum and bile in the diabetes group are significantly higher than those in the control group. HOMA-IR, BMI and WC are independently correlated with the level of bile RBP4. HOMA-IR and WC are independently correlated with the serum RBP4 level. HOMA-IR is the strongest correlation factor with RBP4. RBP4 might play an important role in the course of gallstone formation in Type 2 diabetes mellitus.

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

USGS Publications Warehouse

Kocovsky, P.M.; Carline, R.F.

2006-01-01

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

Thrombocytopenia following implantation of the stentless biological sorin freedom SOLO valve.

PubMed

Gersak, Borut; Gartner, Urska; Antonic, Miha

2011-07-01

Stentless biological valves have proven advantages in hemodynamic performance and left ventricular function compared to stented biological valves. Following a marked postoperative fall in the platelet count of patients after implantation of the Freedom SOLO valve, the study aim was to confirm clinical observations that this effect was more severe in patients receiving Freedom SOLO valves than in those receiving St. Jude Medical (SJM) mechanical aortic valves. Preoperative and postoperative platelet counts were compared in two groups of patients who underwent aortic valve replacement (AVR) without any concomitant procedures between January and December 2007. Patients received either a Freedom SOLO valve (n = 28) or a SJM mechanical valve (n = 41). Mean values of platelet counts were compared using three multiple linear regression models. Platelet counts were significantly lower in the Freedom SOLO group than in the SJM group from the first postoperative day (POD 1) up to POD 6 (p <0.001). In three patients of the Freedom SOLO group the platelet count fell below 30x10(9)/l, while the lowest level in the SJM group was 75x10(9)/l. Based on multiple linear regression models, the type of valve implanted had a statistically significant influence on postoperative platelet counts on POD 1, POD 3, and POD 5 (p <0.001). Whilst the reason for this phenomenon is unknown, the use of consistent monitoring should prevent severe falls in platelet count from becoming dangerous for the patient. Further studies are required to investigate the phenomenon since, despite a shorter cardiopulmonary bypass time, the fall in platelet count was more profound in the Freedom SOLO group.

Sieve estimation of Cox models with latent structures.

PubMed

Cao, Yongxiu; Huang, Jian; Liu, Yanyan; Zhao, Xingqiu

2016-12-01

This article considers sieve estimation in the Cox model with an unknown regression structure based on right-censored data. We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B-spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects. To compute the proposed estimators, we develop a modified blockwise majorization descent algorithm that is efficient and easy to implement. Simulation studies demonstrate that the proposed method performs well in finite sample situations. We also use the primary biliary cirrhosis data to illustrate its application. © 2016, The International Biometric Society.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

Exercise Prescription Using a Group-Normalized Rating of Perceived Exertion in Adolescents and Adults With Spina Bifida.

PubMed

Crytzer, Theresa M; Keramati, Mariam; Anthony, Steven J; Cheng, Yu-Ting; Robertson, Robert J; Dicianno, Brad E

2018-02-03

People with spina bifida (SB) face personal and environmental barriers to exercise that contribute to physical inactivity, obesity, risk of cardiovascular disease, and poor aerobic fitness. The WHEEL rating of perceived exertion (RPE) Scale was validated in people with SB to monitor exercise intensity. However, the psycho-physiological link between RPE and ventilatory breakpoint (Vpt), the group-normalized perceptual response, has not been determined and would provide a starting point for aerobic exercise in this cohort. The primary objectives were to determine the group-normalized RPE equivalent to Vpt based on WHEEL and Borg Scale ratings and to develop a regression model to predict Borg Scale (conditional metric) from WHEEL Scale (criterion metric). The secondary objective was to create a table of interchangeable values between WHEEL and Borg Scale RPE for people with SB performing a load incremental stress test. Cross-sectional observational. University laboratory. Twenty-nine participants with SB. Participants completed a load incremented arm ergometer exercise stress test. WHEEL and Borg Scale ratings were recorded the last 15 seconds of each 1-minute test phase. WHEEL and Borg Scale ratings, metabolic measures (eg, oxygen consumption, carbon dioxide production). Determined Vpt via plots of oxygen consumption and carbon dioxide production against time. Nineteen of 29 participants achieved Vpt (Group A). The mean ± standard deviation peak oxygen consumption at Vpt for Group A was 61.76 ± 16.26. The WHEEL and Borg Scale RPE at Vpt were 5.74 ± 2.58 (range 0-10) and 13.95 ± 3.50 (range 6-19), respectively. A significant linear regression model was developed (Borg Scale rating = 1.22 × WHEEL Scale rating + 7.14) and used to create a WHEEL-to-Borg Scale RPE conversion table. A significant linear regression model and table of interchangeable values was developed for participants with SB. The group-normalized RPE (WHEEL, 5.74; Borg, 13.95) can be used to prescribe and self-regulate arm ergometer exercise intensity approximating the Vpt. II. Copyright © 2018. Published by Elsevier Inc.

Modeling the soil water retention curves of soil-gravel mixtures with regression method on the Loess Plateau of China.

PubMed

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present.

Modeling the Soil Water Retention Curves of Soil-Gravel Mixtures with Regression Method on the Loess Plateau of China

PubMed Central

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present. PMID:23555040

Effects of purified eicosapentaenoic and docosahexaenoic acids in nonalcoholic fatty liver disease: results from the Welcome* study.

PubMed

Scorletti, Eleonora; Bhatia, Lokpal; McCormick, Keith G; Clough, Geraldine F; Nash, Kathryn; Hodson, Leanne; Moyses, Helen E; Calder, Philip C; Byrne, Christopher D

2014-10-01

There is no licensed treatment for non-alcoholic fatty liver disease (NAFLD), a condition that increases risk of chronic liver disease, type 2 diabetes and cardiovascular disease. We tested whether 15-18 months treatment with docosahexaenoic acid (DHA) plus eicosapentaenoic acid (EPA) (Omacor/Lovaza) (4 g/day) decreased liver fat and improved two histologically-validated liver fibrosis biomarker scores (primary outcomes). Patients with NAFLD were randomised in a double blind placebo-controlled trial [DHA+EPA(n=51), placebo(n=52)]. We quantified liver fat percentage (%) by magnetic resonance spectroscopy in three liver zones. We measured liver fibrosis using two validated scores. We tested adherence to the intervention (Omacor group) and contamination (with DHA and EPA) (placebo group) by measuring erythrocyte percentage DHA and EPA enrichment (gas chromatography). We undertook multivariable linear regression to test effects of: a) DHA+EPA treatment (ITT analyses) and b) erythrocyte DHA and EPA enrichment (secondary analysis). Median (IQR) baseline and end of study liver fat% were 21.7 (19.3) and 19.7 (18.0) (placebo), and 23.0 (36.2) and 16.3 (22.0), (DHA+EPA). In the fully adjusted regression model there was a trend towards improvement in liver fat% with DHA+EPA treatment (β=-3.64 (95%CI -8.0,0.8); p=0.1) but there was evidence of contamination in the placebo group and variable adherence to the intervention in the Omacor group. Further regression analysis showed that DHA enrichment was independently associated with a decrease in liver fat% (for each 1% enrichment, β=-1.70 (95%CI -2.9,-0.5); p=0.007). No improvement in the fibrosis scores occurred. Conclusion. Erythrocyte DHA enrichment with DHA+EPA treatment is linearly associated with decreased liver fat%. Substantial decreases in liver fat% can be achieved with high percentage erythrocyte DHA enrichment in NAFLD. (Hepatology 2014;).

Correlates and Predictors of Psychological Distress Among Older Asian Immigrants in California.

PubMed

Chang, Miya; Moon, Ailee

2016-01-01

Psychological distress occurs frequently in older minority immigrants because many have limited social resources and undergo a difficult process related to immigration and acculturation. Despite a rapid increase in the number of Asian immigrants, relatively little research has focused on subgroup mental health comparisons. This study examines the prevalence of psychological distress, and relationship with socio-demographic factors, and health care utilization among older Asian immigrants. Weighted data from Asian immigrants 65 and older from 5 countries (n = 1,028) who participated in the California Health Interview Survey (CHIS) were analyzed descriptively and in multiple linear regressions. The prevalence of psychological distress varied significantly across the 5 ethnic groups, from Filipinos (4.83%) to Chinese (1.64%). General health status, cognitive and physical impairment, and health care utilization are all associated (p < .05) with psychological distress in multiple linear regressions. These findings are similar to those from previous studies. The findings reinforce the need to develop more culturally effective mental health services and outreach programs.

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

ERIC Educational Resources Information Center

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

2013-01-01

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

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

PubMed

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

2009-11-01

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

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

USGS Publications Warehouse

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

2009-01-01

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

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-11-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

[Effect of long-term use of albendazole on mice liver].

PubMed

Zheng, Qi; Liu, Cong-Shan; Jiang, Bin; Xu, Li-Li; Zhang, Hao-Bing

2013-06-01

To observe the change in serum levels of alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), direct bilirubin (DBL), indirect bilirubin (IBIL), albumin (ALB) and globulin (GLB), and mouse liver ultrastructure during 1-16 weeks of albendazole treatment. 180 female Kunming mice were divided randomly into albendazole treatment group and negative control group. Each mouse of albendazole treatment group was treated with 136.3 mg/(kg x d) albendazole. The mice in control group were given same amount of physiological saline. After 1, 2, 4, 6, 8, 10, 12, 14 and 16 weeks of treatment, 10 mice from each group were randomly selected, serum samples were collected and analyzed for the above seven liver function indices. Pathological changes of liver were observed by transmission electron microscopy. Linear regression analysis was conducted for the relationship between liver function indices(dependent variable) and pathological scores (independent variable). During 1-16 weeks of albendazole treatment, there was no significant difference in serum levels of DBL, IBIL, ALB and GLB between albendazole treatment group and control group. Compared with other treatment period, after 12 weeks of treatment the serum levels of ALT (55.2 +/- 23.7), AST(176.4 +/- 49.2) and ALP(141.1 +/- 19.4) in albendazole treatment group were higher than that of the control (35.5 +/- 8.6, 108.2 +/- 21.9, 84.0 +/- 24.8) (P < 0.05). After 2, 8, 10, 12 and 14 weeks of treatment, the pathological score of albendazole treatment group was 11.8 +/- 4.8, 10.6 +/- 4.8, 13.6 +/- 3.5, 29.8 +/- 10.7, and 5.6 +/- 2.5, respectively, which was higher than that of the control (0.8 +/- 0.4, 1.2 +/- 0.8, 2.4 +/- 2.0, 1.2 +/- 0.4, 1.4 +/- 1.1) (P < 0.05). Among the three liver function indices AST, ALT and ALP, AST was the best fit index for linear regression. The regression formula was Y = -17.616 + 0.188X. Long-term treatment with albendazole at a dosage of 136.3 mg/(kg x d) for mice can cause significant elevation of serum levels of ALT, AST and ALP, and result in mild pathological changes in the liver.

Assessing flight safety differences between the United States regional and major airlines

NASA Astrophysics Data System (ADS)

Sharp, Broderick H.

During 2008, the U.S. domestic airline departures exceeded 28,000 flights per day. Thirty-nine or less than 0.2 of 1% of these flights resulted in operational incidents or accidents. However, even a low percentage of airline accidents and incidents continue to cause human suffering and property loss. The charge of this study was the comparison of U.S. major and regional airline safety histories. The study spans safety events from January 1982 through December 2008. In this quantitative analysis, domestic major and regional airlines were statistically tested for their flight safety differences. Four major airlines and thirty-seven regional airlines qualified for the safety study which compared the airline groups' fatal accidents, incidents, non-fatal accidents, pilot errors, and the remaining six safety event probable cause types. The six other probable cause types are mechanical failure, weather, air traffic control, maintenance, other, and unknown causes. The National Transportation Safety Board investigated each airline safety event, and assigned a probable cause to each event. A sample of 500 events was randomly selected from the 1,391 airlines' accident and incident population. The airline groups' safety event probabilities were estimated using the least squares linear regression. A probability significance level of 5% was chosen to conclude the appropriate research question hypothesis. The airline fatal accidents and incidents probability levels were 1.2% and 0.05% respectively. These two research questions did not reach the 5% significance level threshold. Therefore, the airline groups' fatal accidents and non-destructive incidents probabilities favored the airline groups' safety differences hypothesis. The linear progression estimates for the remaining three research questions were 71.5% for non-fatal accidents, 21.8% for the pilot errors, and 7.4% significance level for the six probable causes. These research questions' linear regressions are greater than the 5% level. Consequently, these three research questions favored airline groups' safety similarities hypothesis. The study indicates the U.S. domestic major airlines were safer than the regional airlines. Ideas for potential airline safety progress can examine pilot fatigue, the airline groups' hiring policies, the government's airline oversight personnel, or the comparison of individual airline's operational policies.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

GIS Tools to Estimate Average Annual Daily Traffic

DOT National Transportation Integrated Search

2012-06-01

This project presents five tools that were created for a geographical information system to estimate Annual Average Daily : Traffic using linear regression. Three of the tools can be used to prepare spatial data for linear regression. One tool can be...

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

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

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

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

Comparison between light scattering and gravimetric samplers for PM10 mass concentration in poultry and pig houses

NASA Astrophysics Data System (ADS)

Cambra-López, María; Winkel, Albert; Mosquera, Julio; Ogink, Nico W. M.; Aarnink, André J. A.

2015-06-01

The objective of this study was to compare co-located real-time light scattering devices and equivalent gravimetric samplers in poultry and pig houses for PM10 mass concentration, and to develop animal-specific calibration factors for light scattering samplers. These results will contribute to evaluate the comparability of different sampling instruments for PM10 concentrations. Paired DustTrak light scattering device (DustTrak aerosol monitor, TSI, U.S.) and PM10 gravimetric cyclone sampler were used for measuring PM10 mass concentrations during 24 h periods (from noon to noon) inside animal houses. Sampling was conducted in 32 animal houses in the Netherlands, including broilers, broiler breeders, layers in floor and in aviary system, turkeys, piglets, growing-finishing pigs in traditional and low emission housing with dry and liquid feed, and sows in individual and group housing. A total of 119 pairs of 24 h measurements (55 for poultry and 64 for pigs) were recorded and analyzed using linear regression analysis. Deviations between samplers were calculated and discussed. In poultry, cyclone sampler and DustTrak data fitted well to a linear regression, with a regression coefficient equal to 0.41, an intercept of 0.16 mg m-3 and a correlation coefficient of 0.91 (excluding turkeys). Results in turkeys showed a regression coefficient equal to 1.1 (P = 0.49), an intercept of 0.06 mg m-3 (P < 0.0001) and a correlation coefficient of 0.98. In pigs, we found a regression coefficient equal to 0.61, an intercept of 0.05 mg m-3 and a correlation coefficient of 0.84. Measured PM10 concentrations using DustTraks were clearly underestimated (approx. by a factor 2) in both poultry and pig housing systems compared with cyclone pre-separators. Absolute, relative, and random deviations increased with concentration. DustTrak light scattering devices should be self-calibrated to investigate PM10 mass concentrations accurately in animal houses. We recommend linear regression equations as animal-specific calibration factors for DustTraks instead of manufacturer calibration factors, especially in heavily dusty environments such as animal houses.

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Fluoroscopy Learning Curve in Hip Arthroscopy-A Single Surgeon's Experience.

PubMed

Smith, Kevin M; Duplantier, Neil L; Crump, Kimbelyn H; Delgado, Domenica A; Sullivan, Stephanie L; McCulloch, Patrick C; Harris, Joshua D

2017-10-01

To determine if (1) absorbed radiation dose and (2) fluoroscopy time decreased with experience over the first 100 cases of a single surgeon's hip arthroscopy practice. Subjects who underwent hip arthroscopy for symptomatic femoroacetabular impingement and labral injury were eligible for analysis. Inclusion criteria included the first 100 subjects who underwent hip arthroscopy by a single surgeon (December 2013 to December 2014). Subject demographics, procedure details, fluoroscopy absorbed dose (milligray [mGy]), and time were recorded. Subjects were categorized by date of surgery to one of 4 possible groups (25 per group). One-way analysis of variance was used to determine if a significant difference in dose (mGy) or time was present between groups. Simple linear regression analysis was performed to determine the relation between case number and both radiation dose and fluoroscopy time. Subjects underwent labral repair (n = 93), cam osteoplasty (n = 90), and pincer acetabuloplasty (n = 65). There was a significant (P < .001 for both) linear regression between case number and both radiation dose and fluoroscopy time. A significant difference in mGy was observed between groups, group 1 the highest and group 4 the lowest amounts of radiation (P = .003). Comparing individual groups, group 4 was found to have a significantly lower amount of radiation than group 1 (P = .002), though it was not significantly lower than that of group 2 (P = .09) or group 3 (P = .08). A significant difference in fluoroscopy time was observed between groups, group 1 the highest and group 4 the lowest times (P = .05). Comparing individual groups, group 4 was found to have a significantly lower fluoroscopy time than group 1 (P = .039). Correction for weight, height, and body mass index all revealed the same findings: significant (P < .05) differences in both dose and time across groups. The absorbed dose of radiation and fluoroscopy time decreased significantly over the first 100 cases of a single surgeon's hip arthroscopy practice learning curve. Level IV, therapeutic, retrospective, noncomparative case series. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

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.

Relationship between mechanical factors and pelvic tilt in adults with and without low back pain.

PubMed

Król, Anita; Polak, Maciej; Szczygieł, Elżbieta; Wójcik, Paweł; Gleb, Klaudia

2017-01-01

The assessment of the lumbo-pelvic complex parameters is the basic procedure during the examination of the patients with low back pain syndrome (LBP). The aim of the study was to define the relationship between pelvic tilt and following factors: age, BMI, ability to activate deep abdominal muscles, iliopsoas and hamstrings muscles length, lumbar lordosis and thoracic kyphosis angle value, in adults with and without low back pain. The study covered a group of 60 female students aged 20-26. Average age was 22 years ± 1.83 (median = 22.5 years). In order to investigate the relationship between the anterior pelvic tilt and the analysed variables, simple linear regression and multiple linear regression were carried out. Individuals with and without pain differed significantly in terms of age, p < 0.001. There was a statistically significant relationship between the anterior pelvic tilt and the LBP (R2 = 0.07, p = 0.049) and the lumbar lordosis (R2 = 0.13, p = 0.02). The position of the pelvis depends on age, angle value of lumbar lordosis and BMI. Individuals with and without pain differed significantly in terms of the anterior pelvic tilt. The risk of LBP incidence increased with age in the study group.

Patient satisfaction, empowerment, and health and disability status effects of a disease management-health promotion nurse intervention among Medicare beneficiaries with disabilities.

PubMed

Friedman, Bruce; Wamsley, Brenda R; Liebel, Dianne V; Saad, Zabedah B; Eggert, Gerald M

2009-12-01

To report the impact on patient and informal caregiver satisfaction, patient empowerment, and health and disability status of a primary care-affiliated disease self-management-health promotion nurse intervention for Medicare beneficiaries with disabilities and recent significant health services use. The Medicare Primary and Consumer-Directed Care Demonstration was a 24-month randomized controlled trial that included a nurse intervention. The present study (N = 766) compares the nurse (n = 382) and control (n = 384) groups. Generalized linear models for repeated measures, linear regression, and ordered logit regression were used. The patients whose activities of daily living (ADL) were reported by the same respondent at baseline and 22 months following baseline had significantly fewer dependencies at 22 months than did the control group (p = .038). This constituted the vast majority of respondents. In addition, patient satisfaction significantly improved for 6 of 7 domains, whereas caregiver satisfaction improved for 2 of 8 domains. However, the intervention had no effect on empowerment, self-rated health, the SF-36 physical and mental health summary scores, and the number of dependencies in instrumental ADL. If confirmed in other studies, this intervention holds the potential to reduce the rate of functional decline and improve satisfaction for Medicare beneficiaries with ADL dependence.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

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

2017-01-01

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

[Relationship between highly sensitive cardiac troponin T and sepsis and outcome in critically ill patients].

PubMed

Wang, T T; Jiang, L

2017-10-01

Objective: To investigate the prognostic value of highly sensitive cardiac Troponin T (hs-cTn T) for sepsis in critically ill patients. Methods: Patients estimated to stay in the ICU of Fuxing Hospital for more than 24h were enrolled at from March 2014 to December 2014. Serum hs-cTn T was tested within two hours. Univariate and multivariate linear regression analyses were used to determine the association of variables with the hs-cTn T. Multivariable logistic regression analysis was used to evaluate the risk factors of 28-day mortality. Results: A total of 125 patients were finally enrolled including 68 patients with sepsis and 57 without. The levels of hs-cTn T in sepsis and non-sepsis groups were significantly different[52.0(32.5, 87.5) ng/L vs 14.0(6.5, 29.0) ng/L respectively, P <0.001]. In sepsis group, hs-cTn T among common sepsis, severe sepsis and septic shock were similar. Hs-cTn T was significantly higher in non-survivors than survivors [27(13, 52)ng/L vs 44.5(28.8, 83.5)ng/L, P <0.001]. Age, sepsis, serum creatinine were independent risk factors affecting hs-cTn T by multivariate linear regression analyses. But hs-cTn T was not a risk factor for death. Conclusion: Patients with sepsis had higher serum hs-cTn T than those without sepsis. but it was not found to be associated with the severity of sepsis.

The association of pain and fear of movement/reinjury with function during anterior cruciate ligament reconstruction rehabilitation.

PubMed

Chmielewski, Terese L; Jones, Debi; Day, Tim; Tillman, Susan M; Lentz, Trevor A; George, Steven Z

2008-12-01

Cross-sectional. To measure fear of movement/reinjury levels and determine the association with function at different timeframes during anterior cruciate ligament (ACL) reconstruction rehabilitation. We hypothesized that fear of movement/reinjury would decrease during rehabilitation and be inversely related with function. Fear of movement/reinjury can prevent return to sports after ACL reconstruction, but it has not been studied during rehabilitation. Demographic data and responses on the shortened version of Tampa Scale for Kinesiophobia (TSK-11), 8-Item Short-Form Health Survey (SF-8), and International Knee Documentation Committee (IKDC) subjective form were extracted from a clinical database for 97 patients in the first year after ACL reconstruction. Three groups were formed: group 1, less than or equal to 90 days; group 2, 91 to 180 days; group 3: 181 to 372 days post-ACL reconstruction. Group differences in TSK-11 score, SF-8 bodily pain rating, and IKDC scores were determined. Hierarchical linear regression models were created for each group, with IKDC score as the dependent variable and demographic factors, SF-8 bodily pain rating, and TSK-11 score as independent variables. TSK-11 score was higher in group 1 than in group 3 (P < .05). Across the groups, SF-8 bodily pain rating decreased (P < .001) and IKDC score increased (P < .001). SF-8 bodily pain rating was a significant factor in the regression model for all groups, whereas TSK-11 score only contributed to the regression model in group 3 (partial correlation, -0.529). Pain was consistently associated with function across the timeframes studied. Fear of movement/reinjury levels appear to decrease during ACL reconstruction rehabilitation and are associated with function in the timeframe when patients return to sports. Prognosis, level 4.

Scoring and staging systems using cox linear regression modeling and recursive partitioning.

PubMed

Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H

2006-01-01

Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

Cognition in patients with burn injury in the inpatient rehabilitation population.

PubMed

Purohit, Maulik; Goldstein, Richard; Nadler, Deborah; Mathews, Katie; Slocum, Chloe; Gerrard, Paul; DiVita, Margaret A; Ryan, Colleen M; Zafonte, Ross; Kowalske, Karen; Schneider, Jeffrey C

2014-07-01

To analyze potential cognitive impairment in patients with burn injury in the inpatient rehabilitation population. Rehabilitation patients with burn injury were compared with the following impairment groups: spinal cord injury, amputation, polytrauma and multiple fractures, and hip replacement. Differences between the groups were calculated for each cognitive subscale item and total cognitive FIM. Patients with burn injury were compared with the other groups using a bivariate linear regression model. A multivariable linear regression model was used to determine whether differences in cognition existed after adjusting for covariates (eg, sociodemographic factors, facility factors, medical complications) based on previous studies. Inpatient rehabilitation facilities. Data from Uniform Data System for Medical Rehabilitation from 2002 to 2011 for adults with burn injury (N=5347) were compared with other rehabilitation populations (N=668,816). Not applicable. Comparison of total cognitive FIM scores and subscales (memory, verbal comprehension, verbal expression, social interaction, problem solving) for patients with burn injury versus other rehabilitation populations. Adults with burn injuries had an average total cognitive FIM score ± SD of 26.8±7.0 compared with an average FIM score ± SD of 28.7±6.0 for the other groups combined (P<.001). The subscale with the greatest difference between those with burn injury and the other groups was memory (5.1±1.7 compared with 5.6±1.5, P<.001). These differences persisted after adjustment for covariates. Adults with burn injury have worse cognitive FIM scores than other rehabilitation populations. Future research is needed to determine the impact of this comorbidity on patient outcomes and potential interventions for these deficits. Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Upper arm elevation and repetitive shoulder movements: a general population job exposure matrix based on expert ratings and technical measurements.

PubMed

Dalbøge, Annett; Hansson, Gert-Åke; Frost, Poul; Andersen, Johan Hviid; Heilskov-Hansen, Thomas; Svendsen, Susanne Wulff

2016-08-01

We recently constructed a general population job exposure matrix (JEM), The Shoulder JEM, based on expert ratings. The overall aim of this study was to convert expert-rated job exposures for upper arm elevation and repetitive shoulder movements to measurement scales. The Shoulder JEM covers all Danish occupational titles, divided into 172 job groups. For 36 of these job groups, we obtained technical measurements (inclinometry) of upper arm elevation and repetitive shoulder movements. To validate the expert-rated job exposures against the measured job exposures, we used Spearman rank correlations and the explained variance[Formula: see text] according to linear regression analyses (36 job groups). We used the linear regression equations to convert the expert-rated job exposures for all 172 job groups into predicted measured job exposures. Bland-Altman analyses were used to assess the agreement between the predicted and measured job exposures. The Spearman rank correlations were 0.63 for upper arm elevation and 0.64 for repetitive shoulder movements. The expert-rated job exposures explained 64% and 41% of the variance of the measured job exposures, respectively. The corresponding calibration equations were y=0.5%time+0.16×expert rating and y=27°/s+0.47×expert rating. The mean differences between predicted and measured job exposures were zero due to calibration; the 95% limits of agreement were ±2.9% time for upper arm elevation >90° and ±33°/s for repetitive shoulder movements. The updated Shoulder JEM can be used to present exposure-response relationships on measurement scales. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Does Age Affect the Relationship Between Pain and Disability? A Descriptive Study in Individuals Suffering From Chronic Low Back Pain.

PubMed

Houde, Francis; Cabana, François; Léonard, Guillaume

2016-01-01

Previous studies have revealed a weak to moderate relationship between pain and disability in individuals suffering from low back pain (LBP). However, to our knowledge, no studies have evaluated if this relationship is different between young and older adults. The objective of this descriptive, cross-sectional study was to determine whether the relationship between LBP intensity and physical disability is different between young and older adults. Pain intensity (measured with a visual analog scale) and physical disability scores (measured with the Oswestry Disability Index) were collected from the medical files of 164 patients with LBP. Separate Pearson correlation coefficients were calculated between these 2 variables for young (mean age 40 ± 6 years, n = 82) and older (62 ± 9 years, n = 82) individuals and a Fisher r-to-z transformation was used to test for group differences in the strength of the relationship. Linear regression analyses were also performed to determine whether the slope of the association was different between the 2 groups. A significant and positive association was found between pain intensity and disability for both young and older individuals. However, the correlation was stronger in the young group (r = 0.66; P < .01) than in the older group (r = 0.44; P < .01) (Fisher Z = 2.03; P < .05). The linear regression model also revealed that the slope of the relationship was steeper in the young group (P < .05). Although both young and older individuals showed a significant association between pain intensity and disability, the relationship between these 2 variables was more tenuous in older individuals than in young patients. Future research is essential to identify the factors underlying this age-related difference.

Catatonic Stupor in Schizophrenic Disorders and Subsequent Medical Complications and Mortality

PubMed Central

Funayama, Michitaka; Takata, Taketo; Koreki, Akihiro; Ogino, Satoyuki; Mimura, Masaru

2018-01-01

ABSTRACT Objective Although catatonia can occur secondary to a general medical condition, catatonia itself has been known to lead to various medical compolications. Although case reports on the association of catatonia with subsequent medical complications have been documented, no comprehensive large-scale study has been performed. To investigate specific medical complications after catatonia, we conducted a retrospective cohort study of specific medical complications of schizophrenia patients with catatonia. Methods The 1719 schizophrenia inpatients in our study were categorized into two groups: the catatonia group, i.e., those who exhibited catatonic stupor while they were hospitalized, and the noncatatonia group, i.e., those who never exhibited catatonic stupor. Differences between the two groups in the occurrence of subsequent medical complications were examined using linear and logistic regression analyses, and models were adjusted for potentially confounding factors. Results The catatonia group had an increased risk for mortality (odds ratio = 4.8, 95% confidence interval = 2.0–10.6, p < .01) and certain specific medical complications, i.e., pneumonia, urinary tract infection, sepsis, disseminated intravascular coagulation, rhabdomyolysis, dehydration, deep venous thrombosis, pulmonary embolism, urinary retention, decubitus, arrhythmia, renal failure, neuroleptic malignant syndrome, hypernatremia, and liver dysfunction (all p values < .01, except for deep venous thrombosis, p = .04 in the multiple linear regression analysis). Conclusions Catatonic stupor in schizophrenia substantially raises the risk for specific medical complications and mortality. Hyperactivity of the sympathetic nervous system, dehydration, and immobility, which are frequently involved in catatonia, might contribute to these specific medical complications. In catatonia, meticulous care for both mental and medical conditions should be taken to reduce the risk of adverse medical consequences. PMID:29521882

Trends in asthma mortality in the 0- to 4-year and 5- to 34-year age groups in Brazil.

PubMed

Graudenz, Gustavo Silveira; Carneiro, Dominique Piacenti; Vieira, Rodolfo de Paula

2017-01-01

To provide an update on trends in asthma mortality in Brazil for two age groups: 0-4 years and 5-34 years. Data on mortality from asthma, as defined in the International Classification of Diseases, were obtained for the 1980-2014 period from the Mortality Database maintained by the Information Technology Department of the Brazilian Unified Health Care System. To analyze time trends in standardized asthma mortality rates, we conducted an ecological time-series study, using regression models for the 0- to 4-year and 5- to 34-year age groups. There was a linear trend toward a decrease in asthma mortality in both age groups, whereas there was a third-order polynomial fit in the general population. Although asthma mortality showed a consistent, linear decrease in individuals ≤ 34 years of age, the rate of decline was greater in the 0- to 4-year age group. The 5- to 34-year group also showed a linear decline in mortality, and the rate of that decline increased after the year 2004, when treatment with inhaled corticosteroids became more widely available. The linear decrease in asthma mortality found in both age groups contrasts with the nonlinear trend observed in the general population of Brazil. The introduction of inhaled corticosteroid use through public policies to control asthma coincided with a significant decrease in asthma mortality rates in both subsets of individuals over 5 years of age. The causes of this decline in asthma-related mortality in younger age groups continue to constitute a matter of debate. Apresentar uma atualização das tendências da mortalidade da asma no Brasil em duas faixas etárias: 0-4 anos e 5-34 anos. Dados relativos ao período de 1980 a 2014 referentes à mortalidade da asma, conforme se definiu na Classificação Internacional de Doenças, foram extraídos Sistema de Informação sobre Mortalidade do Departamento de Tecnologia da Informação do Sistema Único de Saúde. Para analisar as tendências temporais das taxas padronizadas de mortalidade da asma, realizou-se um estudo ecológico de séries temporais com modelos de regressão para as faixas etárias de 0 a 4 anos e 5 a 34 anos. Houve uma tendência linear de redução da mortalidade da asma em ambas as faixas etárias e uma tendência polinomial de terceira ordem na população geral. Embora a mortalidade da asma tenha apresentado redução linear consistente em indivíduos com idade ≤ 34 anos, a taxa de declínio foi maior na faixa etária de 0 a 4 anos. A faixa etária de 5 a 34 anos também apresentou redução linear da mortalidade, e essa redução tornou-se mais pronunciada após o ano de 2004, quando o tratamento com corticosteroides inalatórios tornou-se mais amplamente disponível. A redução linear da mortalidade da asma em ambas as faixas etárias contrasta com a tendência não linear observada na população geral do Brasil. A introdução do uso de corticosteroides inalatórios por meio de políticas públicas de controle da asma coincidiu com uma diminuição significativa das taxas de mortalidade da asma em ambos os subgrupos de indivíduos com mais de 5 anos de idade. As causas dessa redução da mortalidade da asma em faixas etárias mais jovens ainda são objeto de debate.

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

EPA Science Inventory

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

A simplified competition data analysis for radioligand specific activity determination.

PubMed

Venturino, A; Rivera, E S; Bergoc, R M; Caro, R A

1990-01-01

Non-linear regression and two-step linear fit methods were developed to determine the actual specific activity of 125I-ovine prolactin by radioreceptor self-displacement analysis. The experimental results obtained by the different methods are superposable. The non-linear regression method is considered to be the most adequate procedure to calculate the specific activity, but if its software is not available, the other described methods are also suitable.

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

NASA Astrophysics Data System (ADS)

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

2009-08-01

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

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

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

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

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

Radiographic assessment of lower third molar eruption in different anteroposterior skeletal patterns and age-related groups.

PubMed

Jakovljevic, Aleksandar; Lazic, Emira; Soldatovic, Ivan; Nedeljkovic, Nenad; Andric, Miroslav

2015-07-01

To analyze radiographic predictors for lower third molar eruption among subjects with different anteroposterior skeletal relations and of different age groups. In total, 300 lower third molars were recorded on diagnostic digital orthopantomograms (DPTs) and lateral cephalograms (LCs). The radiographs were grouped according to sagittal intermaxillary angle (ANB), subject age, and level of lower third molar eruption. The DPT was used to analyze retromolar space, mesiodistal crown width, space/width ratio, third and second molar angulation (α, γ), third molar inclination (β), and gonion angle. The LC was used to determine ANB, angles of maxillar and mandibular prognathism (SNA, SNB), mandibular plane angle (SN/MP), and mandibular lengths. A logistic regression model was created using the statistically significant predictors. The logistic regression analysis revealed a statistically significant impact of β angle and distance between gonion and gnathion (Go-Gn) on the level of lower third molar eruption (P < .001 and P < .015, respectively). The retromolar space was significantly increased in the adult subgroup for all skeletal classes. The lower third molar impaction rate was significantly higher in the adult subgroup with the Class II (62.3%) compared with Class III subjects (31.7%; P < .013). The most favorable values of linear and angular predictors of mandibular third molar eruption were measured in Class III subjects. For valid estimation of mandibular third molar eruption, certain linear and angular measures (β angle, Go-Gn), as well as the size of the retromolar space, need to be considered.

An Investigation of Age-Related Iron Deposition Using Susceptibility Weighted Imaging

PubMed Central

Wang, Dan; Li, Wen-Bin; Wei, Xiao-Er; Li, Yue-Hua; Dai, Yong-Ming

2012-01-01

Aim To quantify age-dependent iron deposition changes in healthy subjects using Susceptibility Weighted Imaging (SWI). Materials and Methods In total, 143 healthy volunteers were enrolled. All underwent conventional MR and SWI sequences. Subjects were divided into eight groups according to age. Using phase images to quantify iron deposition in the head of the caudate nucleus and the lenticular nucleus, the angle radian value was calculated and compared between groups. ANOVA/Pearson correlation coefficient linear regression analysis and polynomial fitting were performed to analyze the relationship between iron deposition in the head of the caudate nucleus and lenticular nucleus with age. Results Iron deposition in the lenticular nucleus increased in individuals aged up to 40 years, but did not change in those aged over 40 years once a peak had been reached. In the head of the caudate nucleus, iron deposition peaked at 60 years (p<0.05). The correlation coefficients for iron deposition in the L-head of the caudate nucleus, R-head of the caudate nucleus, L-lenticular nucleus and R-lenticular nucleus with age were 0.67691, 0.48585, 0.5228 and 0.5228 (p<0.001, respectively). Linear regression analyses showed a significant correlation between iron deposition levels in with age groups. Conclusions Iron deposition in the lenticular nucleus was found to increase with age, reaching a plateau at 40 years. Iron deposition in the head of the caudate nucleus also increased with age, reaching a plateau at 60 years. PMID:23226360

Pushing the boundaries of viability: the economic impact of extreme preterm birth.

PubMed

Petrou, Stavros; Henderson, Jane; Bracewell, Melanie; Hockley, Christine; Wolke, Dieter; Marlow, Neil

2006-02-01

Previous assessments of the economic impact of preterm birth focussed on short term health service costs across the broad spectrum of prematurity. To estimate the societal costs of extreme preterm birth during the sixth year after birth. Unit costs were applied to estimates of health, social and broader resource use made by 241 children born at 20 through 25 completed weeks of gestation in the United Kingdom and Republic of Ireland and a comparison group of 160 children born at full term. Societal costs per child during the sixth year after birth were estimated and subjected to a rigorous sensitivity analysis. The effects of gestational age at birth on annual societal costs were analysed, first in a simple linear regression and then in a multiple linear regression. Mean societal costs over the 12 month period were 9541 pounds sterling (standard deviation 11,678 pounds sterling) for the extreme preterm group and 3883 pounds sterling (1098 pounds sterling) for the term group, generating a mean cost difference of 5658 pounds sterling (bootstrap 95% confidence interval: 4203 pounds sterling, 7256 pounds sterling) that was statistically significant (P<0.001). After adjustment for clinical and sociodemographic covariates, sex-specific extreme preterm birth was a strong predictor of high societal costs. The results of this study should facilitate the effective planning of services and may be used to inform the development of future economic evaluations of interventions aimed at preventing extreme preterm birth or alleviating its effects.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Objective Structured Assessment of Technical Skills (OSATS) evaluation of theoretical versus hands-on training of vaginal breech delivery management: a randomized trial.

PubMed

Buerkle, Bernd; Rueter, Katharina; Hefler, Lukas A; Tempfer-Bentz, Eva-Katrin; Tempfer, Clemens B

2013-12-01

To compare the skills of performing a vaginal breech (VB) delivery after hands-on training versus demonstration. We randomized medical students to a 30-min demonstration (group 1) or a 30-min hands-on (group 2) training session using a standardized VB management algorithm on a pelvic training model. Subjects were tested with a 25 item Objective Structured Assessment of Technical Skills (OSATS) scoring system immediately after training and 72 h thereafter. OSATS scores were the primary outcome. Performance time (PT), self assessment (SA), confidence (CON), and global rating scale (GRS) were the secondary outcomes. Statistics were performed using the Mann-Whitney U-test, chi-square test, and multiple linear regression analysis. 172 subjects were randomized. OSATS scores (primary outcome) were significantly higher in group 2 (n=88) compared to group 1 (n=84) (21.18±2.29 vs. 20.19±2.37, respectively; p=0.006). The secondary outcomes GRS (10.31±2.28 vs. 9.17±2.21; p=0.001), PT (214.60±57.97 s vs. 246.98±59.34 s; p<0.0001), and CON (3.14±0.89 vs. 2.85±0.90; p=0.04) were also significantly different between groups, favoring group 2. After 72 h, primary and secondary outcomes were not significantly different between groups. In a multiple linear regression analysis, group assignment (odds ratio [OR] 1.60; 95% confidence interval [CI] 1.14-2.05; p<0.0001) and gender (OR 2.91; 95% CI 2.45-3.38; p<0.0001) independently influenced OSATS scores. Hands-on training leads to a significant improvement of VB management in a pelvic training model, but this effect was only seen in the short term. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Age- and sex-dependent regression models for predicting the live weight of West African Dwarf goat from body measurements.

PubMed

Sowande, O S; Oyewale, B F; Iyasere, O S

2010-06-01

The relationships between live weight and eight body measurements of West African Dwarf (WAD) goats were studied using 211 animals under farm condition. The animals were categorized based on age and sex. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), and length of hindquarter (LHQ) were fitted into simple linear, allometric, and multiple-regression models to predict live weight from the body measurements according to age group and sex. Results showed that live weight, HG, BL, LHQ, HL, and HW increased with the age of the animals. In multiple-regression model, HG and HL best fit the model for goat kids; HG, HW, and HL for goat aged 13-24 months; while HG, LHQ, HW, and HL best fit the model for goats aged 25-36 months. Coefficients of determination (R(2)) values for linear and allometric models for predicting the live weight of WAD goat increased with age in all the body measurements, with HG being the most satisfactory single measurement in predicting the live weight of WAD goat. Sex had significant influence on the model with R(2) values consistently higher in females except the models for LHQ and HW.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data.

PubMed

Huff, Andrew G; Hodges, James S; Kennedy, Shaun P; Kircher, Amy

2015-08-01

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems' criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank-order of food and agriculture system criticality to a large degree. © 2015 Society for Risk Analysis.

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

DTIC Science & Technology

2013-01-01

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

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies.

PubMed

Buscot, Marie-Jeanne; Wotherspoon, Simon S; Magnussen, Costan G; Juonala, Markus; Sabin, Matthew A; Burgner, David P; Lehtimäki, Terho; Viikari, Jorma S A; Hutri-Kähönen, Nina; Raitakari, Olli T; Thomson, Russell J

2017-06-06

Bayesian hierarchical piecewise regression (BHPR) modeling has not been previously formulated to detect and characterise the mechanism of trajectory divergence between groups of participants that have longitudinal responses with distinct developmental phases. These models are useful when participants in a prospective cohort study are grouped according to a distal dichotomous health outcome. Indeed, a refined understanding of how deleterious risk factor profiles develop across the life-course may help inform early-life interventions. Previous techniques to determine between-group differences in risk factors at each age may result in biased estimate of the age at divergence. We demonstrate the use of Bayesian hierarchical piecewise regression (BHPR) to generate a point estimate and credible interval for the age at which trajectories diverge between groups for continuous outcome measures that exhibit non-linear within-person response profiles over time. We illustrate our approach by modeling the divergence in childhood-to-adulthood body mass index (BMI) trajectories between two groups of adults with/without type 2 diabetes mellitus (T2DM) in the Cardiovascular Risk in Young Finns Study (YFS). Using the proposed BHPR approach, we estimated the BMI profiles of participants with T2DM diverged from healthy participants at age 16 years for males (95% credible interval (CI):13.5-18 years) and 21 years for females (95% CI: 19.5-23 years). These data suggest that a critical window for weight management intervention in preventing T2DM might exist before the age when BMI growth rate is naturally expected to decrease. Simulation showed that when using pairwise comparison of least-square means from categorical mixed models, smaller sample sizes tended to conclude a later age of divergence. In contrast, the point estimate of the divergence time is not biased by sample size when using the proposed BHPR method. BHPR is a powerful analytic tool to model long-term non-linear longitudinal outcomes, enabling the identification of the age at which risk factor trajectories diverge between groups of participants. The method is suitable for the analysis of unbalanced longitudinal data, with only a limited number of repeated measures per participants and where the time-related outcome is typically marked by transitional changes or by distinct phases of change over time.

Does clinical pretest probability influence image quality and diagnostic accuracy in dual-source coronary CT angiography?

PubMed

Thomas, Christoph; Brodoefel, Harald; Tsiflikas, Ilias; Bruckner, Friederike; Reimann, Anja; Ketelsen, Dominik; Drosch, Tanja; Claussen, Claus D; Kopp, Andreas; Heuschmid, Martin; Burgstahler, Christof

2010-02-01

To prospectively evaluate the influence of the clinical pretest probability assessed by the Morise score onto image quality and diagnostic accuracy in coronary dual-source computed tomography angiography (DSCTA). In 61 patients, DSCTA and invasive coronary angiography were performed. Subjective image quality and accuracy for stenosis detection (>50%) of DSCTA with invasive coronary angiography as gold standard were evaluated. The influence of pretest probability onto image quality and accuracy was assessed by logistic regression and chi-square testing. Correlations of image quality and accuracy with the Morise score were determined using linear regression. Thirty-eight patients were categorized into the high, 21 into the intermediate, and 2 into the low probability group. Accuracies for the detection of significant stenoses were 0.94, 0.97, and 1.00, respectively. Logistic regressions and chi-square tests showed statistically significant correlations between Morise score and image quality (P < .0001 and P < .001) and accuracy (P = .0049 and P = .027). Linear regression revealed a cutoff Morise score for a good image quality of 16 and a cutoff for a barely diagnostic image quality beyond the upper Morise scale. Pretest probability is a weak predictor of image quality and diagnostic accuracy in coronary DSCTA. A sufficient image quality for diagnostic images can be reached with all pretest probabilities. Therefore, coronary DSCTA might be suitable also for patients with a high pretest probability. Copyright 2010 AUR. Published by Elsevier Inc. All rights reserved.

The relationship between the target effective site concentration of rocuronium and the degree of recovery from neuromuscular blockade in elderly patients

PubMed Central

Fan, Xiaochong; Ma, Minyu; Li, Zhisong; Gong, Shengkai; Zhang, Wei; Wen, Yuanyuan

2015-01-01

Objective: To study the relationship between the target effective site concentration (Ce) of rocuronium and the degree of recovery from neuromuscular blockade in elderly patients. Methods: 50 elderly patients (ASA grade II) scheduled for selective surgical procedure under general anaesthesia were randomly divided into two groups, A and B, with 25 cases in each group. The Ce of rocuronium for intubation was 3 μg·ml-1 in both groups, and the Ce during operation were 0.8 and 1.0 μg·ml-1 in group A and B, respectively. When target controlled infusion of rocuronium was stopped, without the administration of reversal agents for neuromuscular blockade, the relationship between Ce and the first twitch height (T1) was studied by regression analysis. Results: There was a significant linear relationship between Ce and T1, and there was no statistical difference in regression coefficient and interception between group A and B (P>0.05). Conclusion: The degree of recovery from neuromuscular blockade could be judged by the target effective site concentration of rocuronium at the time of reversal from neuromuscular blockade in the elderly patients. PMID:26629159

Placenta previa: an outcome-based cohort study in a contemporary obstetric population.

PubMed

Lal, Ann K; Hibbard, Judith U

2015-08-01

The objective of the study is to characterize the maternal and neonatal morbidities of women with placenta previa. This retrospective group study used the Consortium on Safe Labor electronic database, including 12 clinical centers, and 19 hospitals. Patients with placenta previa noted at the time of delivery were included. Maternal and neonatal variables were compared to a control group of women undergoing cesarean delivery with no previa. Logistic regression and general linear regression were used for the analysis, with p < 0.05 significance. There were 19,069 patients in the study: 452 in the placenta previa group and 18,617 in the control group. Neonates born to mothers with placenta previa had lower gestational ages and birth weights. In univariate analysis only, these neonates were at increased risk of lower 5 min Apgar scores, neonatal intensive care unit admission, anemia, respiratory distress syndrome, mechanical ventilation, and intraventricular hemorrhage. There was no association of placenta previa with small for gestational age infants, congenital anomalies or death. As previously shown, women with placenta previa have significantly more maternal morbidities. Increased maternal morbidity was noted; however, only those neonatal morbidities associated with preterm delivery occurred in the placenta previa group.

Specialization Agreements in the Council for Mutual Economic Assistance

DTIC Science & Technology

1988-02-01

proportions to stabilize variance (S. Weisberg, Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134). If the dependent...27, 1986, p. 3. Weisberg, S., Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134. Wiles, P. J., Communist International

Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

DTIC Science & Technology

2006-08-14

63 4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz 69 4.4.7. Projected Scaling to...4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz In order to construct a comprehensive numerical algorithm capable of

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

EPA Science Inventory

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

Data Transformations for Inference with Linear Regression: Clarifications and Recommendations

ERIC Educational Resources Information Center

Pek, Jolynn; Wong, Octavia; Wong, C. M.

2017-01-01

Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to…

USING LINEAR AND POLYNOMIAL MODELS TO EXAMINE THE ENVIRONMENTAL STABILITY OF VIRUSES

EPA Science Inventory

The article presents the development of model equations for describing the fate of viral infectivity in environmental samples. Most of the models were based upon the use of a two-step linear regression approach. The first step employs regression of log base 10 transformed viral t...

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

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

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

TiO2 dye sensitized solar cell (DSSC): linear relationship of maximum power point and anthocyanin concentration

NASA Astrophysics Data System (ADS)

Ahmadian, Radin

2010-09-01

This study investigated the relationship of anthocyanin concentration from different organic fruit species and output voltage and current in a TiO2 dye-sensitized solar cell (DSSC) and hypothesized that fruits with greater anthocyanin concentration produce higher maximum power point (MPP) which would lead to higher current and voltage. Anthocyanin dye solution was made with crushing of a group of fresh fruits with different anthocyanin content in 2 mL of de-ionized water and filtration. Using these test fruit dyes, multiple DSSCs were assembled such that light enters through the TiO2 side of the cell. The full current-voltage (I-V) co-variations were measured using a 500 Ω potentiometer as a variable load. Point-by point current and voltage data pairs were measured at various incremental resistance values. The maximum power point (MPP) generated by the solar cell was defined as a dependent variable and the anthocyanin concentration in the fruit used in the DSSC as the independent variable. A regression model was used to investigate the linear relationship between study variables. Regression analysis showed a significant linear relationship between MPP and anthocyanin concentration with a p-value of 0.007. Fruits like blueberry and black raspberry with the highest anthocyanin content generated higher MPP. In a DSSC, a linear model may predict MPP based on the anthocyanin concentration. This model is the first step to find organic anthocyanin sources in the nature with the highest dye concentration to generate energy.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Naval Research Logistics Quarterly. Volume 28. Number 3,

DTIC Science & Technology

1981-09-01

denotes component-wise maximum. f has antone (isotone) differences on C x D if for cl < c2 and d, < d2, NAVAL RESEARCH LOGISTICS QUARTERLY VOL. 28...or negative correlations and linear or nonlinear regressions. Given are the mo- ments to order two and, for special cases, (he regression function and...data sets. We designate this bnb distribution as G - B - N(a, 0, v). The distribution admits only of positive correlation and linear regressions

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

NASA Astrophysics Data System (ADS)

Di, Nur Faraidah Muhammad; Satari, Siti Zanariah

2017-05-01

Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.

The effect of interactive multimedia on preoperative knowledge and postoperative recovery of patients undergoing laparoscopic cholecystectomy.

PubMed

Stergiopoulou, A; Birbas, K; Katostaras, T; Mantas, J

2007-01-01

Aim of this study is the evaluation of the impact of a multimedia CD (MCD) on preoperative anxiety and postoperative recovery of patients undergoing elective laparoscopic cholecystectomy (LC). Sixty consecutive candidates for elective LC were randomly assigned to four groups. Group A included 15 patients preoperatively informed regarding LC through the MCD presented by Registered Nurse (RN). Patients in group B (n = 15) were informed through a leaflet. Patients in group C (n = 15) were informed verbally from a RN. Finally, the control Group D included 15 patients informed conventionally by the attending surgeon and anesthesiologist, as every other patient included in groups A, B, and C. Preoperative assessment of knowledge about LC was performed after each informative session through a questionnaire. Evaluation of preoperative anxiety was conducted using APAIS scale. Postoperative pain and nausea scores were measured using an NRS scale, 16 hours after the patient had returned to the ward. Statistical processing of the results (single linear regression) showed that patients in groups A, B, and C achieved a higher knowledge score, less preoperative anxiety score and less postoperative pain and nausea, compared to Group D. In multiple regression analysis, group A had a higher knowledge score compared to the four groups (p < 0.001 r(2) = 0.41). Informative sessions using MCD is an effective means of improving patient's preoperative knowledge, especially in day-surgery cases, like LC.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

Optimization of isotherm models for pesticide sorption on biopolymer-nanoclay composite by error analysis.

PubMed

Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M

2017-04-01

A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Conventional multi-slice computed tomography (CT) and cone-beam CT (CBCT) for computer-aided implant placement. Part II: reliability of mucosa-supported stereolithographic guides.

PubMed

Arisan, Volkan; Karabuda, Zihni Cüneyt; Pişkin, Bülent; Özdemir, Tayfun

2013-12-01

Deviations of implants that were placed by conventional computed tomography (CT)- or cone beam CT (CBCT)-derived mucosa-supported stereolithographic (SLA) surgical guides were analyzed in this study. Eleven patients were randomly scanned by a multi-slice CT (CT group) or a CBCT scanner (CBCT group). A total of 108 implants were planned on the software and placed using SLA guides. A new CT or CBCT scan was obtained and merged with the planning data to identify the deviations between the planned and placed implants. Results were analyzed by Mann-Whitney U test and multiple regressions (p < .05). Mean angular and linear deviations in the CT group were 3.30° (SD 0.36), and 0.75 (SD 0.32) and 0.80 mm (SD 0.35) at the implant shoulder and tip, respectively. In the CBCT group, mean angular and linear deviations were 3.47° (SD 0.37), and 0.81 (SD 0.32) and 0.87 mm (SD 0.32) at the implant shoulder and tip, respectively. No statistically significant differences were detected between the CT and CBCT groups (p = .169 and p = .551, p = .113 for angular and linear deviations, respectively). Implant placement via CT- or CBCT-derived mucosa-supported SLA guides yielded similar deviation values. Results should be confirmed on alternative CBCT scanners. © 2012 Wiley Periodicals, Inc.

Examining Differential Resilience Mechanisms by Comparing 'Tipping Points' of the Effects of Neighborhood Conditions on Anxiety by Race/Ethnicity.

PubMed

Coman, Emil Nicolae; Wu, Helen Zhao

2018-02-20

Exposure to adverse environmental and social conditions affects physical and mental health through complex mechanisms. Different racial/ethnic (R/E) groups may be more or less vulnerable to the same conditions, and the resilience mechanisms that can protect them likely operate differently in each population. We investigate how adverse neighborhood conditions (neighborhood disorder, NDis) differentially impact mental health (anxiety, Anx) in a sample of white and Black (African American) young women from Southeast Texas, USA. We illustrate a simple yet underutilized segmented regression model where linearity is relaxed to allow for a shift in the strength of the effect with the levels of the predictor. We compare how these effects change within R/E groups with the level of the predictor, but also how the "tipping points," where the effects change in strength, may differ by R/E. We find with classic linear regression that neighborhood disorder adversely affects Black women's anxiety, while in white women the effect seems negligible. Segmented regressions show that the Ndis → Anx effects in both groups of women appear to shift at similar levels, about one-fifth of a standard deviation below the mean of NDis, but the effect for Black women appears to start out as negative, then shifts in sign, i.e., to increase anxiety, while for white women, the opposite pattern emerges. Our findings can aid in devising better strategies for reducing health disparities that take into account different coping or resilience mechanisms operating differentially at distinct levels of adversity. We recommend that researchers investigate when adversity becomes exceedingly harmful and whether this happens differentially in distinct populations, so that intervention policies can be planned to reverse conditions that are more amenable to change, in effect pushing back the overall social risk factors below such tipping points.

Daily commuting to work is not associated with variables of health.

PubMed

Mauss, Daniel; Jarczok, Marc N; Fischer, Joachim E

2016-01-01

Commuting to work is thought to have a negative impact on employee health. We tested the association of work commute and different variables of health in German industrial employees. Self-rated variables of an industrial cohort (n = 3805; 78.9 % male) including absenteeism, presenteeism and indices reflecting stress and well-being were assessed by a questionnaire. Fasting blood samples, heart-rate variability and anthropometric data were collected. Commuting was grouped into one of four categories: 0-19.9, 20-44.9, 45-59.9, ≥60 min travelling one way to work. Bivariate associations between commuting and all variables under study were calculated. Linear regression models tested this association further, controlling for potential confounders. Commuting was positively correlated with waist circumference and inversely with triglycerides. These associations did not remain statistically significant in linear regression models controlling for age, gender, marital status, and shiftwork. No other association with variables of physical, psychological, or mental health and well-being could be found. The results indicate that commuting to work has no significant impact on well-being and health of German industrial employees.

Mental ability and psychological work performance in Chinese workers.

PubMed

Zhong, Fei; Yano, Eiji; Lan, Yajia; Wang, Mianzhen; Wang, Zhiming; Wang, Xiaorong

2006-10-01

This study was to explore the relationship among mental ability, occupational stress, and psychological work performance in Chinese workers, and to identify relevant modifiers of mental ability and psychological work performance. Psychological Stress Intensity (PSI), psychological work performance, and mental ability (Mental Function Index, MFI) were determined among 485 Chinese workers (aged 33 to 62 yr, 65% of men) with varied work occupations. Occupational Stress Questionnaire (OSQ) and mental ability with 3 tests (including immediate memory, digit span, and cipher decoding) were used. The relationship between mental ability and psychological work performance was analyzed with multiple linear regression approach. PSI, MFI, or psychological work performance were significantly different among different work types and educational level groups (p<0.01). Multiple linear regression analysis showed that MFI was significantly related to gender, age, educational level, and work type. Higher MFI and lower PSI predicted a better psychological work performance, even after adjusted for gender, age, educational level, and work type. The study suggests that occupational stress and low mental ability are important predictors for poor psychological work performance, which is modified by both gender and educational level.

Acculturation and health-related quality of life: results from the German National Cohort migrant feasibility study.

PubMed

Brand, Tilman; Samkange-Zeeb, Florence; Ellert, Ute; Keil, Thomas; Krist, Lilian; Dragano, Nico; Jöckel, Karl-Heinz; Razum, Oliver; Reiss, Katharina; Greiser, Karin Halina; Zimmermann, Heiko; Becher, Heiko; Zeeb, Hajo

2017-06-01

We assessed the association between acculturation and health-related quality of life (HRQoL) among persons with a Turkish migrant background in Germany. 1226 adults of Turkish origin were recruited in four German cities. Acculturation was assessed using the Frankfurt Acculturation Scale resulting in four groups (integration, assimilation, separation and marginalization). Short Form-8 physical and mental components were used to assess the HRQoL. Associations were analysed with linear regression models. Of the respondents, 20% were classified as integrated, 29% assimilated, 29% separated and 19% as marginalized. Separation was associated with poorer physical and mental health (linear regression coefficient (RC) = -2.3, 95% CI -3.9 to -0.8 and RC = -2.4, 95% CI -4.4 to -0.5, respectively; reference: integration). Marginalization was associated with poorer mental health in descendants of migrants (RC = -6.4, 95% CI -12.0 to -0.8; reference: integration). Separation and marginalization are associated with a poorer HRQoL. Policies should support the integration of migrants, and health promotion interventions should target separated and marginalized migrants to improve their HRQoL.

Using Parametric Cost Models to Estimate Engineering and Installation Costs of Selected Electronic Communications Systems

DTIC Science & Technology

1994-09-01

Institute of Technology, Wright- Patterson AFB OH, January 1994. 4. Neter, John and others. Applied Linear Regression Models. Boston: Irwin, 1989. 5...Technology, Wright-Patterson AFB OH 5 April 1994. 29. Neter, John and others. Applied Linear Regression Models. Boston: Irwin, 1989. 30. Office of

An Evaluation of the Automated Cost Estimating Integrated Tools (ACEIT) System

DTIC Science & Technology

1989-09-01

residual and it is described as the residual divided by its standard deviation (13:App A,17). Neter, Wasserman, and Kutner, in Applied Linear Regression Models...others. Applied Linear Regression Models. Homewood IL: Irwin, 1983. 19. Raduchel, William J. "A Professional’s Perspective on User-Friendliness," Byte

A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

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

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

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

Fitting program for linear regressions according to Mahon (1996)

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

Trappitsch, Reto G.

2018-01-09

This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

Revisiting the Scale-Invariant, Two-Dimensional Linear Regression Method

ERIC Educational Resources Information Center

Patzer, A. Beate C.; Bauer, Hans; Chang, Christian; Bolte, Jan; Su¨lzle, Detlev

2018-01-01

The scale-invariant way to analyze two-dimensional experimental and theoretical data with statistical errors in both the independent and dependent variables is revisited by using what we call the triangular linear regression method. This is compared to the standard least-squares fit approach by applying it to typical simple sets of example data…

An Introduction to Graphical and Mathematical Methods for Detecting Heteroscedasticity in Linear Regression.

ERIC Educational Resources Information Center

Thompson, Russel L.

Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are…

On the null distribution of Bayes factors in linear regression

USDA-ARS?s Scientific Manuscript database

We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

Common pitfalls in statistical analysis: Linear regression analysis

PubMed Central

Aggarwal, Rakesh; Ranganathan, Priya

2017-01-01

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis. PMID:28447022

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling of time trends and interactions in vital rates using restricted regression splines.

PubMed

Heuer, C

1997-03-01

For the analysis of time trends in incidence and mortality rates, the age-period-cohort (apc) model has became a widely accepted method. The considered data are arranged in a two-way table by age group and calendar period, which are mostly subdivided into 5- or 10-year intervals. The disadvantage of this approach is the loss of information by data aggregation and the problems of estimating interactions in the two-way layout without replications. In this article we show how splines can be useful when yearly data, i.e., 1-year age groups and 1-year periods, are given. The estimated spline curves are still smooth and represent yearly changes in the time trends. Further, it is straightforward to include interaction terms by the tensor product of the spline functions. If the data are given in a nonrectangular table, e.g., 5-year age groups and 1-year periods, the period and cohort variables can be parameterized by splines, while the age variable is parameterized as fixed effect levels, which leads to a semiparametric apc model. An important methodological issue in developing the nonparametric and semiparametric models is stability of the estimated spline curve at the boundaries. Here cubic regression splines will be used, which are constrained to be linear in the tails. Another point of importance is the nonidentifiability problem due to the linear dependency of the three time variables. This will be handled by decomposing the basis of each spline by orthogonal projection into constant, linear, and nonlinear terms, as suggested by Holford (1983, Biometrics 39, 311-324) for the traditional apc model. The advantage of using splines for yearly data compared to the traditional approach for aggregated data is the more accurate curve estimation for the nonlinear trend changes and the simple way of modeling interactions between the time variables. The method will be demonstrated with hypothetical data as well as with cancer mortality data.

Shift work schedule and night work load: Effects on body mass index - a four-year longitudinal study.

PubMed

Buchvold, Hogne Vikanes; Pallesen, Ståle; Waage, Siri; Bjorvatn, Bjørn

2018-05-01

Objectives The aim of this study was to investigate changes in body mass index (BMI) between different work schedules and different average number of yearly night shifts over a four-year follow-up period. Methods A prospective study of Norwegian nurses (N=2965) with different work schedules was conducted: day only, two-shift rotation (day and evening shifts), three-shift rotation (day, evening and night shifts), night only, those who changed towards night shifts, and those who changed away from schedules containing night shifts. Paired student's t-tests were used to evaluate within subgroup changes in BMI. Multiple linear regression analysis was used to evaluate between groups effects on BMI when adjusting for BMI at baseline, sex, age, marital status, children living at home, and years since graduation. The same regression model was used to evaluate the effect of average number of yearly night shifts on BMI change. Results We found that night workers [mean difference (MD) 1.30 (95% CI 0.70-1.90)], two shift workers [MD 0.48 (95% CI 0.20-0.75)], three shift workers [MD 0.46 (95% CI 0.30-0.62)], and those who changed work schedule away from [MD 0.57 (95% CI 0.17-0.84)] or towards night work [MD 0.63 (95% CI 0.20-1.05)] all had significant BMI gain (P<0.01) during the follow-up period. However, day workers had a non-significant BMI gain. Using adjusted multiple linear regressions, we found that night workers had significantly larger BMI gain compared to day workers [B=0.89 (95% CI 0.06-1.72), P<0.05]. We did not find any significant association between average number of yearly night shifts and BMI change using our multiple linear regression model. Conclusions After adjusting for possible confounders, we found that BMI increased significantly more among night workers compared to day workers.

Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

NASA Astrophysics Data System (ADS)

Wu, Cheng; Zhen Yu, Jian

2018-03-01

Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

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

PubMed

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

2006-08-01

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

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

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

RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

IQ is an independent predictor of glycated haemoglobin level in young and middle-aged adults with intellectual disability.

PubMed

Yano, T; Miki, T; Itoh, T; Ohnishi, H; Asari, M; Chihiro, S; Yamamoto, A; Aotsuka, K; Kawakami, N; Ichikawa, J; Hirota, Y; Miura, T

2015-01-01

Here we examined whether intellectual disability is independently associated with hyperglycaemia. We recruited 233 consecutive young and middle-aged adults with intellectual disability. After exclusion of subjects on medication for metabolic diseases or with severe intellectual disability (IQ < 35), 121 subjects were divided by IQ into a group with moderate intellectual disability (35 ≤ IQ ≤ 50), a mild intellectual disability group (51 ≤ IQ ≤ 70) and a borderline group (IQ > 70). HbA1c level was higher in subjects with moderate intellectual disability (42 ± 9 mmol/mol; 6.0 ± 0.8%) than those in the borderline group (36 ± 4 mmol/mol; 5.5 ± 0.3%) and mild intellectual disability group (37 ± 5 mmol/mol; 5.5 ± 0.5%) groups. HbA1c level was correlated with age, BMI, blood pressure, serum triglycerides and IQ in simple linear regression analysis. Multiple regression analysis indicated that IQ, age, BMI and diastolic blood pressure were independent explanatory factors of HbA1c level. An unfavourable effect of intellectual disability on lifestyle and untoward effect of hyperglycaemia on cognitive function may underlie the association of low IQ with hyperglycaemia. © 2014 The Authors. Diabetic Medicine © 2014 Diabetes UK.

Preoperative Fasting C-Peptide Predicts Type 2 Diabetes Mellitus Remission in Low-BMI Chinese Patients After Roux-en-Y Gastric Bypass.

PubMed

Zhao, Lei; Li, Weizheng; Su, Zhihong; Liu, Yong; Zhu, Liyong; Zhu, Shaihong

2018-05-29

This study investigated the role of preoperative fasting C-peptide (FCP) levels in predicting diabetic outcomes in low-BMI Chinese patients following Roux-en-Y gastric bypass (RYGB) by comparing the metabolic outcomes of patients with FCP > 1 ng/ml versus FCP ≤ 1 ng/ml. The study sample included 78 type 2 diabetes mellitus patients with an average BMI < 30 kg/m 2 at baseline. Patients' parameters were analyzed before and after surgery, with a 2-year follow-up. A univariate logistic regression analysis and multivariate analysis of variance between the remission and improvement group were performed to determine factors that were associated with type 2 diabetes remission after RYGB. Linear correlation analyses between FCP and metabolic parameters were performed. Patients were divided into two groups: FCP > 1 ng/ml and FCP ≤ 1 ng/ml, with measured parameters compared between the groups. Patients' fasting plasma glucose, 2-h postprandial plasma glucose, FCP, and HbA1c improved significantly after surgery (p < 0.05). Factors associated with type 2 diabetes remission were BMI, 2hINS, and FCP at the univariate logistic regression analysis (p < 0.05). Multivariate logistic regression analysis was performed then showed the results were more related to FCP (OR = 2.39). FCP showed a significant linear correlation with fasting insulin and BMI (p < 0.05). There was a significant difference in remission rate between the FCP > 1 ng/ml and FCP ≤ 1 ng/ml groups (p = 0.01). The parameters of patients with FCP > 1 ng/ml, including BMI, plasma glucose, HbA1c, and plasma insulin, decreased markedly after surgery (p < 0.05). FCP level is a significant predictor of diabetes outcomes after RYGB in low-BMI Chinese patients. An FCP level of 1 ng/ml may be a useful threshold for predicting surgical prognosis, with FCP > 1 ng/ml predicting better clinical outcomes following RYGB.

Everolimus with reduced-dose cyclosporine versus full-dose cyclosporine and mycophenolate in de novo renal transplant patients: a 2-year single-center experience.

PubMed

Santos, S M; Carlos, C M; Cabanayan-Casasola, C B; Danguilan, R A

2012-01-01

Although calcineurin inhibitors (CNIs) has improved short-term graft survival, long-term function remains a challenge. CNIs have been implicated in the development of chronic allograft failure. Low-dose cyclosporine with everolimus may mitigate CNI nephrotoxicity and prolong graft survival. We compared the efficacy and safety of de novo everolimus with low-dose cyclosporine and prednisone versus cyclosporine, mycophenolate, and prednisone among kidney transplant patients up to 24 months after transplantation. Kidney transplant patients given low-dose cyclosporine, everolimus, and prednisone were compared with patients given cyclosporine, mycophenolate, and prednisone from December 2006 to December 2008. All had living donors, panel reactive antibody <15%, and follow-up for 2 years after transplantation. Continuous variables using mean and standard deviation, t test and test for proportions were used to determine significant differences between the baseline characteristics of the 2 treatment groups. Generalized linear regression and logistic regression were used to measure the effect of treatment on outcomes. Demographic characteristics were similar in both groups except for age, length of time awaiting kidney transplantation, type of renal replacement therapy, follow-up time, sex distribution, and number of HLA mismatches. These independent variables were used in the generalized linear regression model. There was no significant difference between the everolimus and mycophenolate groups up to 2 years in mean serum creatinine (1.2 mg/dL vs 1.4 mg/dL-, respectively P ≥ .05), acute rejection (12 months: 20% vs 31%; 24 months: 31% vs 40%; P ≥ .05), patient survival (98%), and graft survival (100%). Likewise, there were no significant differences in surgical, infectious, metabolic, and gastrointestinal side effects between the 2 groups. Everolimus with low-dose cyclosporine and prednisone in de novo kidney transplant recipients was similar in efficacy and safety to cyclosporine, mycophenolate, and prednisone. Longer follow-up is needed to see whether everolimus with low-dose cyclosporine will result in improved kidney function. Copyright © 2012 Elsevier Inc. All rights reserved.

Random regression models using different functions to model milk flow in dairy cows.

PubMed

Laureano, M M M; Bignardi, A B; El Faro, L; Cardoso, V L; Tonhati, H; Albuquerque, L G

2014-09-12

We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.

Circulating interleukin-8 and tumor necrosis factor-α are associated with hot flashes in healthy postmenopausal women.

PubMed

Huang, Wan-Yu; Hsin, I-Lun; Chen, Dar-Ren; Chang, Chia-Chu; Kor, Chew-Teng; Chen, Ting-Yu; Wu, Hung-Ming

2017-01-01

Hot flashes have been postulated to be linked to systemic inflammation. This study aimed to investigate the relationship between hot flashes, pro-inflammatory factors, and leukocytes in healthy, non-obese postmenopausal women. In this cross-sectional study, a total of 202 women aged 45-60 years were stratified into one of four groups according to their hot-flash status: never experienced hot flashes (Group N), mild hot flashes (Group m), moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, leukocytes, and fasting plasma levels of nine circulating cytokines/chemokines measured by using multiplex assays. Multiple linear regression analysis was used to evaluate the associations of hot flashes with these pro-inflammatory factors. The study was performed in a hospital medical center. The mean values of leukocyte number were not different between these four groups. The hot flash status had a positive tendency toward increased levels of circulating IL-6 (P-trend = 0.049), IL-8 (P-trend < 0.001), TNF-α (P-trend = 0.008), and MIP1β (P-trend = 0.04). Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with IL-8 (P-trend < 0.001) and TNFα (P-trend = 0.007) among these nine cytokines/chemokines after adjustment for age, menopausal duration, BMI and FSH. Multivariate analysis further revealed that severe hot flashes were strongly associated with a higher IL-8 (% difference, 37.19%; 95% confidence interval, 14.98,63.69; P < 0.001) and TNFα (51.27%; 6.64,114.57; P < 0.05). The present study provides evidence that hot flashes are associated with circulating IL-8 and TNF-α in healthy postmenopausal women. It suggests that hot flashes might be related to low-grade systemic inflammation.

Circulating interleukin-8 and tumor necrosis factor-α are associated with hot flashes in healthy postmenopausal women

PubMed Central

Huang, Wan-Yu; Hsin, I-Lun; Chen, Dar-Ren; Chang, Chia-Chu; Kor, Chew-Teng; Chen, Ting-Yu

2017-01-01

Introduction Hot flashes have been postulated to be linked to systemic inflammation. This study aimed to investigate the relationship between hot flashes, pro-inflammatory factors, and leukocytes in healthy, non-obese postmenopausal women. Participants and design In this cross-sectional study, a total of 202 women aged 45–60 years were stratified into one of four groups according to their hot-flash status: never experienced hot flashes (Group N), mild hot flashes (Group m), moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, leukocytes, and fasting plasma levels of nine circulating cytokines/chemokines measured by using multiplex assays. Multiple linear regression analysis was used to evaluate the associations of hot flashes with these pro-inflammatory factors. Settings The study was performed in a hospital medical center. Results The mean values of leukocyte number were not different between these four groups. The hot flash status had a positive tendency toward increased levels of circulating IL-6 (P-trend = 0.049), IL-8 (P-trend < 0.001), TNF-α (P-trend = 0.008), and MIP1β (P-trend = 0.04). Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with IL-8 (P-trend < 0.001) and TNFα (P-trend = 0.007) among these nine cytokines/chemokines after adjustment for age, menopausal duration, BMI and FSH. Multivariate analysis further revealed that severe hot flashes were strongly associated with a higher IL-8 (% difference, 37.19%; 95% confidence interval, 14.98,63.69; P < 0.001) and TNFα (51.27%; 6.64,114.57; P < 0.05). Conclusion The present study provides evidence that hot flashes are associated with circulating IL-8 and TNF-α in healthy postmenopausal women. It suggests that hot flashes might be related to low-grade systemic inflammation. PMID:28846735

The impact of emphysema on dosimetric parameters for stereotactic body radiotherapy of the lung

PubMed Central

Ochiai, Satoru; Nomoto, Yoshihito; Yamashita, Yasufumi; Inoue, Tomoki; Murashima, Shuuichi; Hasegawa, Daisuke; Kurita, Yoshie; Watanabe, Yui; Toyomasu, Yutaka; Kawamura, Tomoko; Takada, Akinori; Noriko; Kobayashi, Shigeki; Sakuma, Hajime

2016-01-01

The purpose of this study was to evaluate the impact of emphysematous changes in lung on dosimetric parameters in stereotactic body radiation therapy (SBRT) for lung tumor. A total of 72 treatment plans were reviewed, and dosimetric factors [including homogeneity index (HI) and conformity index (CI)] were evaluated. Emphysematous changes in lung were observed in 43 patients (60%). Patients were divided into three groups according to the severity of emphysema: no emphysema (n = 29), mild emphysema (n = 22) and moderate to severe emphysema groups (n = 21). The HI (P < 0.001) and the CI (P = 0.029) were significantly different in accordance with the severity of emphysema in one-way analysis of variance (ANOVA). The HI value was significantly higher in the moderate to severe emphysema group compared with in the no emphysema (Tukey, P < 0.001) and mild emphysema groups (P = 0.002). The CI value was significantly higher in the moderate to severe emphysema group compared with in the no emphysema group (P = 0.044). In multiple linear regression analysis, the severity of emphysema (P < 0.001) and the mean material density of the lung within the PTV (P < 0.001) were significant factors for HI, and the mean density of the lung within the PTV (P = 0.005) was the only significant factor for CI. The mean density of the lung within the PTV was significantly different in accordance with the severity of emphysema (one-way ANOVA, P = 0.008) and the severity of emphysema (P < 0.001) was one of the significant factors for the density of the lung within the PTV in multiple linear regression analysis. Our results suggest that emphysematous changes in the lung significantly impact on several dosimetric parameters in SBRT, and they should be carefully evaluated before treatment planning. PMID:27380802

Generalized Linear Mixed Model Analysis of Urban-Rural Differences in Social and Behavioral Factors for Colorectal Cancer Screening

PubMed Central

Wang, Ke-Sheng; Liu, Xuefeng; Ategbole, Muyiwa; Xie, Xin; Liu, Ying; Xu, Chun; Xie, Changchun; Sha, Zhanxin

2017-01-01

Objective: Screening for colorectal cancer (CRC) can reduce disease incidence, morbidity, and mortality. However, few studies have investigated the urban-rural differences in social and behavioral factors influencing CRC screening. The objective of the study was to investigate the potential factors across urban-rural groups on the usage of CRC screening. Methods: A total of 38,505 adults (aged ≥40 years) were selected from the 2009 California Health Interview Survey (CHIS) data - the latest CHIS data on CRC screening. The weighted generalized linear mixed-model (WGLIMM) was used to deal with this hierarchical structure data. Weighted simple and multiple mixed logistic regression analyses in SAS ver. 9.4 were used to obtain the odds ratios (ORs) and their 95% confidence intervals (CIs). Results: The overall prevalence of CRC screening was 48.1% while the prevalence in four residence groups - urban, second city, suburban, and town/rural, were 45.8%, 46.9%, 53.7% and 50.1%, respectively. The results of WGLIMM analysis showed that there was residence effect (p<0.0001) and residence groups had significant interactions with gender, age group, education level, and employment status (p<0.05). Multiple logistic regression analysis revealed that age, race, marital status, education level, employment stats, binge drinking, and smoking status were associated with CRC screening (p<0.05). Stratified by residence regions, age and poverty level showed associations with CRC screening in all four residence groups. Education level was positively associated with CRC screening in second city and suburban. Infrequent binge drinking was associated with CRC screening in urban and suburban; while current smoking was a protective factor in urban and town/rural groups. Conclusions: Mixed models are useful to deal with the clustered survey data. Social factors and behavioral factors (binge drinking and smoking) were associated with CRC screening and the associations were affected by living areas such as urban and rural regions. PMID:28952708

Generalized Linear Mixed Model Analysis of Urban-Rural Differences in Social and Behavioral Factors for Colorectal Cancer Screening

PubMed

Wang, Ke-Sheng; Liu, Xuefeng; Ategbole, Muyiwa; Xie, Xin; Liu, Ying; Xu, Chun; Xie, Changchun; Sha, Zhanxin

2017-09-27

Objective: Screening for colorectal cancer (CRC) can reduce disease incidence, morbidity, and mortality. However, few studies have investigated the urban-rural differences in social and behavioral factors influencing CRC screening. The objective of the study was to investigate the potential factors across urban-rural groups on the usage of CRC screening. Methods: A total of 38,505 adults (aged ≥40 years) were selected from the 2009 California Health Interview Survey (CHIS) data - the latest CHIS data on CRC screening. The weighted generalized linear mixed-model (WGLIMM) was used to deal with this hierarchical structure data. Weighted simple and multiple mixed logistic regression analyses in SAS ver. 9.4 were used to obtain the odds ratios (ORs) and their 95% confidence intervals (CIs). Results: The overall prevalence of CRC screening was 48.1% while the prevalence in four residence groups - urban, second city, suburban, and town/rural, were 45.8%, 46.9%, 53.7% and 50.1%, respectively. The results of WGLIMM analysis showed that there was residence effect (p<0.0001) and residence groups had significant interactions with gender, age group, education level, and employment status (p<0.05). Multiple logistic regression analysis revealed that age, race, marital status, education level, employment stats, binge drinking, and smoking status were associated with CRC screening (p<0.05). Stratified by residence regions, age and poverty level showed associations with CRC screening in all four residence groups. Education level was positively associated with CRC screening in second city and suburban. Infrequent binge drinking was associated with CRC screening in urban and suburban; while current smoking was a protective factor in urban and town/rural groups. Conclusions: Mixed models are useful to deal with the clustered survey data. Social factors and behavioral factors (binge drinking and smoking) were associated with CRC screening and the associations were affected by living areas such as urban and rural regions. Creative Commons Attribution License

Post-processing through linear regression

NASA Astrophysics Data System (ADS)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Determination of glomerular filtration rate (GFR) from fractional renal accumulation of iodinated contrast material: a convenient and rapid single-kidney CT-GFR technique.

PubMed

Yuan, XiaoDong; Tang, Wei; Shi, WenWei; Yu, Libao; Zhang, Jing; Yuan, Qing; You, Shan; Wu, Ning; Ao, Guokun; Ma, Tingting

2018-07-01

To develop a convenient and rapid single-kidney CT-GFR technique. One hundred and twelve patients referred for multiphasic renal CT and 99mTc-DTPA renal dynamic imaging Gates-GFR measurement were prospectively included and randomly divided into two groups of 56 patients each: the training group and the validation group. On the basis of the nephrographic phase images, the fractional renal accumulation (FRA) was calculated and correlated with the Gates-GFR in the training group. From this correlation a formula was derived for single-kidney CT-GFR calculation, which was validated by a paired t test and linear regression analysis with the single-kidney Gates-GFR in the validation group. In the training group, the FRA (x-axis) correlated well (r = 0.95, p < 0.001) with single-kidney Gates-GFR (y-axis), producing a regression equation of y = 1665x + 1.5 for single-kidney CT-GFR calculation. In the validation group, the difference between the methods of single-kidney GFR measurements was 0.38 ± 5.57 mL/min (p = 0.471); the regression line is identical to the diagonal (intercept = 0 and slope = 1) (p = 0.727 and p = 0.473, respectively), with a standard deviation of residuals of 5.56 mL/min. A convenient and rapid single-kidney CT-GFR technique was presented and validated in this investigation. • The new CT-GFR method takes about 2.5 min of patient time. • The CT-GFR method demonstrated identical results to the Gates-GFR method. • The CT-GFR method is based on the fractional renal accumulation of iodinated CM. • The CT-GFR method is achieved without additional radiation dose to the patient.

Structure-function relationships using spectral-domain optical coherence tomography: comparison with scanning laser polarimetry.

PubMed

Aptel, Florent; Sayous, Romain; Fortoul, Vincent; Beccat, Sylvain; Denis, Philippe

2010-12-01

To evaluate and compare the regional relationships between visual field sensitivity and retinal nerve fiber layer (RNFL) thickness as measured by spectral-domain optical coherence tomography (OCT) and scanning laser polarimetry. Prospective cross-sectional study. One hundred and twenty eyes of 120 patients (40 with healthy eyes, 40 with suspected glaucoma, and 40 with glaucoma) were tested on Cirrus-OCT, GDx VCC, and standard automated perimetry. Raw data on RNFL thickness were extracted for 256 peripapillary sectors of 1.40625 degrees each for the OCT measurement ellipse and 64 peripapillary sectors of 5.625 degrees each for the GDx VCC measurement ellipse. Correlations between peripapillary RNFL thickness in 6 sectors and visual field sensitivity in the 6 corresponding areas were evaluated using linear and logarithmic regression analysis. Receiver operating curve areas were calculated for each instrument. With spectral-domain OCT, the correlations (r(2)) between RNFL thickness and visual field sensitivity ranged from 0.082 (nasal RNFL and corresponding visual field area, linear regression) to 0.726 (supratemporal RNFL and corresponding visual field area, logarithmic regression). By comparison, with GDx-VCC, the correlations ranged from 0.062 (temporal RNFL and corresponding visual field area, linear regression) to 0.362 (supratemporal RNFL and corresponding visual field area, logarithmic regression). In pairwise comparisons, these structure-function correlations were generally stronger with spectral-domain OCT than with GDx VCC and with logarithmic regression than with linear regression. The largest areas under the receiver operating curve were seen for OCT superior thickness (0.963 ± 0.022; P < .001) in eyes with glaucoma and for OCT average thickness (0.888 ± 0.072; P < .001) in eyes with suspected glaucoma. The structure-function relationship was significantly stronger with spectral-domain OCT than with scanning laser polarimetry, and was better expressed logarithmically than linearly. Measurements with these 2 instruments should not be considered to be interchangeable. Copyright © 2010 Elsevier Inc. All rights reserved.

E-cigarette Dual Users, Exclusive Users and Perceptions of Tobacco Products.

PubMed

Cooper, Maria; Case, Kathleen R; Loukas, Alexandra; Creamer, Melisa R; Perry, Cheryl L

2016-01-01

We examined differences in the characteristics of youth non-users, cigarette-only, e-cigarette-only, and dual e-cigarette and cigarette users. Using weighted, representative data, logistic regression analyses were conducted to examine differences in demographic characteristics and tobacco use behaviors across tobacco usage groups. Multiple linear regression analyses were conducted to examine differences in harm perceptions of various tobacco products and perceived peer use of e-cigarettes by tobacco usage group. Compared to non-users, dual users were more likely to be white, male, and high school students. Dual users had significantly higher prevalence of current use of all products (except hookah) than e-cigarette-only users, and higher prevalence of current use of snus and hookah than the cigarette-only group. Dual users had significantly lower harm perceptions for all tobacco products except for e-cigarettes and hookah as compared to e-cigarette-only users. Dual users reported higher peer use of cigarettes as compared to both exclusive user groups. Findings highlight dual users' higher prevalence of use of most other tobacco products, their lower harm perceptions of most tobacco products compared to e-cigarette-only users, and their higher perceived peer use of cigarettes compared to exclusive users.

Periodontal Health in Women With Early-Stage Postmenopausal Breast Cancer Newly on Aromatase Inhibitors: A Pilot Study.

PubMed

Taichman, L Susan; Inglehart, Marita R; Giannobile, William V; Braun, Thomas; Kolenic, Giselle; Van Poznak, Catherine

2015-07-01

Aromatase inhibitor (AI) use results in low estrogen levels, which in turn affect bone mineral density (BMD). Periodontitis, alveolar bone loss, and tooth loss are associated with low BMD. The goal of this study is to assess the prevalence of periodontitis and perceived oral health and evaluate salivary biomarkers in postmenopausal women who are survivors of early-stage (I to IIIA) breast cancer (BCa) and receive adjuvant AI therapy. Participants included 58 postmenopausal women: 29 with BCa on AIs and 29 controls without BCa diagnoses. Baseline periodontal status was assessed with: 1) periodontal probing depth (PD); 2) bleeding on probing (BOP); and 3) attachment loss (AL). Demographic and dental utilization information was gathered by questionnaire. Linear regression modeling was used to analyze the outcomes. No differences were found in mean PD or number of teeth. The AI group had significantly more sites with BOP (27.8 versus 16.7; P = 0.02), higher worst-site AL (5.2 versus 4.0 mm; P <0.01), and more sites with dental calculus (18.2 versus 6.4; P <0.001) than controls. Linear regression adjusted for income, tobacco use, dental insurance, and previous radiation and chemotherapy exposure demonstrated that AI use increased AL by >2 mm (95% confidence interval, 0.46 to 3.92). Median salivary osteocalcin and tumor necrosis factor-α levels were significantly higher in the AI group than the control group. This first investigation of the periodontal status of women initiating adjuvant AI therapy identifies this population as having an increased risk for periodontitis.

Leukocyte Telomere Length in HIV-Infected and HIV-Exposed Uninfected Children: Shorter Telomeres with Uncontrolled HIV Viremia

PubMed Central

Côté, Hélène C. F.; Soudeyns, Hugo; Thorne, Anona; Alimenti, Ariane; Lamarre, Valérie; Maan, Evelyn J.; Sattha, Beheroze; Singer, Joel; Lapointe, Normand; Money, Deborah M.; Forbes, John

2012-01-01

Objectives Nucleoside reverse transcriptase inhibitors (NRTIs) used in HIV antiretroviral therapy can inhibit human telomerase reverse transcriptase. We therefore investigated whether in utero or childhood exposure to NRTIs affects leukocyte telomere length (LTL), a marker of cellular aging. Methods In this cross-sectional CARMA cohort study, we investigated factors associated with LTL in HIV -1-infected (HIV+) children (n = 94), HIV-1-exposed uninfected (HEU) children who were exposed to antiretroviral therapy (ART) perinatally (n = 177), and HIV-unexposed uninfected (HIV−) control children (n = 104) aged 0–19 years. Univariate followed by multivariate linear regression models were used to examine relationships of explanatory variables with LTL for: a) all subjects, b) HIV+/HEU children only, and c) HIV+ children only. Results After adjusting for age and gender, there was no difference in LTL between the 3 groups, when considering children of all ages together. In multivariate models, older age and male gender were associated with shorter LTL. For the HIV+ group alone, having a detectable HIV viral load was also strongly associated with shorter LTL (p = 0.007). Conclusions In this large study, group rates of LTL attrition were similar for HIV+, HEU and HIV− children. No associations between children’s LTL and their perinatal ART exposure or HIV status were seen in linear regression models. However, the association between having a detectable HIV viral load and shorter LTL suggests that uncontrolled HIV viremia rather than duration of ART exposure may be associated with acceleration of blood telomere attrition. PMID:22815702

A Simulation-Based Comparison of Several Stochastic Linear Regression Methods in the Presence of Outliers.

ERIC Educational Resources Information Center

Rule, David L.

Several regression methods were examined within the framework of weighted structural regression (WSR), comparing their regression weight stability and score estimation accuracy in the presence of outlier contamination. The methods compared are: (1) ordinary least squares; (2) WSR ridge regression; (3) minimum risk regression; (4) minimum risk 2;…

Unit Cohesion and the Surface Navy: Does Cohesion Affect Performance

DTIC Science & Technology

1989-12-01

v. 68, 1968. Neter, J., Wasserman, W., and Kutner, M. H., Applied Linear Regression Models, 2d ed., Boston, MA: Irwin, 1989. Rand Corporation R-2607...Neter, J., Wasserman, W., and Kutner, M. H., Applied Linear Regression Models, 2d ed., Boston, MA: Irwin, 1989. SAS User’s Guide: Basics, Version 5 ed

Comparison of Selection Procedures and Validation of Criterion Used in Selection of Significant Control Variates of a Simulation Model

DTIC Science & Technology

1990-03-01

and M.H. Knuter. Applied Linear Regression Models. Homewood IL: Richard D. Erwin Inc., 1983. Pritsker, A. Alan B. Introduction to Simulation and SLAM...Control Variates in Simulation," European Journal of Operational Research, 42: (1989). Neter, J., W. Wasserman, and M.H. Xnuter. Applied Linear Regression Models

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

ERIC Educational Resources Information Center

Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

Calibrated Peer Review for Interpreting Linear Regression Parameters: Results from a Graduate Course

ERIC Educational Resources Information Center

Enders, Felicity B.; Jenkins, Sarah; Hoverman, Verna

2010-01-01

Biostatistics is traditionally a difficult subject for students to learn. While the mathematical aspects are challenging, it can also be demanding for students to learn the exact language to use to correctly interpret statistical results. In particular, correctly interpreting the parameters from linear regression is both a vital tool and a…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

Using Simple Linear Regression to Assess the Success of the Montreal Protocol in Reducing Atmospheric Chlorofluorocarbons

ERIC Educational Resources Information Center

Nelson, Dean

2009-01-01

Following the Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendation to use real data, an example is presented in which simple linear regression is used to evaluate the effect of the Montreal Protocol on atmospheric concentration of chlorofluorocarbons. This simple set of data, obtained from a public archive, can…

Quantum State Tomography via Linear Regression Estimation

PubMed Central

Qi, Bo; Hou, Zhibo; Li, Li; Dong, Daoyi; Xiang, Guoyong; Guo, Guangcan

2013-01-01

A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the unknown parameters. An asymptotic mean squared error (MSE) upper bound for all possible states to be estimated is given analytically, which depends explicitly upon the involved measurement bases. This analytical MSE upper bound can guide one to choose optimal measurement sets. The computational complexity of LRE is O(d4) where d is the dimension of the quantum state. Numerical examples show that LRE is much faster than maximum-likelihood estimation for quantum state tomography. PMID:24336519

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

[Current situation of sleeping duration in Chinese Han students in 2010].

PubMed

Song, Yi; Zhang, Bing; Hu, Peijin; Ma, Jun

2014-07-01

To analyze the characteristics of sleep duration in Chinese primary and middle school students. The data was collected from 30 provinces (Autonomous regions, Municipalities) in 165 363 Han Primary school students above 4 grade, the junior and senior high school students who participated in 2010 National Physical Fitness and Health Surveillance by using stratified random cluster sampling method, and the questionnaire of sleep duration, insufficient sleep and commuting way from school was conducted at the same time.χ² test and χ² linear-by-linear test were used to analyze the difference between the different groups, and logistic regression was used to analyze the factors of insufficient sleep. Nationwide in 2010, 39.09% (64 646/165 363) of students reported they had more than 8 hours sleep duration per day, the prevalence was lower among urban (37.06% (30 767/83 027)) than rural (41.15% (33 879/82 336)) students (χ² = 290.53, P < 0.01), and higher among boys (40.25% (33 193/82 446)) than girls (37.94% (31 453/82 897)) (χ² = 92.51, P < 0.01). The prevalence of having more than 8 hours sleep duration per day in 9-12 years group, 13-15 years group and 16-18 years group was 70.24% (43 934/62 549), 31.31% (16 166/51 652) and 8.89% (546/51 162), respectively, and decreased with the age increasing (χ² linear-by-linear = 50 617.75, P < 0.01). The prevalence of insufficient sleep was 93.64% (154 838/165 363) in total students, the prevalence was higher among urban (94.94% (78 829/83 027)) than rural students (92.32% (76 009/82 336)) (χ² = 479.14, P < 0.01), and lower among boys (92.65% (76 408/82 466) than girls 94.61% (78 430/82 897) (χ² = 265.79, P < 0.01). The prevalence of insufficient sleep in 9-12 years group, 13-15 years group and 16-18 years group was 96.42% (60 310/62 549), 92.76% (47 912/51 562) and 91.11% (46 616/51 162), respectively. A multivariate logistic regression analysis (OR (95% CI)) revealed that the insufficient sleep was significantly associated with being urban (1.58 (1.51-1.65)), being girls (1.39 (1.34-1.45)), being 9-12 years group (2.77 (2.62-2.93)), living in the middle (1.19 (1.13-1.25)) or western (1.08 (1.03-1.13)) of China, and commuting from school by bicycle (1.21 (1.14-1.28)), bus/car (1.09 (1.03-1.15)), or in a boarding school (1.17 (1.10-1.24)). The sleep duration in Chinese school children is low, a sizeable proportion of school children sleep less than the recommended hours. The prevalence of insufficient sleep is high, and there are significant differences in different groups.

Mapping Soil pH Buffering Capacity of Selected Fields

NASA Technical Reports Server (NTRS)

Weaver, A. R.; Kissel, D. E.; Chen, F.; West, L. T.; Adkins, W.; Rickman, D.; Luvall, J. C.

2003-01-01

Soil pH buffering capacity, since it varies spatially within crop production fields, may be used to define sampling zones to assess lime requirement, or for modeling changes in soil pH when acid forming fertilizers or manures are added to a field. Our objective was to develop a procedure to map this soil property. One hundred thirty six soil samples (0 to 15 cm depth) from three Georgia Coastal Plain fields were titrated with calcium hydroxide to characterize differences in pH buffering capacity of the soils. Since the relationship between soil pH and added calcium hydroxide was approximately linear for all samples up to pH 6.5, the slope values of these linear relationships for all soils were regressed on the organic C and clay contents of the 136 soil samples using multiple linear regression. The equation that fit the data best was b (slope of pH vs. lime added) = 0.00029 - 0.00003 * % clay + 0.00135 * % O/C, r(exp 2) = 0.68. This equation was applied within geographic information system (GIS) software to create maps of soil pH buffering capacity for the three fields. When the mapped values of the pH buffering capacity were compared with measured values for a total of 18 locations in the three fields, there was good general agreement. A regression of directly measured pH buffering capacities on mapped pH buffering capacities at the field locations for these samples gave an r(exp 2) of 0.88 with a slope of 1.04 for a group of soils that varied approximately tenfold in their pH buffering capacities.

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

PubMed

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characteristic Motor and Nonmotor Symptoms Related to Quality of Life in Drug-Naïve Patients with Late-Onset Parkinson Disease.

PubMed

Park, Hea Ree; Youn, Jinyoung; Cho, Jin Whan; Oh, Eung-Seok; Kim, Ji Sun; Park, Suyeon; Jang, Wooyoung; Park, Jin Se

2018-01-01

Unlike young-onset Parkinson disease (YOPD), characteristics of late-onset PD (LOPD) have not yet been clearly elucidated. We investigated characteristic features and symptoms related to quality of life (QoL) in LOPD patients. We recruited drug-naïve, early PD patients. The patient cohort was divided into 3 subgroups based on patient age at onset (AAO): the YOPD group (AAO <50 years), the middle-onset PD (MOPD) group, and the LOPD group (AAO ≥70 years). Using various scales for motor symptoms (MS) and non-MS (NMS) and QoL, we compared the clinical features and impact on QoL. Of the 132 enrolled patients, 26 were in the YOPD group, 74 in the MOPD group, and 32 in the LOPD group. Among parkinsonian symptoms, patients in the LOPD group had a lower score on the Korean version of the Montreal Cognitive Assessment than the other groups. Logistic regression analysis showed genitourinary symptoms were related to the LOPD group. Linear regression analysis showed both MS and NMS were correlated with QoL in the MOPD group, but only NMS were correlated with QoL in the LOPD group. Particularly, anxiety and fatigue affected QoL in the LOPD group. LOPD patients showed different characteristic clinical features, and different symptoms were related with QoL for LOPD than YOPD and MOPD patients. © 2018 S. Karger AG, Basel.

[Visual field progression in glaucoma: cluster analysis].

PubMed

Bresson-Dumont, H; Hatton, J; Foucher, J; Fonteneau, M

2012-11-01

Visual field progression analysis is one of the key points in glaucoma monitoring, but distinction between true progression and random fluctuation is sometimes difficult. There are several different algorithms but no real consensus for detecting visual field progression. The trend analysis of global indices (MD, sLV) may miss localized deficits or be affected by media opacities. Conversely, point-by-point analysis makes progression difficult to differentiate from physiological variability, particularly when the sensitivity of a point is already low. The goal of our study was to analyse visual field progression with the EyeSuite™ Octopus Perimetry Clusters algorithm in patients with no significant changes in global indices or worsening of the analysis of pointwise linear regression. We analyzed the visual fields of 162 eyes (100 patients - 58 women, 42 men, average age 66.8 ± 10.91) with ocular hypertension or glaucoma. For inclusion, at least six reliable visual fields per eye were required, and the trend analysis (EyeSuite™ Perimetry) of visual field global indices (MD and SLV), could show no significant progression. The analysis of changes in cluster mode was then performed. In a second step, eyes with statistically significant worsening of at least one of their clusters were analyzed point-by-point with the Octopus Field Analysis (OFA). Fifty four eyes (33.33%) had a significant worsening in some clusters, while their global indices remained stable over time. In this group of patients, more advanced glaucoma was present than in stable group (MD 6.41 dB vs. 2.87); 64.82% (35/54) of those eyes in which the clusters progressed, however, had no statistically significant change in the trend analysis by pointwise linear regression. Most software algorithms for analyzing visual field progression are essentially trend analyses of global indices, or point-by-point linear regression. This study shows the potential role of analysis by clusters trend. However, for best results, it is preferable to compare the analyses of several tests in combination with morphologic exam. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Lower limb muscle strength is associated with poor balance in middle-aged women: linear and nonlinear analyses.

PubMed

Wu, F; Callisaya, M; Laslett, L L; Wills, K; Zhou, Y; Jones, G; Winzenberg, T

2016-07-01

This was the first study investigating both linear associations between lower limb muscle strength and balance in middle-aged women and the potential for thresholds for the associations. There was strong evidence that even in middle-aged women, poorer LMS was associated with reduced balance. However, no evidence was found for thresholds. Decline in balance begins in middle age, yet, the role of muscle strength in balance is rarely examined in this age group. We aimed to determine the association between lower limb muscle strength (LMS) and balance in middle-aged women and investigate whether cut-points of LMS exist that might identify women at risk of poorer balance. Cross-sectional analysis of 345 women aged 36-57 years was done. Associations between LMS and balance tests (timed up and go (TUG), step test (ST), functional reach test (FRT), and lateral reach test (LRT)) were assessed using linear regression. Nonlinear associations were explored using locally weighted regression smoothing (LOWESS) and potential cut-points identified using nonlinear least-squares estimation. Segmented regression was used to estimate associations above and below the identified cut-points. Weaker LMS was associated with poorer performance on the TUG (β -0.008 (95 % CI: -0.010, -0.005) second/kg), ST (β 0.031 (0.011, 0.051) step/kg), FRT (β 0.071 (0.047, 0.096) cm/kg), and LRT (β 0.028 (0.011, 0.044) cm/kg), independent of confounders. Potential nonlinear associations were evident from LOWESS results; significant cut-points of LMS were identified for all balance tests (29-50 kg). However, excepting ST, cut-points did not persist after excluding potentially influential data points. In middle-aged women, poorer LMS is associated with reduced balance. Therefore, improving muscle strength in middle-age may be a useful strategy to improve balance and reduce falls risk in later life. Middle-aged women with low muscle strength may be an effective target group for future randomized controlled trials. Australian New Zealand Clinical Trials Registry (ANZCTR) NCT00273260.

Relationships between use of television during meals and children's food consumption patterns.

PubMed

Coon, K A; Goldberg, J; Rogers, B L; Tucker, K L

2001-01-01

We examined relationships between the presence of television during meals and children's food consumption patterns to test whether children's overall food consumption patterns, including foods not normally advertised, vary systematically with the extent to which television is part of normal mealtime routines. Ninety-one parent-child pairs from suburbs adjacent to Washington, DC, recruited via advertisements and word of mouth, participated. Children were in the fourth, fifth, or sixth grades. Socioeconomic data and information on television use were collected during survey interviews. Three nonconsecutive 24-hour dietary recalls, conducted with each child, were used to construct nutrient and food intake outcome variables. Independent sample t tests were used to compare mean food and nutrient intakes of children from families in which the television was usually on during 2 or more meals (n = 41) to those of children from families in which the television was either never on or only on during one meal (n = 50). Multiple linear regression models, controlling for socioeconomic factors and other covariates, were used to test strength of associations between television and children's consumption of food groups and nutrients. Children from families with high television use derived, on average, 6% more of their total daily energy intake from meats; 5% more from pizza, salty snacks, and soda; and nearly 5% less of their energy intake from fruits, vegetables, and juices than did children from families with low television use. Associations between television and children's consumption of food groups remained statistically significant in multiple linear regression models that controlled for socioeconomic factors and other covariates. Children from high television families derived less of their total energy from carbohydrate and consumed twice as much caffeine as children from low television families. There continued to be a significant association between television and children's consumption of caffeine when these relationships were tested in multiple linear regression models. The dietary patterns of children from families in which television viewing is a normal part of meal routines may include fewer fruits and vegetables and more pizzas, snack foods, and sodas than the dietary patterns of children from families in which television viewing and eating are separate activities.

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

PubMed

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

PubMed Central

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables. PMID:29713298

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

PubMed

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.

Regression of non-linear coupling of noise in LIGO detectors

NASA Astrophysics Data System (ADS)

Da Silva Costa, C. F.; Billman, C.; Effler, A.; Klimenko, S.; Cheng, H.-P.

2018-03-01

In 2015, after their upgrade, the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors started acquiring data. The effort to improve their sensitivity has never stopped since then. The goal to achieve design sensitivity is challenging. Environmental and instrumental noise couple to the detector output with different, linear and non-linear, coupling mechanisms. The noise regression method we use is based on the Wiener–Kolmogorov filter, which uses witness channels to make noise predictions. We present here how this method helped to determine complex non-linear noise couplings in the output mode cleaner and in the mirror suspension system of the LIGO detector.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Methamphetamine abuse during pregnancy and its health impact on neonates born at Siriraj Hospital, Bangkok, Thailand.

PubMed

Chomchai, Chulathida; Na Manorom, Natawadee; Watanarungsan, Pornchai; Yossuck, Panitan; Chomchai, Summon

2004-03-01

To ascertain the impact of intrauterine methamphetamine exposure on the overall health of newborn infants at Siriraj Hospital, Bangkok, Thailand, birth records of somatic growth parameters and neonatal withdrawal symptoms of 47 infants born to methamphetamine-abusing women during January 2001 to December 2001 were compared to 49 newborns whose mothers did not use methamphetamines during pregnancy. The data on somatic growth was analyzed using linear regression and multiple linear regression. The association between methamphetamine use and withdrawal symptoms was analyzed using the chi-square. Home visitation and maternal interview records were reviewed in order to assess for child-rearing attitude, and psychosocial parameters. Infants of methamphetamine-abusing mothers were found to have a significantly smaller gestational age-adjusted head circumference (regression coefficient = -1.458, p < 0.001) and birth weight (regression coefficient = -217.9, p < or = 0.001) measurements. Methamphetamine exposure was also associated with symptoms of agitation (5/47), vomiting (11/47) and tachypnea (12/47) when compared to the non-exposed group (p < 0r =0.001). Maternal interviews were conducted in 23 cases and showed that: 96% of the cases had inadequate prenatal care (<5 visits), 48% had at least one parent involved in prostitution, 39% of the mothers were unwilling to take their children home, and government or non-government support were provided in only 30% of the cases. In-utero methamphetamine exposure has been shown to adversely effect somatic growth of newborns and cause a variety of withdrawal-like symptoms. These infants are also psychosocially disadvantaged and are at greater risk for abuse and neglect.

Is long-term exposure to traffic pollution associated with mortality? A small-area study in London.

PubMed

Halonen, Jaana I; Blangiardo, Marta; Toledano, Mireille B; Fecht, Daniela; Gulliver, John; Ghosh, Rebecca; Anderson, H Ross; Beevers, Sean D; Dajnak, David; Kelly, Frank J; Wilkinson, Paul; Tonne, Cathryn

2016-01-01

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Association of dentine hypersensitivity with different risk factors - a cross sectional study.

PubMed

Vijaya, V; Sanjay, Venkataraam; Varghese, Rana K; Ravuri, Rajyalakshmi; Agarwal, Anil

2013-12-01

This study was done to assess the prevalence of Dentine hypersensitivity (DH) and its associated risk factors. This epidemiological study was done among patients coming to dental college regarding prevalence of DH. A self structured questionnaire along with clinical examination was done for assessment. Descriptive statistics were obtained and frequency distribution was calculated using Chi square test at p value <0.05. Stepwise multiple linear regression was also done to access frequency of DH with different factors. The study population was comprised of 655 participants with different age groups. Our study showed prevalence as 55% and it was more common among males. Similarly smokers and those who use hard tooth brush had more cases of DH. Step wise multiple linear regression showed that best predictor for DH was age followed by habit of smoking and type of tooth brush. Most aggravating factors were cold water (15.4%) and sweet foods (14.7%), whereas only 5% of the patients had it while brushing. A high level of dental hypersensitivity has been in this study and more common among males. A linear finding was shown with age, smoking and type of tooth brush. How to cite this article: Vijaya V, Sanjay V, Varghese RK, Ravuri R, Agarwal A. Association of Dentine Hypersensitivity with Different Risk Factors - A Cross Sectional Study. J Int Oral Health 2013;5(6):88-92 .

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Dental computed tomographic imaging as age estimation: morphological analysis of the third molar of a group of Turkish population.

PubMed

Cantekin, Kenan; Sekerci, Ahmet Ercan; Buyuk, Suleyman Kutalmis

2013-12-01

Computed tomography (CT) is capable of providing accurate and measurable 3-dimensional images of the third molar. The aims of this study were to analyze the development of the mandibular third molar and its relation to chronological age and to create new reference data for a group of Turkish participants aged 9 to 25 years on the basis of cone-beam CT images. All data were obtained from the patients' records including medical, social, and dental anamnesis and cone-beam CT images of 752 patients. Linear regression analysis was performed to obtain regression formulas for dental age calculation with chronological age and to determine the coefficient of determination (r) for each sex. Statistical analysis showed a strong correlation between age and third-molar development for the males (r2 = 0.80) and the females (r2 = 0.78). Computed tomographic images are clinically useful for accurate and reliable estimation of dental ages of children and youth.

Ataque de nervios: relationship to anxiety sensitivity and dissociation predisposition.

PubMed

Hinton, Devon E; Chong, Roberto; Pollack, Mark H; Barlow, David H; McNally, Richard J

2008-01-01

We investigated the relative importance of "fear of arousal symptoms" (i.e., anxiety sensitivity) and "dissociation tendency" in generating ataque de nervios. Puerto Rican patients attending an outpatient psychiatric clinic were assessed for ataque de nervios frequency in the previous month, and they completed the Anxiety Sensitivity Index (ASI) and the Dissociation Experiences Scale (DES). ASI scores were especially high in the ataque-positive group (M=41.6, SD=12.8) as compared with the ataque-negative group (M=27.2, SD=11.7), t(2, 68)=4.6, P<.001. Among the whole sample (N=70), in a logistic regression analysis, the ASI significantly predicted (odds ratio=2.6) the presence of ataque de nervios, but the DES did not. In a linear regression analysis, ataque severity was significantly predicted by both the ASI (beta=.46) and the DES (beta=.29). The theoretical and clinical implications of the strong relationship of the ASI to ataque severity are discussed.

Correlates of motivation to change in pathological gamblers completing cognitive-behavioral group therapy.

PubMed

Gómez-Peña, Mónica; Penelo, Eva; Granero, Roser; Fernández-Aranda, Fernando; Alvarez-Moya, Eva; Santamaría, Juan José; Moragas, Laura; Neus Aymamí, Maria; Gunnard, Katarina; Menchón, José M; Jimenez-Murcia, Susana

2012-07-01

The present study analyzes the association between the motivation to change and the cognitive-behavioral group intervention, in terms of dropouts and relapses, in a sample of male pathological gamblers. The specific objectives were as follows: (a) to estimate the predictive value of baseline University of Rhode Island Change Assessment scale (URICA) scores (i.e., at the start of the study) as regards the risk of relapse and dropout during treatment and (b) to assess the incremental predictive ability of URICA scores, as regards the mean change produced in the clinical status of patients between the start and finish of treatment. The relationship between the URICA and the response to treatment was analyzed by means of a pre-post design applied to a sample of 191 patients who were consecutively receiving cognitive-behavioral group therapy. The statistical analysis included logistic regression models and hierarchical multiple linear regression models. The discriminative ability of the models including the four URICA scores regarding the likelihood of relapse and dropout was acceptable (area under the receiver operating haracteristic curve: .73 and .71, respectively). No significant predictive ability was found as regards the differences between baseline and posttreatment scores (changes in R(2) below 5% in the multiple regression models). The availability of useful measures of motivation to change would enable treatment outcomes to be optimized through the application of specific therapeutic interventions. © 2012 Wiley Periodicals, Inc.

Income analysis of goat farmers on the farmers group in district of Serdang Bedagai

NASA Astrophysics Data System (ADS)

Manurung, J. N.; Hasnudi; Supriana, T.

2018-02-01

The farmers group are expected to reduce the production cost of goat breeding and improve the income of farmers which impact on the welfare of goat farmers. This research aim to analyze the factors that influence the income of farmers group, in sub-district Dolok Masihul Pegajahan, and Dolok Merawan, Serdang Bedagai. The method used is survey method with 90 respondents. Data was analysed by multiple linear regression. The result showed, simultaneously goat cost, sale price of goat, fixed cost and variable cost had significant effect on income of goat farmers. Partially, goat cost, variable cost and sale price of goat had significant effect on income of goat farmers, while fixed cost had no significant effect.

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

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

Adding a Parameter Increases the Variance of an Estimated Regression Function

ERIC Educational Resources Information Center

Withers, Christopher S.; Nadarajah, Saralees

2011-01-01

The linear regression model is one of the most popular models in statistics. It is also one of the simplest models in statistics. It has received applications in almost every area of science, engineering and medicine. In this article, the authors show that adding a predictor to a linear model increases the variance of the estimated regression…

Using nonlinear quantile regression to estimate the self-thinning boundary curve

Treesearch

Quang V. Cao; Thomas J. Dean

2015-01-01

The relationship between tree size (quadratic mean diameter) and tree density (number of trees per unit area) has been a topic of research and discussion for many decades. Starting with Reineke in 1933, the maximum size-density relationship, on a log-log scale, has been assumed to be linear. Several techniques, including linear quantile regression, have been employed...

Simultaneous spectrophotometric determination of salbutamol and bromhexine in tablets.

PubMed

Habib, I H I; Hassouna, M E M; Zaki, G A

2005-03-01

Typical anti-mucolytic drugs called salbutamol hydrochloride and bromhexine sulfate encountered in tablets were determined simultaneously either by using linear regression at zero-crossing wavelengths of the first derivation of UV-spectra or by application of multiple linear partial least squares regression method. The results obtained by the two proposed mathematical methods were compared with those obtained by the HPLC technique.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Image interpolation via regularized local linear regression.

PubMed

Liu, Xianming; Zhao, Debin; Xiong, Ruiqin; Ma, Siwei; Gao, Wen; Sun, Huifang

2011-12-01

The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the l(2)-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation. © 2011 IEEE

Factors determining the smooth flow and the non-operative time in a one-induction room to one-operating room setting

PubMed Central

Mulier, Jan P; De Boeck, Liesje; Meulders, Michel; Beliën, Jeroen; Colpaert, Jan; Sels, Annabel

2015-01-01

Rationale, aims and objectives What factors determine the use of an anaesthesia preparation room and shorten non-operative time? Methods A logistic regression is applied to 18 751 surgery records from AZ Sint-Jan Brugge AV, Belgium, where each operating room has its own anaesthesia preparation room. Surgeries, in which the patient's induction has already started when the preceding patient's surgery has ended, belong to a first group where the preparation room is used as an induction room. Surgeries not fulfilling this property belong to a second group. A logistic regression model tries to predict the probability that a surgery will be classified into a specific group. Non-operative time is calculated as the time between end of the previous surgery and incision of the next surgery. A log-linear regression of this non-operative time is performed. Results It was found that switches in surgeons, being a non-elective surgery as well as the previous surgery being non-elective, increase the probability of being classified into the second group. Only a few surgery types, anaesthesiologists and operating rooms can be found exclusively in one of the two groups. Analysis of variance demonstrates that the first group has significantly lower non-operative times. Switches in surgeons, anaesthesiologists and longer scheduled durations of the previous surgery increases the non-operative time. A switch in both surgeon and anaesthesiologist strengthens this negative effect. Only a few operating rooms and surgery types influence the non-operative time. Conclusion The use of the anaesthesia preparation room shortens the non-operative time and is determined by several human and structural factors. PMID:25496600

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

PubMed Central

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

PubMed

Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Effect of a combined education and eHealth programme on the control of oral anticoagulation patients (PORTALS study): a parallel cohort design in Dutch primary care.

PubMed

Talboom-Kamp, Esther P W A; Verdijk, Noortje A; Kasteleyn, Marise J; Harmans, Lara M; Talboom, Irvin J S H; Numans, Mattijs E; Chavannes, Niels H

2017-09-27

To analyse the effect on therapeutic control and self-management skills of the implementation of self-management programmes, including eHealth by e-learning versus group training. Primary Care Thrombosis Service Center. Of the 247 oral anticoagulation therapy (OAT) patients, 63 started self-management by e-learning, 74 self-management by group training and 110 received usual care. Parallel cohort design with two randomised self-management groups (e-learning and group training) and a group receiving usual care. The effect of implementation of self-management on time in therapeutic range (TTR) was analysed with multilevel linear regression modelling. Usage of a supporting eHealth platform and the impact on self-efficacy (Generalised Self-Efficacy Scale (GSES)) and education level were analysed with linear regression analysis. After intervention, TTR was measured in three time periods of 6 months. (1) TTR, severe complications,(2) usage of an eHealth platform,(3) GSES, education level. Analysis showed no significant differences in TTR between the three time periods (p=0.520), the three groups (p=0.460) or the groups over time (p=0.263). Comparison of e-learning and group training showed no significant differences in TTR between the time periods (p=0.614), the groups (p=0.460) or the groups over time (p=0.263). No association was found between GSES and TTR (p=0.717) or education level and TTR (p=0.107). No significant difference was found between the self-management groups in usage of the platform (0-6 months p=0.571; 6-12 months p=0.866; 12-18 months p=0.260). The percentage of complications was low in all groups (3.2%; 1.4%; 0%). No differences were found between OAT patients trained by e-learning or by a group course regarding therapeutic control (TTR) and usage of a supporting eHealth platform. The TTR was similar in self-management and regular care patients. With adequate e-learning or group training, self-management seems safe and reliable for a selected proportion of motivated vitamin K antagonist patients. NTR3947. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

[Association of occupational chronic psychological stress with heat shock protein 70 in serum and tumor necrosis factor-alpha expression levels].

PubMed

Qiu, F Y; Tian, R L; Qiang, Y; He, K P; Liu, H R; Zhang, W; Song, H

2016-05-01

To investigate the relationship between occupational chronic psychological stress with heat shock protein 70 (HSP70) and tumor necrosis factor-alpha (TNF-α). Using case-control study design, we selected 622 cases in 20 to 60 years old and unrelated patients with metabolic syndrome as the case group between October 2011 and October 2012 at two hospitals of Ningxia hui autonomous region. At the same time, we selected 600 healthy people from health check-up crowd in the above two hospitals as control group. The the research objects were sex, age, nation, height, weight, smoking, drinking, exercise, and so on. After informed consent, all the research objects were collected fasting venous blood samples 10 ml in order to proceed laboratory testing of biochemical indicators. The expression of HSP70 and TNF-α in serum was determined by ELISA. Using the revised occupational stress inventory (OSI) to survey the occupational chronic psychological stress factors and stress level of research object. The correlation of occupational chronic psychological stress scores with HSP70 and TNF-α was investigated by partial correlation analysis. We built a multivariate linear regression equation With HSP70 and TNF alpha as the independent variable and occupational chronic psychological stress scores as the dependent variable, using equation of the determination coefficient R(2) to judge the degree of fitting equation. The total points of chronic stress factors in all respondents was (136.65±16.19). Among them, the mild stress level group was 313, moderate was 588, severe was 321, chronic heart stress factors scores were (119.96±13.30), (135.33±3.23), (155.33±13.55) points, respectively. In the case group subjects, the expression of HSP70 in mild, moderate and severe occupational chronic psychological stress levels were (29.88±30.08), (36.38±30.08), (27.16±23.77) ng/ml (F=6.85, P=0.001). The control group were (27.64±9.89), (39.78±29.77), (3.94±3.09) ng/ml (F=125.71, P<0.001). Multiple linear regression analysis showed the expression of psychological stress and HSP70 was a negative linear relationship, while positive linear relationship with TNF-α, the fitting of the regression equation was y=-0.07x1+ 0.011x2+ 136.88. Partial correlation analysis results showed that the occupational chronic psychological stress scores and negatively correlated with HSP70 (r=-0.11, P<0.001) and was positively related with TNF-α (r=0.11, P<0.001). In all survey respondents, the expression of HSP70 in mild, moderate and severe occupational chronic psychological stress levels group were (28.49±20.10), (37.99±29.96), (17.98±21.77) ng/ml (F=64.08, P<0.001). The expression of TNF-α were (133.61±129.51), (171.23±133.69), (169.31±196.09) pg/ml (F=6.93, P=0.001). The expression levels of HSP70 and TNF-α in serum were affected by occupational chronic psychological stress. While the level of occupational chronic psychological stress increased, the expression level of HSP70 in serum reduced, the expression level of TNF-α raised.

The culture of mentoring: Ethnocultural empathy and ethnic identity in mentoring for minority girls.

PubMed

Peifer, Janelle S; Lawrence, Edith C; Williams, Joanna Lee; Leyton-Armakan, Jen

2016-07-01

Many mentoring programs place minority group mentees with majority group mentors. These programs aim to promote beneficial outcomes for their diverse participants. The present study explores mentors of color and White mentors' ethnocultural empathy and ethnic identities in association with their minority group mentees' ethnic identities. Our study examined 95 mentoring pairs of middle school girls of color and college student women from both majority and minority group cultural backgrounds. A series of linear regressions revealed an association between mentors' ethnocultural empathy and EI exploration/commitment and minority group mentees' ethnic identity exploration, regardless of the mentors' majority group status. The results of this preliminary study suggest that mentors' cultural identity and empathy may be linked with mentees' willingness to explore their own ethnic identities. We discuss the implications for mentoring programs that seek to build participants' ethnic identities and ethnocultural empathy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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

PubMed Central

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

2016-01-01

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

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Clinical significance and distribution of BRCA genes mutation in sporadic high grade serous ovarian cancer].

PubMed

Liu, W L; Wang, Z Z; Zhao, J Z; Hou, Y Y; Wu, X X; Li, W; Dong, B; Tong, T T; Guo, Y J

2017-01-25

Objective: To investigate the mutations of BRCA genes in sporadic high grade serous ovarian cancer (HGSOC) and study its clinical significance. Methods: Sixty-eight patients between January 2015 and January 2016 from the Affiliated Cancer Hospital of Zhengzhou University were collected who were based on pathological diagnosis of ovarian cancer and had no reported family history, and all patients firstly hospitalized were untreated in other hospitals before. (1) The BRCA genes were detected by next-generation sequencing (NGS) method. (2) The serum tumor markers included carcinoembryonic antigen (CEA), CA(125), CA(199), and human epididymis protein 4 (HE4) were detected by the chemiluminescence methods, and their correlation was analyzed by Pearson linear correlation. Descriptive statistics and comparisons were performed using two-tailed t -tests, Pearson's chi square test, Fisher's exact tests or logistic regression analysis as appropriate to research the clinicopathologic features associated with BRCA mutations, including age, International Federation of Gynecology and Obstetrics (FIGO) stage, platinum-based chemotherapy sensitivity, distant metastases, serum tumor markers (STM) . Results: (1) Fifteen cases (22%, 15/68) BRCA mutations were identified (BRCA1: 11 cases; BRCA2: 4 cases), and four novel mutations were observed. (2) The levels of CEA, CA(199), and HE4 were lower in BRCA mutations compared to that in control group, while no significant differences were found ( P >0.05), but the level of CA(125) was much higher in BRCA mutation group than that in controls ( t =-3.536, P =0.003). Further linear regression analysis found that there was a significant linear correlation between CA(125) and HE4 group ( r =0.494, P <0.01), and the same correlation as CEA and CA(199) group ( r =0.897, P <0.01). (3) Single factor analysis showed that no significant differences were observed in onset age, FIGO stage, distant metastasis, and STM between BRCA(+) and BRCA(-) group ( P >0.05), while significant differences were found in CA(125) and sensitivity to platinum-based chemotherapy between the patients with BRCA mutation and wild type ( P <0.05). The multiple factors analysis showed that the high level of CA(125) was a independent risk factor of BRCA mutations in sporadic HGSOC ( P =0.007). Conclusion: The combination of CA(125) with BRCA have great clinical significance, the mutation of BRCA gene could guild the clinical chemotherapy regiments.

Gonadotropin-releasing hormone agonist trigger increases the number of oocytes and embryos available for cryopreservation in cancer patients undergoing ovarian stimulation for fertility preservation.

PubMed

Pereira, Nigel; Kelly, Amelia G; Stone, Logan D; Witzke, Justine D; Lekovich, Jovana P; Elias, Rony T; Schattman, Glenn L; Rosenwaks, Zev

2017-09-01

To compare the oocyte and embryo yield associated with GnRH-agonist triggers vs. hCG triggers in cancer patients undergoing controlled ovarian stimulation (COS) for fertilization preservation. Retrospective cohort study. Academic center. Cancer patients undergoing COS with letrozole and gonadotropins or gonadotropin-only protocols for oocyte or embryo cryopreservation. Gonadotropin-releasing hormone agonist or hCG trigger. Number of metaphase II (MII) oocytes or two-pronuclei (2PN) embryos available for cryopreservation were primary outcomes. Separate multivariate linear regression models were used to assess the effect of trigger type on the primary outcomes, after controlling for confounders of interest. A total of 341 patients were included, 99 (29.0%) in the GnRH-agonist group and 242 (71%) in the hCG group. There was no difference in the baseline demographics of patients receiving GnRH-agonist or hCG triggers. Within the letrozole and gonadotropins group (n = 269), the number (mean ± SD, 11.8 ± 5.8 vs. 9.9 ± 6.0) and percentage of MII oocytes (89.6% vs. 73.0%) available for cryopreservation was higher with GnRH-agonist triggers compared with hCG triggers. Similar results were noted with GnRH-agonist triggers in the gonadotropin-only group (n = 72) (i.e., a higher number [13.3 ± 7.9 vs. 9.3 ± 6.0] and percentage of MII oocytes [85.7% vs. 72.8%] available for cryopreservation). Multivariate linear regression demonstrated approximately three more MII oocytes and 2PN embryos available for cryopreservation in the GnRH-agonist trigger group, irrespective of cancer and COS protocol type. Utilization of a GnRH-agonist trigger increases the number of MII oocytes and 2PN embryos available for cryopreservation in cancer patients undergoing COS for fertility preservation. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Determinants of linear growth in Malaysian children with cerebral palsy.

PubMed

Zainah, S H; Ong, L C; Sofiah, A; Poh, B K; Hussain, I H

2001-08-01

To compare the linear growth and nutritional parameters of a group of Malaysian children with cerebral palsy (CP) against a group of controls, and to determine the nutritional, medical and sociodemographic factors associated with poor growth in children with CP. The linear growth of 101 children with CP and of their healthy controls matched for age, sex and ethnicity was measured using upper-arm length (UAL). Nutritional parameters of weight, triceps skin-fold thickness and mid-arm circumference were also measured. Total caloric intake was assessed using a 24-h recall of a 3-day food intake and calculated as a percentage of the Recommended Daily Allowance. Multiple regression analysis was used to determine nutritional, medical and sociodemographic factors associated with poor growth (using z-scores of UAL) in children with CP. Compared with the controls, children with CP had significantly lower mean UAL measurements (difference between means -1.1, 95% confidence interval -1.65 to - 0.59), weight (difference between means -6.0, 95% CI -7.66 to -4.34), mid-arm circumference (difference between means -1.3, 95% CI -2.06 to -0.56) and triceps skin-fold thickness (difference between means -2.5, 95% CI -3.5 to -1.43). Factors associated with low z-scores of UAL were a lower percentage of median weight (P < 0.001), tube feeding (P < 0.001) and increasing age (P < 0.001). A large proportion of Malaysian children with CP have poor nutritional status and linear growth. Nutritional assessment and management at an early age might help this group of children achieve adequate growth.

Solving a mixture of many random linear equations by tensor decomposition and alternating minimization.

DOT National Transportation Integrated Search

2016-09-01

We consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample...

Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Edgerton, Eric S.; Hartsell, Benjamin E.

A variety of linear regression techniques and simple slope estimators are evaluated for use in the elemental carbon (EC) tracer method of secondary organic carbon (OC) estimation. Linear regression techniques based on ordinary least squares are not suitable for situations where measurement uncertainties exist in both regressed variables. In the past, regression based on the method of Deming [1943. Statistical Adjustment of Data. Wiley, London] has been the preferred choice for EC tracer method parameter estimation. In agreement with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], we find that in the limited case where primary non-combustion OC (OC non-comb) is assumed to be zero, the ratio of averages (ROA) approach provides a stable and reliable estimate of the primary OC-EC ratio, (OC/EC) pri. In contrast with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], however, we find that the optimal use of Deming regression (and the more general York et al. [2004. Unified equations for the slope, intercept, and standard errors of the best straight line. American Journal of Physics 72, 367-375] regression) provides excellent results as well. For the more typical case where OC non-comb is allowed to obtain a non-zero value, we find that regression based on the method of York is the preferred choice for EC tracer method parameter estimation. In the York regression technique, detailed information on uncertainties in the measurement of OC and EC is used to improve the linear best fit to the given data. If only limited information is available on the relative uncertainties of OC and EC, then Deming regression should be used. On the other hand, use of ROA in the estimation of secondary OC, and thus the assumption of a zero OC non-comb value, generally leads to an overestimation of the contribution of secondary OC to total measured OC.

Estimation of streamflow for selected sites on the Carson and Truckee rivers in California and Nevada, 1944-80

USGS Publications Warehouse

Blodgett, J.C.; Oltmann, R.N.; Poeschel, K.R.

1984-01-01

Daily mean and monthly discharges were estimated for 10 sites on the Carson and Truckee Rivers for periods of incomplete records and for tributary sites affected by reservoir regulation. On the basis of the hydrologic characteristics, stream-flow data for a water year were grouped by month or season for subsequent regression analysis. In most cases, simple linear regressions adequately defined a relation of streamflow between gaging stations, but in some instances a nonlinear relation for several months of the water year was derived. Statistical data are presented to indicate the reliability of the estimated streamflow data. Records of discharges including historical and estimated data for the gaging stations for the water years 1944-80 are presented. (USGS)

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Caloric sweetener consumption and dyslipidemia among US adults.

PubMed

Welsh, Jean A; Sharma, Andrea; Abramson, Jerome L; Vaccarino, Viola; Gillespie, Cathleen; Vos, Miriam B

2010-04-21

Dietary carbohydrates have been associated with dyslipidemia, a lipid profile known to increase cardiovascular disease risk. Added sugars (caloric sweeteners used as ingredients in processed or prepared foods) are an increasing and potentially modifiable component in the US diet. No known studies have examined the association between the consumption of added sugars and lipid measures. To assess the association between consumption of added sugars and blood lipid levels in US adults. Cross-sectional study among US adults (n = 6113) from the National Health and Nutrition Examination Survey (NHANES) 1999-2006. Respondents were grouped by intake of added sugars using limits specified in dietary recommendations (< 5% [reference group], 5%-<10%, 10%-<17.5%, 17.5%-<25%, and > or = 25% of total calories). Linear regression was used to estimate adjusted mean lipid levels. Logistic regression was used to determine adjusted odds ratios of dyslipidemia. Interactions between added sugars and sex were evaluated. Adjusted mean high-density lipoprotein cholesterol (HDL-C), geometric mean triglycerides, and mean low-density lipoprotein cholesterol (LDL-C) levels and adjusted odds ratios of dyslipidemia, including low HDL-C levels (< 40 mg/dL for men; < 50 mg/dL for women), high triglyceride levels (> or = 150 mg/dL), high LDL-C levels (> or = 130 mg/dL), or high ratio of triglycerides to HDL-C (> 3.8). Results were weighted to be representative of the US population. A mean of 15.8% of consumed calories was from added sugars. Among participants consuming less than 5%, 5% to less than 17.5%, 17.5% to less than 25%, and 25% or greater of total energy as added sugars, adjusted mean HDL-C levels were, respectively, 58.7, 57.5, 53.7, 51.0, and 47.7 mg/dL (P < .001 for linear trend), geometric mean triglyceride levels were 105, 102, 111, 113, and 114 mg/dL (P < .001 for linear trend), and LDL-C levels modified by sex were 116, 115, 118, 121, and 123 mg/dL among women (P = .047 for linear trend). There were no significant trends in LDL-C levels among men. Among higher consumers (> or = 10% added sugars) the odds of low HDL-C levels were 50% to more than 300% greater compared with the reference group (< 5% added sugars). In this study, there was a statistically significant correlation between dietary added sugars and blood lipid levels among US adults.

Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure.

PubMed

Senn, Stephen; Graf, Erika; Caputo, Angelika

2007-12-30

Stratifying and matching by the propensity score are increasingly popular approaches to deal with confounding in medical studies investigating effects of a treatment or exposure. A more traditional alternative technique is the direct adjustment for confounding in regression models. This paper discusses fundamental differences between the two approaches, with a focus on linear regression and propensity score stratification, and identifies points to be considered for an adequate comparison. The treatment estimators are examined for unbiasedness and efficiency. This is illustrated in an application to real data and supplemented by an investigation on properties of the estimators for a range of underlying linear models. We demonstrate that in specific circumstances the propensity score estimator is identical to the effect estimated from a full linear model, even if it is built on coarser covariate strata than the linear model. As a consequence the coarsening property of the propensity score-adjustment for a one-dimensional confounder instead of a high-dimensional covariate-may be viewed as a way to implement a pre-specified, richly parametrized linear model. We conclude that the propensity score estimator inherits the potential for overfitting and that care should be taken to restrict covariates to those relevant for outcome. Copyright (c) 2007 John Wiley & Sons, Ltd.

Determination of pKa values of alendronate sodium in aqueous solution by piecewise linear regression based on acid-base potentiometric titration.

PubMed

Ke, Jing; Dou, Hanfei; Zhang, Ximin; Uhagaze, Dushimabararezi Serge; Ding, Xiali; Dong, Yuming

2016-12-01

As a mono-sodium salt form of alendronic acid, alendronate sodium presents multi-level ionization for the dissociation of its four hydroxyl groups. The dissociation constants of alendronate sodium were determined in this work by studying the piecewise linear relationship between volume of titrant and pH value based on acid-base potentiometric titration reaction. The distribution curves of alendronate sodium were drawn according to the determined pKa values. There were 4 dissociation constants (pKa 1 =2.43, pKa 2 =7.55, pKa 3 =10.80, pKa 4 =11.99, respectively) of alendronate sodium, and 12 existing forms, of which 4 could be ignored, existing in different pH environments.

Durango delta: Complications on San Juan basin Cretaceous linear strandline theme

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

Zech, R.S.; Wright, R.

1989-09-01

The Upper Cretaceous Point Lookout Sandstone generally conforms to a predictable cyclic shoreface model in which prograding linear strandline lithosomes dominate formation architecture. Multiple transgressive-regressive cycles results in systematic repetition of lithologies deposited in beach to inner shelf environments. Deposits of approximately five cycles are locally grouped into bundles. Such bundles extend at least 20 km along depositional strike and change from foreshore sandstone to offshore, time-equivalent Mancos mud rock in a downdip distance of 17 to 20 km. Excellent hydrocarbon reservoirs exist where well-sorted shoreface sandstone bundles stack and the formation thickens. This depositional model breaks down in themore » vicinity of Durango, Colorado, where a fluvial-dominated delta front and associated large distributary channels characterize the Point Lookout Sandstone and overlying Menefee Formation.« less

Kinetics of hydrogen peroxide decomposition by catalase: hydroxylic solvent effects.

PubMed

Raducan, Adina; Cantemir, Anca Ruxandra; Puiu, Mihaela; Oancea, Dumitru

2012-11-01

The effect of water-alcohol (methanol, ethanol, propan-1-ol, propan-2-ol, ethane-1,2-diol and propane-1,2,3-triol) binary mixtures on the kinetics of hydrogen peroxide decomposition in the presence of bovine liver catalase is investigated. In all solvents, the activity of catalase is smaller than in water. The results are discussed on the basis of a simple kinetic model. The kinetic constants for product formation through enzyme-substrate complex decomposition and for inactivation of catalase are estimated. The organic solvents are characterized by several physical properties: dielectric constant (D), hydrophobicity (log P), concentration of hydroxyl groups ([OH]), polarizability (α), Kamlet-Taft parameter (β) and Kosower parameter (Z). The relationships between the initial rate, kinetic constants and medium properties are analyzed by linear and multiple linear regression.

Response to antiretroviral therapy (ART): comparing women with previous use of zidovudine monotherapy (ZDVm) in pregnancy with ART naïve women.

PubMed

Huntington, Susie; Thorne, Claire; Anderson, Jane; Newell, Marie-Louise; Taylor, Graham P; Pillay, Deenan; Hill, Teresa; Tookey, Pat; Sabin, Caroline

2014-03-04

Short-term zidovudine monotherapy (ZDVm) remains an option for some pregnant HIV-positive women not requiring treatment for their own health but may affect treatment responses once antiretroviral therapy (ART) is subsequently started. Data were obtained by linking two UK studies: the UK Collaborative HIV Cohort (UK CHIC) study and the National Study of HIV in Pregnancy and Childhood (NSHPC). Treatment responses were assessed for 2028 women initiating ART at least one year after HIV-diagnosis. Outcomes were compared using logistic regression, proportional hazards regression or linear regression. In adjusted analyses, ART-naïve (n = 1937) and ZDVm-experienced (n = 91) women had similar increases in CD4 count and a similar proportion achieving virological suppression; both groups had a low risk of AIDS. In this setting, antenatal ZDVm exposure did not adversely impact on outcomes once ART was initiated for the woman's health.

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).

PubMed

O'Leary, Neil; Chauhan, Balwantray C; Artes, Paul H

2012-10-01

To establish a method for estimating the overall statistical significance of visual field deterioration from an individual patient's data, and to compare its performance to pointwise linear regression. The Truncated Product Method was used to calculate a statistic S that combines evidence of deterioration from individual test locations in the visual field. The overall statistical significance (P value) of visual field deterioration was inferred by comparing S with its permutation distribution, derived from repeated reordering of the visual field series. Permutation of pointwise linear regression (PoPLR) and pointwise linear regression were evaluated in data from patients with glaucoma (944 eyes, median mean deviation -2.9 dB, interquartile range: -6.3, -1.2 dB) followed for more than 4 years (median 10 examinations over 8 years). False-positive rates were estimated from randomly reordered series of this dataset, and hit rates (proportion of eyes with significant deterioration) were estimated from the original series. The false-positive rates of PoPLR were indistinguishable from the corresponding nominal significance levels and were independent of baseline visual field damage and length of follow-up. At P < 0.05, the hit rates of PoPLR were 12, 29, and 42%, at the fifth, eighth, and final examinations, respectively, and at matching specificities they were consistently higher than those of pointwise linear regression. In contrast to population-based progression analyses, PoPLR provides a continuous estimate of statistical significance for visual field deterioration individualized to a particular patient's data. This allows close control over specificity, essential for monitoring patients in clinical practice and in clinical trials.

A Model Comparison for Count Data with a Positively Skewed Distribution with an Application to the Number of University Mathematics Courses Completed

ERIC Educational Resources Information Center

Liou, Pey-Yan

2009-01-01

The current study examines three regression models: OLS (ordinary least square) linear regression, Poisson regression, and negative binomial regression for analyzing count data. Simulation results show that the OLS regression model performed better than the others, since it did not produce more false statistically significant relationships than…

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Objectively assessed moderate-to-vigorous physical activity levels among primary school children in Norway: The Health Oriented Pedagogical Project (HOPP).

PubMed

Deng, Wei Hai; Fredriksen, Per Morten

2018-05-01

The objective was to investigate moderate-to-vigorous physical activity levels (MVPA) of primary school children at baseline of the Health Oriented Pedagogical Project (HOPP), Norway. Data on 2123 children aged 6-12 years were included for analysis (75% participation rate). Average minutes per day in MVPA was objectively measured using accelerometry based on seven-day averages. The sample was analysed for age-, sex-, socioeconomic-, and season-related patterns. A linear regression investigated the moderating effect of these factors as well as body mass index and waist circumference. Some 86.5% of the sample had at least 60 min/day MVPA, averaging 90.7 min/day. The main differences in daily averages were between age groups 6½-9 and 10-12 ( p < .05). Boys (95.8 min/day, 95% CI: 94.1-97.5) were more active than girls (85.6 min/day, 95% CI: 83.9-87.2) in all age groups ( p < .0001). MVPA was lower by 3.5 min ( p < .0001) per additional year of age in the linear regression (R 2 = 0.176) and was reduced by 20 min less per day in MVPA in the winter months compared with the summer months ( p < .0001). Physical activity levels are already in decline from 6-7 years old and are likely to continue to decline into adolescence. Interventions must therefore focus on primary school children.

Pulse Pressure, Instead of Brachium-Ankle Pulse Wave Velocity, is Associated with Reduced Kidney Function in a Chinese Han Population.

PubMed

Jia, Linpei; Zhang, Weiguang; Ma, Jie; Chen, Xizhao; Chen, Lei; Li, Zuoxiang; Cai, Guangyan; Huang, Jing; Zhang, Jinping; Bai, Xiaojuan; Feng, Zhe; Sun, Xuefeng; Chen, Xiangmei

2017-01-01

In this study, we aim to investigate the association between renal function and arterial stiffness in a Chinese Han population, and further to discuss the effects of smoking on renal function. We collected the data of the brachium-ankle pulse wave velocity (baPWV), blood pressure, blood chemistry and smoking status. Then, the multiple linear regression was done to explore the relationship between estimated glomerular filtration (eGFR) and baPWV. Further, the parameters were compared among the four groups divided according to the quartiles of baPWV. Finally, the baPWV, eGFR and albuminuria values were compared between smokers and non-smokers. baPWV is associated with eGFR in the correlation analysis and univariate linear regression model. After adjustment, the pulse pressure (PP) instead of baPWV showed a significant association with eGFR. Nevertheless, the eGFR values differed among the four baPWV groups; the baPWV values were significantly higher in the subjects at the CKD (eGFR<60 mL/min/1.73 m2) and the early CKD stage (eGFR60-80 mL/min/1.73 m2). The baPWV values and the ratio of proteinuria were significantly increased in smokers. PP but not baPWV is a predictor of declined renal function. Smokers have worse arterial stiffness and worse renal function. © 2017 The Author(s)Published by S. Karger AG, Basel.

Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

PubMed

Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

2017-10-01

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

Systemic acute phase proteins response in calves experimentally infected with Eimeria zuernii.

PubMed

Lassen, Brian; Bangoura, Berit; Lepik, Triin; Orro, Toomas

2015-09-15

Acute phase proteins (APPs) have been demonstrated to be useful in evaluating general health stress and diseases in cattle. Serum amyloid A (SAA) and haptoglobin (Hp) are APPs that are produced during inflammation, and likely play a role in host immunological defence against Eimeria infection and the associated intestinal tissue damage. We investigated the involvement of SAA and HP in an experimental study, including three groups of calves: a control group (group 0, n=11), and two groups infected with either 150,000 or 250,000 Eimeria zuernii oocysts (group 1 (n=11) and group 2 (n=12), respectively). The calves were monitored for 28 days and data was collected on oocyst excretion, faecal score, animal weight, and SAA and Hp serum concentrations. Generalized linear mixed models showed that the clinical symptoms, indicated by an increase in the number of oocysts in the faeces and severe diarrhoea, manifested at patency for group 1 and 2. Serum Hp and SAA levels also increased during this period. Hp appeared to be a more sensitive marker than SAA, and differences between groups 1 and 2 were observed only for Hp. Linear regression models showed a negative association between weight gain and Hp concentrations, calculated as the area under the curve (AUC) during the overall experimental period and the patency period. A similar result was seen for SAA only during the patency period. This result supports the assumption that reduced weight gain due to E. zuernii infection is an immunologically driven process that involves an increase in APPs. A random intercept regression model of oocyst shedding groups showed that calves shedding 1-500 oocysts had reduced concentrations of Hp, indicating that a different immunological reaction occurs during mild shedding of E. zuernii oocysts than during more intensive shedding. A similar model was used to examine associations between faecal scores and Hp concentrations for each group. Group 2 calves with haemorrhagic diarrhoea displayed higher Hp levels than calves in that group with lower faecal scores, which may be in response to an increased demand for Hp in the repair process as a result of haemolysis. APPs seem to play an important role in determining the course of E. zuernii infection in calves, which may enhance our understanding of the immunological reaction and development of this disease. Copyright © 2015 Elsevier B.V. All rights reserved.

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

PubMed

Nachit, M M; Nachit, G; Ketata, H; Gauch, H G; Zobel, R W

1992-03-01

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

NASA Astrophysics Data System (ADS)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Orthogonal Projection in Teaching Regression and Financial Mathematics

ERIC Educational Resources Information Center

Kachapova, Farida; Kachapov, Ilias

2010-01-01

Two improvements in teaching linear regression are suggested. The first is to include the population regression model at the beginning of the topic. The second is to use a geometric approach: to interpret the regression estimate as an orthogonal projection and the estimation error as the distance (which is minimized by the projection). Linear…

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Effort-Reward Imbalance, Work-Privacy Conflict, and Burnout Among Hospital Employees.

PubMed

Häusler, Nadine; Bopp, Matthias; Hämmig, Oliver

2018-04-01

Studies investigating the relative importance of effort-reward imbalance and work-privacy conflict for burnout risk between professional groups in the health care sector are rare and analyses by educational attainment within professional groups are lacking. The study population consists of 1422 hospital employees in Switzerland. Multivariate linear regression analyses with standardized coefficients were performed for the overall study population and stratified for professional groups refined for educational attainment. Work-privacy conflict is a strong predictor for burnout and more strongly associated with burnout than effort-reward imbalance in the overall study population and across all professional groups. Effort-reward imbalance only had a minor effect on burnout in tertiary-educated medical professionals. Interventions aiming at increasing the compatibility of work and private life may substantially help to decrease burnout risk of professionals working in a health care setting.

The effects of guided inquiry instruction on student achievement in high school biology

NASA Astrophysics Data System (ADS)

Vass, Laszlo

The purpose of this quantitative, quasi-experimental study was to measure the effect of a student-centered instructional method called guided inquiry on the achievement of students in a unit of study in high school biology. The study used a non-random sample of 109 students, the control group of 55 students enrolled in high school one, received teacher centered instruction while the experimental group of 54 students enrolled at high school two received student-centered, guided inquiry instruction. The pretest-posttest design of the study analyzed scores using an independent t-test, a dependent t-test (p = <.001), an ANCOVA (p = .007), mixed method ANOVA (p = .024) and hierarchical linear regression (p = <.001). The experimental group that received guided inquiry instruction had statistically significantly higher achievement than the control group.

A quality control method for intensity-modulated radiation therapy planning based on generalized equivalent uniform dose.

PubMed

Pang, Haowen; Sun, Xiaoyang; Yang, Bo; Wu, Jingbo

2018-05-01

To ensure good quality intensity-modulated radiation therapy (IMRT) planning, we proposed the use of a quality control method based on generalized equivalent uniform dose (gEUD) that predicts absorbed radiation doses in organs at risk (OAR). We conducted a retrospective analysis of patients who underwent IMRT for the treatment of cervical carcinoma, nasopharyngeal carcinoma (NPC), or non-small cell lung cancer (NSCLC). IMRT plans were randomly divided into data acquisition and data verification groups. OAR in the data acquisition group for cervical carcinoma and NPC were further classified as sub-organs at risk (sOAR). The normalized volume of sOAR and normalized gEUD (a = 1) were analyzed using multiple linear regression to establish a fitting formula. For NSCLC, the normalized intersection volume of the planning target volume (PTV) and lung, the maximum diameter of the PTV (left-right, anterior-posterior, and superior-inferior), and the normalized gEUD (a = 1) were analyzed using multiple linear regression to establish a fitting formula for the lung gEUD (a = 1). The r-squared and P values indicated that the fitting formula was a good fit. In the data verification group, IMRT plans verified the accuracy of the fitting formula, and compared the gEUD (a = 1) for each OAR between the subjective method and the gEUD-based method. In conclusion, the gEUD-based method can be used effectively for quality control and can reduce the influence of subjective factors on IMRT planning optimization. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Associations of Genetically Determined Continental Ancestry with CD4+ Count and Plasma HIV-1 RNA beyond Self-Reported Race and Ethnicity

PubMed Central

Brummel, Sean S; Singh, Kumud K; Maihofer, Adam X.; Farhad, Mona; Qin, Min; Fenton, Terry; Nievergelt, Caroline M.; Spector, Stephen A.

2015-01-01

Background Ancestry informative markers (AIMs) measure genetic admixtures within an individual beyond self-reported racial/ethnic (SRR) groups. Here, we used genetically determined ancestry (GDA) across SRR groups and examine associations between GDA and HIV-1 RNA and CD4+ counts in HIV-positive children in the US. Methods 41 AIMs, developed to distinguish 7 continental regions, were detected by real-time-PCR in 994 HIV-positive, antiretroviral naïve children. GDA was estimated comparing each individual’s genotypes to allele frequencies found in a large set of reference individuals originating from global populations using STRUCTURE. The means of GDA were calculated for each category of SRR. Linear regression was used to model GDA on CD4+ count and log10 RNA, adjusting for SRR and age. Results Subjects were 61% Black, 25% Hispanic, 13% White and 1.3% Unknown. The mean age was 2.3 years (45% male), mean CD4+ count 981 cells/mm3, and mean log10 RNA 5.11. Marked heterogeneity was found for all SRR groups with high admixture for Hispanics. In adjusted linear regression models, subjects with 100% European ancestry were estimated to have 0.33 higher log10 RNA levels (95% CI: (0.03, 0.62), p=0.028) and 253 CD4+ cells /mm3 lower (95% CI: (−517, 11), p = 0.06) in CD4+ count, compared to subjects with 100% African ancestry. Conclusion Marked continental admixture was found among this cohort of HIV-infected children from the US. GDA contributed to differences in RNA and CD4+ counts beyond SRR, and should be considered when outcomes associated with HIV infection are likely to have a genetic component. PMID:26536313

Associations of Genetically Determined Continental Ancestry With CD4+ Count and Plasma HIV-1 RNA Beyond Self-Reported Race and Ethnicity.

PubMed

Brummel, Sean S; Singh, Kumud K; Maihofer, Adam X; Farhad, Mona; Qin, Min; Fenton, Terry; Nievergelt, Caroline M; Spector, Stephen A

2016-04-15

Ancestry informative markers (AIMs) measure genetic admixtures within an individual beyond self-reported racial/ethnic (SRR) groups. Here, we used genetically determined ancestry (GDA) across SRR groups and examine associations between GDA and HIV-1 RNA and CD4 counts in HIV-positive children in the United States. Forty-one AIMs, developed to distinguish 7 continental regions, were detected by real-time PCR in 994 HIV-positive, antiretroviral naive children. GDA was estimated comparing each individual's genotypes to allele frequencies found in a large set of reference individuals originating from global populations using STRUCTURE. The means of GDA were calculated for each category of SRR. Linear regression was used to model GDA on CD4 count and log10 RNA, adjusting for SRR and age. Subjects were 61% black, 25% Hispanic, 13% white, and 1.3% Unknown. The mean age was 2.3 years (45% male), mean CD4 count of 981 cells per cubic millimeter, and mean log10 RNA of 5.11. Marked heterogeneity was found for all SRR groups with high admixture for Hispanics. In adjusted linear regression models, subjects with 100% European ancestry were estimated to have 0.33 higher log10 RNA levels (95% CI: 0.03 to 0.62, P = 0.028) and 253 CD4 cells per cubic millimeter lower (95% CI: -517 to 11, P = 0.06) in CD4 count, compared to subjects with 100% African ancestry. Marked continental admixture was found among this cohort of HIV-infected children from the United States. GDA contributed to differences in RNA and CD4 counts beyond SRR and should be considered when outcomes associated with HIV infection are likely to have a genetic component.

Clinically Practical Approach for Screening of Low Muscularity Using Electronic Linear Measures on Computed Tomography Images in Critically Ill Patients.

PubMed

Avrutin, Egor; Moisey, Lesley L; Zhang, Roselyn; Khattab, Jenna; Todd, Emma; Premji, Tahira; Kozar, Rosemary; Heyland, Daren K; Mourtzakis, Marina

2017-12-06

Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass. We analyzed 145 scans of elderly ICU patients (≥65 years old) using a combination of measures obtained with a digital ruler, commonly found on hospital radiological software. The psoas and paraspinal muscle groups at the level of the third lumbar vertebra (L3) were evaluated by using 2 linear measures each and compared with an established method of CT image analysis of total muscle CSA in the L3 region. There was a strong association between linear measures of psoas and paraspinal muscle groups and total L3 muscle CSA (R 2 = 0.745, P < 0.001). Linear measures, age, and sex were included as covariates in a multiple logistic regression to predict those with low muscle mass; receiver operating characteristic (ROC) area under the curve (AUC) of the combined psoas and paraspinal linear index model was 0.920. Intraclass correlation coefficients (ICCs) were used to evaluate intrarater and interrater reliability, resulting in scores of 0.979 (95% CI: 0.940-0.992) and 0.937 (95% CI: 0.828-0.978), respectively. A digital ruler can reliably predict L3 muscle CSA, and these linear measures may be used to identify critically ill patients with low muscularity who are at risk for worse clinical outcomes. © 2017 American Society for Parenteral and Enteral Nutrition.

Analysis of Learning Curve Fitting Techniques.

DTIC Science & Technology

1987-09-01

1986. 15. Neter, John and others. Applied Linear Regression Models. Homewood IL: Irwin, 19-33. 16. SAS User’s Guide: Basics, Version 5 Edition. SAS... Linear Regression Techniques (15:23-52). Random errors are assumed to be normally distributed when using -# ordinary least-squares, according to Johnston...lot estimated by the improvement curve formula. For a more detailed explanation of the ordinary least-squares technique, see Neter, et. al., Applied

On vertical profile of ozone at Syowa

NASA Technical Reports Server (NTRS)

Chubachi, Shigeru

1994-01-01

The difference in the vertical ozone profile at Syowa between 1966-1981 and 1982-1988 is shown. The month-height cross section of the slope of the linear regressions between ozone partial pressure and 100-mb temperature is also shown. The vertically integrated values of the slopes are in close agreement with the slopes calculated by linear regression of Dobson total ozone on 100-mb temperature in the period of 1982-1988.

Association between breastfeeding duration, non-nutritive sucking habits and dental arch dimensions in deciduous dentition: a cross-sectional study.

PubMed

Agarwal, Shiv Shankar; Nehra, Karan; Sharma, Mohit; Jayan, Balakrishna; Poonia, Anish; Bhattal, Hiteshwar

2014-10-31

This cross-sectional retrospective study was conducted to determine association between breastfeeding duration, non-nutritive sucking habits, dental arch transverse diameters, posterior crossbite and anterior open bite in deciduous dentition. 415 children (228 males and 187 females), 4 to 6 years old, from a mixed Indian population were clinically examined. Based on written questionnaire answered by parents, children were divided into two groups: group 1 (breastfed for <6 months (n = 158)) and group 2 (breastfed for ≥6 months (n = 257)). The associations were analysed using chi-square test (P < 0.05 taken as statistically significant). Odds ratio (OR) was calculated to determine the strength of associations tested. Multivariate logistic regression analysis was done for obtaining independent predictors of posterior crossbite and maxillary and mandibular IMD (Inter-molar distance) and ICD (Inter-canine distance). Non-nutritive sucking (NNS) was present in 15.18% children (20.3% in group 1 as compared to 12.1% in group 2 (P = 0.024)). The average ICD and IMD in maxilla and average IMD in mandible were significantly higher among group 2 as compared to group 1 (P < 0.01). In mandible, average ICD did not differ significantly between the two groups (P = 0.342). The distribution of anterior open bite did not differ significantly between the two groups (P = 0.865). The distribution of posterior crossbite was significantly different between the two groups (P = 0.001). OR assessment (OR = 1.852) revealed that group 1 had almost twofold higher prevalence of NNS habits than group 2. Multivariate logistic regression analysis revealed that the first group had independently fourfold increased risk of developing crossbite compared to the second group (OR = 4.3). Multivariate linear regression analysis also revealed that age and breastfeeding duration were the most significant determinants of ICD and IMD. An increased prevalence of NNS in the first group suggests that NNS is a dominant variable in the association between breastfeeding duration and reduced intra-arch transverse diameters which leads to increased prevalence of posterior crossbites as seen in our study. Mandibular inter-canine width is however unaffected due to a lowered tongue posture seen in these children.

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Random regression analyses using B-spline functions to model growth of Nellore cattle.

PubMed

Boligon, A A; Mercadante, M E Z; Lôbo, R B; Baldi, F; Albuquerque, L G

2012-02-01

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Use of mental health services by Asian Americans.

PubMed

Barreto, Rose M; Segal, Steven P

2005-06-01

This study explored the use of mental health services by Asian Americans and other ethnic populations (N=104,773) in California. The authors used linear regression analyses to assess the role of ethnicity and diagnosis in predicting six-month use of services. East Asians used more services than Southeast Asians, Filipinos, other Asians, Caucasians, African Americans, Latinos, and Native Americans, even when severity of illness was taken into account. The findings suggest that aggregating Asian subpopulations into a single group in services research is no longer appropriate. Attention needs to be placed on the needs of Southeast Asians and other Asians, whose service use patterns approximate those of the traditionally most underserved groups, African Americans and Latinos.

MEDICAL vs. MEDICAL AND SURGICAL TREATMENT FOR BRUCELLA ENDOCARDITIS: A REVIEW OF THE LITERATURE

PubMed Central

Keshtkar-Jahromi, Maryam; Razavi, Seyed-Mostafa; Gholamin, Sharareh; Keshtkar-Jahromi, Marzieh; Hossain, Mian; Sajadi, Mohammad

2012-01-01

This review was undertaken to determine the role of surgery in the treatment of brucella endocarditis. All English and French articles reporting brucella endocarditis (1966–2011) in Pubmed, Google and Scopus were reviewed. 308 cases were identified and Linear and Logistic regression was performed. Surgery improved outcomes by decreasing mortality from 32.7% in the medical treatment only group to 6.7% in the combined surgical and medical treatment group (p<.001). This association was still significant while controlling for other contributing factors. In the absence of a controlled trial, we recommend the utmost vigilance and consideration of surgical management in treating such patients. PMID:23102495

Nonlinear isochrones in murine left ventricular pressure-volume loops: how well does the time-varying elastance concept hold?

PubMed

Claessens, T E; Georgakopoulos, D; Afanasyeva, M; Vermeersch, S J; Millar, H D; Stergiopulos, N; Westerhof, N; Verdonck, P R; Segers, P

2006-04-01

The linear time-varying elastance theory is frequently used to describe the change in ventricular stiffness during the cardiac cycle. The concept assumes that all isochrones (i.e., curves that connect pressure-volume data occurring at the same time) are linear and have a common volume intercept. Of specific interest is the steepest isochrone, the end-systolic pressure-volume relationship (ESPVR), of which the slope serves as an index for cardiac contractile function. Pressure-volume measurements, achieved with a combined pressure-conductance catheter in the left ventricle of 13 open-chest anesthetized mice, showed a marked curvilinearity of the isochrones. We therefore analyzed the shape of the isochrones by using six regression algorithms (two linear, two quadratic, and two logarithmic, each with a fixed or time-varying intercept) and discussed the consequences for the elastance concept. Our main observations were 1) the volume intercept varies considerably with time; 2) isochrones are equally well described by using quadratic or logarithmic regression; 3) linear regression with a fixed intercept shows poor correlation (R(2) < 0.75) during isovolumic relaxation and early filling; and 4) logarithmic regression is superior in estimating the fixed volume intercept of the ESPVR. In conclusion, the linear time-varying elastance fails to provide a sufficiently robust model to account for changes in pressure and volume during the cardiac cycle in the mouse ventricle. A new framework accounting for the nonlinear shape of the isochrones needs to be developed.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

PubMed

Lopes, Marta B; Calado, Cecília R C; Figueiredo, Mário A T; Bioucas-Dias, José M

2017-06-01

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Application of General Regression Neural Network to the Prediction of LOD Change

NASA Astrophysics Data System (ADS)

Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao

2012-01-01

Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.

Relation of ABO blood groups to the severity of coronary atherosclerosis: an Gensini score assessment.

PubMed

Gong, Ping; Luo, Song-Hui; Li, Xiao-Lin; Guo, Yuan-Lin; Zhu, Cheng-Gang; Xu, Rui-Xia; Li, Sha; Dong, Qian; Liu, Geng; Chen, Juan; Zeng, Rui-Xiang; Li, Jian-Jun

2014-12-01

Although the study on the relationship between ABO blood groups and coronary atherosclerosis has a long history, few data is available regarding ABO to severity of coronary atherosclerosis in a large cohort study. Therefore, the present study aimed to investigate the relation of the ABO blood groups to the severity of coronary atherosclerosis assessed by Gensini score (GS) in a large Chinese cohort undergoing coronary angiography. A total of 2919 consecutive patients undergoing coronary angiography were enrolled, and their baseline characteristics and ABO blood groups were collected. The GS was calculated as 1st tertile (0-10), 2nd tertile (11-36), 3rd tertile (>36) according to angiographic results. The relation of the ABO blood groups to GS was investigated. The frequency of blood group A was significantly higher in the upper GS tertiles (24.4% vs. 28.2% vs. 29.5%, p = 0.032). Multivariable linear regression analysis revealed that blood group A was independently associated with GS (β = 0.043, p = 0.017). Likewise, multivariable logistic regression analysis showed that group A remained significantly associated with mid-high GS (OR = 1.44, 95% CI 1.16-1.80, p = 0.001), and the group O was showed as a protective factor (OR = 0.77, 95% CI = 0.65-0.92, p = 0.004). In this large Chinese cohort study, the data indicated that there was an association between ABO blood groups and the severity of coronary atherosclerosis. Moreover, the blood group A was an independent risk factor for serious coronary atherosclerosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Visuoconstructional Impairment in Subtypes of Mild Cognitive Impairment

PubMed Central

Ahmed, Samrah; Brennan, Laura; Eppig, Joel; Price, Catherine C.; Lamar, Melissa; Delano-Wood, Lisa; Bangen, Katherine J.; Edmonds, Emily C.; Clark, Lindsey; Nation, Daniel A.; Jak, Amy; Au, Rhoda; Swenson, Rodney; Bondi, Mark W.; Libon, David J.

2018-01-01

Clock Drawing Test performance was examined alongside other neuropsychological tests in mild cognitive impairment (MCI). We tested the hypothesis that clock-drawing errors are related to executive impairment. The current research examined 86 patients with MCI for whom, in prior research, cluster analysis was used to sort patients into dysexecutive (dMCI, n=22), amnestic (aMCI, n=13), and multi-domain (mMCI, n=51) subtypes. First, principal components analysis (PCA) and linear regression examined relations between clock-drawing errors and neuropsychological test performance independent of MCI subtype. Second, between-group differences were assessed with analysis of variance (ANOVA) where MCI subgroups were compared to normal controls (NC). PCA yielded a 3-group solution. Contrary to expectations, clock-drawing errors loaded with lower performance on naming/lexical retrieval, rather than with executive tests. Regression analyses found increasing clock-drawing errors to command were associated with worse performance only on naming/lexical retrieval tests. ANOVAs revealed no differences in clock-drawing errors between dMCI versus mMCI or aMCI versus NCs. Both the dMCI and mMCI groups generated more clock-drawing errors than the aMCI and NC groups in the command condition. In MCI, language-related skills contribute to clock-drawing impairment. PMID:26397732

E-cigarette Dual Users, Exclusive Users and Perceptions of Tobacco Products

PubMed Central

Cooper, Maria; Case, Kathleen R.; Loukas, Alexandra; Creamer, MeLisa R.; Perry, Cheryl L.

2016-01-01

Objectives We examined differences in the characteristics of youth non-users, cigarette-only, e-cigarette-only, and dual e-cigarette and cigarette users. Methods Using weighted, representative data, logistic regression analyses were conducted to examine differences in demographic characteristics and tobacco use behaviors across tobacco usage groups. Multiple linear regression analyses were conducted to examine differences in harm perceptions of various tobacco products and perceived peer use of e-cigarettes by tobacco usage group. Results Compared to non-users, dual users were more likely to be white, male, and high school students. Dual users had significantly higher prevalence of current use of all products (except hookah) than e-cigarette-only users, and higher prevalence of current use of snus and hookah than the cigarette-only group. Dual users had significantly lower harm perceptions for all tobacco products except for e-cigarettes and hookah as compared to e-cigarette-only users. Dual users reported higher peer use of cigarettes as compared to both exclusive user groups. Conclusion Findings highlight dual users’ higher prevalence of use of most other tobacco products, their lower harm perceptions of most tobacco products compared to e-cigarette-only users, and their higher perceived peer use of cigarettes compared to exclusive users. PMID:26685819

The difference engine: a model of diversity in speeded cognition.

PubMed

Myerson, Joel; Hale, Sandra; Zheng, Yingye; Jenkins, Lisa; Widaman, Keith F

2003-06-01

A theory of diversity in speeded cognition, the difference engine, is proposed, in which information processing is represented as a series of generic computational steps. Some individuals tend to perform all of these computations relatively quickly and other individuals tend to perform them all relatively slowly, reflecting the existence of a general cognitive speed factor, but the time required for response selection and execution is assumed to be independent of cognitive speed. The difference engine correctly predicts the positively accelerated form of the relation between diversity of performance, as measured by the standard deviation for the group, and task difficulty, as indexed by the mean response time (RT) for the group. In addition, the difference engine correctly predicts approximately linear relations between the RTs of any individual and average performance for the group, with the regression lines for fast individuals having slopes less than 1.0 (and positive intercepts) and the regression lines for slow individuals having slopes greater than 1.0 (and negative intercepts). Similar predictions are made for comparisons of slow, average, and fast subgroups, regardless of whether those subgroups are formed on the basis of differences in ability, age, or health status. These predictions are consistent with evidence from studies of healthy young and older adults as well as from studies of depressed and age-matched control groups.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Functional capacity following univentricular repair--midterm outcome.

PubMed

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

2012-01-01

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

Optimum pelvic incidence minus lumbar lordosis value can be determined by individual pelvic incidence.

PubMed

Inami, Satoshi; Moridaira, Hiroshi; Takeuchi, Daisaku; Shiba, Yo; Nohara, Yutaka; Taneichi, Hiroshi

2016-11-01

Adult spinal deformity (ASD) classification showing that ideal pelvic incidence minus lumbar lordosis (PI-LL) value is within 10° has been received widely. But no study has focused on the optimum level of PI-LL value that reflects wide variety in PI among patients. This study was conducted to determine the optimum PI-LL value specific to an individual's PI in postoperative ASD patients. 48 postoperative ASD patients were recruited. Spino-pelvic parameters and Oswestry Disability Index (ODI) were measured at the final follow-up. Factors associated with good clinical results were determined by stepwise multiple regression model using the ODI. The patients with ODI under the 75th percentile cutoff were designated into the "good" health related quality of life (HRQOL) group. In this group, the relationship between the PI-LL and PI was assessed by regression analysis. Multiple regression analysis revealed PI-LL as significant parameters associated with ODI. Thirty-six patients with an ODI <22 points (75th percentile cutoff) were categorized into a good HRQOL group, and linear regression models demonstrated the following equation: PI-LL = 0.41PI-11.12 (r = 0.45, P = 0.0059). On the basis of this equation, in the patients with a PI = 50°, the PI-LL is 9°. Whereas in those with a PI = 30°, the optimum PI-LL is calculated to be as low as 1°. In those with a PI = 80°, PI-LL is estimated at 22°. Consequently, an optimum PI-LL is inconsistent in that it depends on the individual PI.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

40 CFR 1066.220 - Linearity verification for chassis dynamometer systems.

Code of Federal Regulations, 2014 CFR

2014-07-01

... dynamometer speed and torque at least as frequently as indicated in Table 1 of § 1066.215. The intent of... linear regression and the linearity criteria specified in Table 1 of this section. (b) Performance requirements. If a measurement system does not meet the applicable linearity criteria in Table 1 of this...

A Learning Progression Should Address Regression: Insights from Developing Non-Linear Reasoning in Ecology

ERIC Educational Resources Information Center

Hovardas, Tasos

2016-01-01

Although ecological systems at varying scales involve non-linear interactions, learners insist thinking in a linear fashion when they deal with ecological phenomena. The overall objective of the present contribution was to propose a hypothetical learning progression for developing non-linear reasoning in prey-predator systems and to provide…

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

ERIC Educational Resources Information Center

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

Estimating normative limits of Heidelberg Retina Tomograph optic disc rim area with quantile regression.

PubMed

Artes, Paul H; Crabb, David P

2010-01-01

To investigate why the specificity of the Moorfields Regression Analysis (MRA) of the Heidelberg Retina Tomograph (HRT) varies with disc size, and to derive accurate normative limits for neuroretinal rim area to address this problem. Two datasets from healthy subjects (Manchester, UK, n = 88; Halifax, Nova Scotia, Canada, n = 75) were used to investigate the physiological relationship between the optic disc and neuroretinal rim area. Normative limits for rim area were derived by quantile regression (QR) and compared with those of the MRA (derived by linear regression). Logistic regression analyses were performed to quantify the association between disc size and positive classifications with the MRA, as well as with the QR-derived normative limits. In both datasets, the specificity of the MRA depended on optic disc size. The odds of observing a borderline or outside-normal-limits classification increased by approximately 10% for each 0.1 mm(2) increase in disc area (P < 0.1). The lower specificity of the MRA with large optic discs could be explained by the failure of linear regression to model the extremes of the rim area distribution (observations far from the mean). In comparison, the normative limits predicted by QR were larger for smaller discs (less specific, more sensitive), and smaller for larger discs, such that false-positive rates became independent of optic disc size. Normative limits derived by quantile regression appear to remove the size-dependence of specificity with the MRA. Because quantile regression does not rely on the restrictive assumptions of standard linear regression, it may be a more appropriate method for establishing normative limits in other clinical applications where the underlying distributions are nonnormal or have nonconstant variance.

Does physical exposure throughout working life influence chair-rise performance in midlife? A retrospective cohort study of associations between work and physical function in Denmark.

PubMed

Møller, Anne; Reventlow, Susanne; Hansen, Åse Marie; Andersen, Lars L; Siersma, Volkert; Lund, Rikke; Avlund, Kirsten; Andersen, Johan Hviid; Mortensen, Ole Steen

2015-11-04

Our aim was to study associations between physical exposures throughout working life and physical function measured as chair-rise performance in midlife. The Copenhagen Aging and Midlife Biobank (CAMB) provided data about employment and measures of physical function. Individual job histories were assigned exposures from a job exposure matrix. Exposures were standardised to ton-years (lifting 1000 kg each day in 1 year), stand-years (standing/walking for 6 h each day in 1 year) and kneel-years (kneeling for 1 h each day in 1 year). The associations between exposure-years and chair-rise performance (number of chair-rises in 30 s) were analysed in multivariate linear and non-linear regression models adjusted for covariates. Mean age among the 5095 participants was 59 years in both genders, and, on average, men achieved 21.58 (SD=5.60) and women 20.38 (SD=5.33) chair-rises in 30 s. Physical exposures were associated with poorer chair-rise performance in both men and women, however, only associations between lifting and standing/walking and chair-rise remained statistically significant among men in the final model. Spline regression analyses showed non-linear associations and confirmed the findings. Higher physical exposure throughout working life is associated with slightly poorer chair-rise performance. The associations between exposure and outcome were non-linear. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Indole 3-acetic acid, indoxyl sulfate and paracresyl-sulfate do not influence anemia parameters in hemodialysis patients.

PubMed

Bataille, Stanislas; Pelletier, Marion; Sallée, Marion; Berland, Yvon; McKay, Nathalie; Duval, Ariane; Gentile, Stéphanie; Mouelhi, Yosra; Brunet, Philippe; Burtey, Stéphane

2017-07-26

The main reason for anemia in renal failure patients is the insufficient erythropoietin production by the kidneys. Beside erythropoietin deficiency, in vitro studies have incriminated uremic toxins in the pathophysiology of anemia but clinical data are sparse. In order to assess if indole 3-acetic acid (IAA), indoxyl sulfate (IS), and paracresyl sulfate (PCS) -three protein bound uremic toxins- are clinically implicated in end-stage renal disease anemia we studied the correlation between IAA, IS and PCS plasmatic concentrations with hemoglobin and Erythropoietin Stimulating Agents (ESA) use in hemodialysis patients. Between June and July 2014, we conducted an observational cross sectional study in two hemodialysis center. Three statistical approaches were conducted. First, we compared patients treated with ESA and those not treated. Second, we performed linear regression models between IAA, IS, and PCS plasma concentrations and hemoglobin, the ESA dose over hemoglobin ratio (ESA/Hemoglobin) or the ESA resistance index (ERI). Third, we used a polytomous logistic regression model to compare groups of patients with no/low/high ESA dose and low/high hemoglobin statuses. Overall, 240 patients were included in the study. Mean age ± SD was 67.6 ± 16.0 years, 55.4% were men and 42.5% had diabetes mellitus. When compared with ESA treated patients, patients with no ESA had higher hemoglobin (mean 11.4 ± 1.1 versus 10.6 ± 1.2 g/dL; p <0.001), higher transferrin saturation (TSAT, 31.1 ± 16.3% versus 23.1 ± 11.5%; p < 0.001), less frequently an IV iron prescription (52.1 versus 65.7%, p = 0.04) and were more frequently treated with hemodiafiltration (53.5 versus 36.7%). In univariate analysis, IAA, IS or PCS plasma concentrations did not differ between the two groups. In the linear model, IAA plasma concentration was not associated with hemoglobin, but was negatively associated with ESA/Hb (p = 0.02; R = 0.18) and with the ERI (p = 0.03; R = 0.17). IS was associated with none of the three anemia parameters. PCS was positively associated with hemoglobin (p = 0.03; R = 0.14), but negatively with ESA/Hb (p = 0.03; R = 0.17) and the ERI (p = 0.02; R = 0.19). In multivariate analysis, the association of IAA concentration with ESA/Hb or ERI was not statistically significant, neither was the association of PCS with ESA/Hb or ERI. Identically, in the subgroup of 76 patients with no inflammation (CRP <5 mg/L) and no iron deficiency (TSAT >20%) linear regression between IAA, IS or PCS and any anemia parameter did not reach significance. In the third model, univariate analysis showed no intergroup significant differences for IAA and IS. Regarding PCS, the Low Hb/High ESA group had lower concentrations. However, when we compared PCS with the other significant characteristics of the five groups to the Low Hb/high ESA (our reference group), the polytomous logistic regression model didn't show any significant difference for PCS. In our study, using three different statistical models, we were unable to show any correlation between IAA, IS and PCS plasmatic concentrations and any anemia parameter in hemodialysis patients. Indolic uremic toxins and PCS have no or a very low effect on anemia parameters.

Relationship between body fat and BMI in a US Hispanic population-based cohort study: Results from HCHS/SOL

PubMed Central

Wong, William W.; Strizich, Garrett; Heo, Moonseong; Heymsfield, Steven B.; Himes, John H.; Rock, Cheryl L.; Gellman, Marc D.; Siega-Riz, Anna Maria; Sotres-Alvarez, Daniela; Davis, Sonia M.; Arredondo, Elva M.; Van Horn, Linda; Wylie-Rosett, Judith; Sanchez-Johnsen, Lisa; Kaplan, Robert; Mossavar-Rahmani, Yasmin

2016-01-01

Objective To evaluate the percentage of body fat (%BF)-BMI relationship, identify %BF levels corresponding to adult BMI cut-points, and examine %BF-BMI agreement in a diverse Hispanic/Latino population. Methods %BF by bioelectrical impedance analysis (BIA) was corrected against %BF by 18O dilution in 476 participants of the ancillary Hispanic Community Health/Latinos Studies. Corrected %BF were regressed against 1/BMI in the parent study (n=15,261), fitting models for each age group, by sex and Hispanic/Latino background; predicted %BF was then computed for each BMI cut-point. Results BIA underestimated %BF by 8.7 ± 0.3% in women and 4.6 ± 0.3% in men (P < 0.0001). The %BF-BMI relationshp was non-linear and linear for 1/BMI. Sex- and age-specific regression parameters between %BF and 1/BMI were consistent across Hispanic/Latino backgrounds (P > 0.05). The precision of the %BF-1/BMI association weakened with increasing age in men but not women. The proportion of participants classified as non-obese by BMI but obese by %BF was generally higher among women and older adults (16.4% in women vs. 12.0% in men aged 50-74 y). Conclusions %BF was linearly related to 1/BMI with consistent relationship across Hispanic/Lation backgrounds. BMI cut-points consistently underestimated the proportion of Hispanics/Latinos with excess adiposity. PMID:27184359

Association of Dentine Hypersensitivity with Different Risk Factors – A Cross Sectional Study

PubMed Central

Vijaya, V; Sanjay, Venkataraam; Varghese, Rana K; Ravuri, Rajyalakshmi; Agarwal, Anil

2013-01-01

Background: This study was done to assess the prevalence of Dentine hypersensitivity (DH) and its associated risk factors. Materials & Methods: This epidemiological study was done among patients coming to dental college regarding prevalence of DH. A self structured questionnaire along with clinical examination was done for assessment. Descriptive statistics were obtained and frequency distribution was calculated using Chi square test at p value <0.05. Stepwise multiple linear regression was also done to access frequency of DH with different factors. Results: The study population was comprised of 655 participants with different age groups. Our study showed prevalence as 55% and it was more common among males. Similarly smokers and those who use hard tooth brush had more cases of DH. Step wise multiple linear regression showed that best predictor for DH was age followed by habit of smoking and type of tooth brush. Most aggravating factors were cold water (15.4%) and sweet foods (14.7%), whereas only 5% of the patients had it while brushing. Conclusion: A high level of dental hypersensitivity has been in this study and more common among males. A linear finding was shown with age, smoking and type of tooth brush. How to cite this article: Vijaya V, Sanjay V, Varghese RK, Ravuri R, Agarwal A. Association of Dentine Hypersensitivity with Different Risk Factors – A Cross Sectional Study. J Int Oral Health 2013;5(6):88-92 . PMID:24453451

Developmental changes in emotion recognition from full-light and point-light displays of body movement.

PubMed

Ross, Patrick D; Polson, Louise; Grosbras, Marie-Hélène

2012-01-01

To date, research on the development of emotion recognition has been dominated by studies on facial expression interpretation; very little is known about children's ability to recognize affective meaning from body movements. In the present study, we acquired simultaneous video and motion capture recordings of two actors portraying four basic emotions (Happiness Sadness, Fear and Anger). One hundred and seven primary and secondary school children (aged 4-17) and 14 adult volunteers participated in the study. Each participant viewed the full-light and point-light video clips and was asked to make a forced-choice as to which emotion was being portrayed. As a group, children performed worse than adults for both point-light and full-light conditions. Linear regression showed that both age and lighting condition were significant predictors of performance in children. Using piecewise regression, we found that a bilinear model with a steep improvement in performance until 8.5 years of age, followed by a much slower improvement rate through late childhood and adolescence best explained the data. These findings confirm that, like for facial expression, adolescents' recognition of basic emotions from body language is not fully mature and seems to follow a non-linear development. This is in line with observations of non-linear developmental trajectories for different aspects of human stimuli processing (voices and faces), perhaps suggesting a shift from one perceptual or cognitive strategy to another during adolescence. These results have important implications to understanding the maturation of social cognition.

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

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Correlation of Respirator Fit Measured on Human Subjects and a Static Advanced Headform

PubMed Central

Bergman, Michael S.; He, Xinjian; Joseph, Michael E.; Zhuang, Ziqing; Heimbuch, Brian K.; Shaffer, Ronald E.; Choe, Melanie; Wander, Joseph D.

2015-01-01

This study assessed the correlation of N95 filtering face-piece respirator (FFR) fit between a Static Advanced Headform (StAH) and 10 human test subjects. Quantitative fit evaluations were performed on test subjects who made three visits to the laboratory. On each visit, one fit evaluation was performed on eight different FFRs of various model/size variations. Additionally, subject breathing patterns were recorded. Each fit evaluation comprised three two-minute exercises: “Normal Breathing,” “Deep Breathing,” and again “Normal Breathing.” The overall test fit factors (FF) for human tests were recorded. The same respirator samples were later mounted on the StAH and the overall test manikin fit factors (MFF) were assessed utilizing the recorded human breathing patterns. Linear regression was performed on the mean log10-transformed FF and MFF values to assess the relationship between the values obtained from humans and the StAH. This is the first study to report a positive correlation of respirator fit between a headform and test subjects. The linear regression by respirator resulted in R2 = 0.95, indicating a strong linear correlation between FF and MFF. For all respirators the geometric mean (GM) FF values were consistently higher than those of the GM MFF. For 50% of respirators, GM FF and GM MFF values were significantly different between humans and the StAH. For data grouped by subject/respirator combinations, the linear regression resulted in R2 = 0.49. A weaker correlation (R2 = 0.11) was found using only data paired by subject/respirator combination where both the test subject and StAH had passed a real-time leak check before performing the fit evaluation. For six respirators, the difference in passing rates between the StAH and humans was < 20%, while two respirators showed a difference of 29% and 43%. For data by test subject, GM FF and GM MFF values were significantly different for 40% of the subjects. Overall, the advanced headform system has potential for assessing fit for some N95 FFR model/sizes. PMID:25265037

Age-related energy values of bakery meal for broiler chickens determined using the regression method.

PubMed

Stefanello, C; Vieira, S L; Xue, P; Ajuwon, K M; Adeola, O

2016-07-01

A study was conducted to determine the ileal digestible energy (IDE), ME, and MEn contents of bakery meal using the regression method and to evaluate whether the energy values are age-dependent in broiler chickens from zero to 21 d post hatching. Seven hundred and eighty male Ross 708 chicks were fed 3 experimental diets in which bakery meal was incorporated into a corn-soybean meal-based reference diet at zero, 100, or 200 g/kg by replacing the energy-yielding ingredients. A 3 × 3 factorial arrangement of 3 ages (1, 2, or 3 wk) and 3 dietary bakery meal levels were used. Birds were fed the same experimental diets in these 3 evaluated ages. Birds were grouped by weight into 10 replicates per treatment in a randomized complete block design. Apparent ileal digestibility and total tract retention of DM, N, and energy were calculated. Expression of mucin (MUC2), sodium-dependent phosphate transporter (NaPi-IIb), solute carrier family 7 (cationic amino acid transporter, Y(+) system, SLC7A2), glucose (GLUT2), and sodium-glucose linked transporter (SGLT1) genes were measured at each age in the jejunum by real-time PCR. Addition of bakery meal to the reference diet resulted in a linear decrease in retention of DM, N, and energy, and a quadratic reduction (P < 0.05) in N retention and ME. There was a linear increase in DM, N, and energy as birds' ages increased from 1 to 3 wk. Dietary bakery meal did not affect jejunal gene expression. Expression of genes encoding MUC2, NaPi-IIb, and SLC7A2 linearly increased (P < 0.05) with age. Regression-derived MEn of bakery meal linearly increased (P < 0.05) as the age of birds increased, with values of 2,710, 2,820, and 2,923 kcal/kg DM for 1, 2, and 3 wk, respectively. Based on these results, utilization of energy and nitrogen in the basal diet decreased when bakery meal was included and increased with age of broiler chickens. © 2016 Poultry Science Association Inc.

Parathyroid Hormone Levels and Cognition

NASA Technical Reports Server (NTRS)

Burnett, J.; Smith, S.M.; Aung, K.; Dyer, C.

2009-01-01

Hyperparathyroidism is a well-recognized cause of impaired cognition due to hypercalcemia. However, recent studies have suggested that perhaps parathyroid hormone itself plays a role in cognition, especially executive dysfunction. The purpose of this study was to explore the relationship of parathyroid hormone levels in a study cohort of elders with impaied cognition. Methods: Sixty community-living adults, 65 years of age and older, reported to Adult Protective Services for self-neglect and 55 controls matched (on age, ethnicity, gender and socio-economic status) consented and participated in this study. The research team conducted in-home comprehensive geriatric assessments which included the Mini-mental state exam (MMSE), the 15-item geriatric depression scale (GDS) , the Wolf-Klein clock test and a comprehensive nutritional panel, which included parathyroid hormone and ionized calcium. Students t tests and linear regression analyses were performed to assess for bivariate associations. Results: Self-neglecters (M = 73.73, sd=48.4) had significantly higher PTH levels compared to controls (M =47.59, sd=28.7; t=3.59, df=98.94, p<.01). There was no significant group difference in ionized calcium levels. Overall, PTH was correlated with the MMSE (r=-.323, p=.001). Individual regression analyses revealed a statistically significant correlation between PTH and MMSE in the self-neglect group (r=-.298, p=.024) and this remained significant after controlling for ionized calcium levels in the regression. No significant associations were revealed in the control group or among any of the other cognitive measures. Conclusion: Parathyroid hormone may be associated with cognitive performance.

Mental chronometry with simple linear regression.

PubMed

Chen, J Y

1997-10-01

Typically, mental chronometry is performed by means of introducing an independent variable postulated to affect selectively some stage of a presumed multistage process. However, the effect could be a global one that spreads proportionally over all stages of the process. Currently, there is no method to test this possibility although simple linear regression might serve the purpose. In the present study, the regression approach was tested with tasks (memory scanning and mental rotation) that involved a selective effect and with a task (word superiority effect) that involved a global effect, by the dominant theories. The results indicate (1) the manipulation of the size of a memory set or of angular disparity affects the intercept of the regression function that relates the times for memory scanning with different set sizes or for mental rotation with different angular disparities and (2) the manipulation of context affects the slope of the regression function that relates the times for detecting a target character under word and nonword conditions. These ratify the regression approach as a useful method for doing mental chronometry.

Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression.

PubMed

Ali, Faraz Mahmood; Kay, Richard; Finlay, Andrew Y; Piguet, Vincent; Kupfer, Joerg; Dalgard, Florence; Salek, M Sam

2017-11-01

The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Comparing a single case to a control group - Applying linear mixed effects models to repeated measures data.

PubMed

Huber, Stefan; Klein, Elise; Moeller, Korbinian; Willmes, Klaus

2015-10-01

In neuropsychological research, single-cases are often compared with a small control sample. Crawford and colleagues developed inferential methods (i.e., the modified t-test) for such a research design. In the present article, we suggest an extension of the methods of Crawford and colleagues employing linear mixed models (LMM). We first show that a t-test for the significance of a dummy coded predictor variable in a linear regression is equivalent to the modified t-test of Crawford and colleagues. As an extension to this idea, we then generalized the modified t-test to repeated measures data by using LMMs to compare the performance difference in two conditions observed in a single participant to that of a small control group. The performance of LMMs regarding Type I error rates and statistical power were tested based on Monte-Carlo simulations. We found that starting with about 15-20 participants in the control sample Type I error rates were close to the nominal Type I error rate using the Satterthwaite approximation for the degrees of freedom. Moreover, statistical power was acceptable. Therefore, we conclude that LMMs can be applied successfully to statistically evaluate performance differences between a single-case and a control sample. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

Comparison of the occlusal contact area of virtual models and actual models: a comparative in vitro study on Class I and Class II malocclusion models.

PubMed

Lee, Hyemin; Cha, Jooly; Chun, Youn-Sic; Kim, Minji

2018-06-19

The occlusal registration of virtual models taken by intraoral scanners sometimes shows patterns which seem much different from the patients' occlusion. Therefore, this study aims to evaluate the accuracy of virtual occlusion by comparing virtual occlusal contact area with actual occlusal contact area using a plaster model in vitro. Plaster dental models, 24 sets of Class I models and 20 sets of Class II models, were divided into a Molar, Premolar, and Anterior group. The occlusal contact areas calculated by the Prescale method and the virtual occlusion by scanning method were compared, and the ratio of the molar and incisor area were compared in order to find any particular tendencies. There was no significant difference between the Prescale results and the scanner results in both the molar and premolar groups (p = 0.083 and 0.053, respectively). On the other hand, there was a significant difference between the Prescale and the scanner results in the anterior group with the scanner results presenting overestimation of the occlusal contact points (p < 0.05). In Molars group, the regression analysis shows that the two variables express linear correlation and has a linear equation with a slope of 0.917. R 2 is 0.930. Groups of Premolars and Anteriors had a week linear relationship and greater dispersion. Difference between the actual and virtual occlusion revealed in the anterior portion, where overestimation was observed in the virtual model obtained from the scanning method. Nevertheless, molar and premolar areas showed relatively accurate occlusal contact area in the virtual model.

Distance correction system for localization based on linear regression and smoothing in ambient intelligence display.

PubMed

Kim, Dae-Hee; Choi, Jae-Hun; Lim, Myung-Eun; Park, Soo-Jun

2008-01-01

This paper suggests the method of correcting distance between an ambient intelligence display and a user based on linear regression and smoothing method, by which distance information of a user who approaches to the display can he accurately output even in an unanticipated condition using a passive infrared VIR) sensor and an ultrasonic device. The developed system consists of an ambient intelligence display and an ultrasonic transmitter, and a sensor gateway. Each module communicates with each other through RF (Radio frequency) communication. The ambient intelligence display includes an ultrasonic receiver and a PIR sensor for motion detection. In particular, this system selects and processes algorithms such as smoothing or linear regression for current input data processing dynamically through judgment process that is determined using the previous reliable data stored in a queue. In addition, we implemented GUI software with JAVA for real time location tracking and an ambient intelligence display.

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER’S DISEASE*

PubMed Central

Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G

2015-01-01

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM. PMID:26900412

Liquid electrolyte informatics using an exhaustive search with linear regression.

PubMed

Sodeyama, Keitaro; Igarashi, Yasuhiko; Nakayama, Tomofumi; Tateyama, Yoshitaka; Okada, Masato

2018-06-14

Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the "prediction accuracy" and "calculation cost" of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the "accuracy" and "cost" when the search of a huge amount of new materials was carried out.

Applicability of both dentist and patient perceptions of dentists' explanations to the evaluation of dentist-patient communication.

PubMed

Hamasaki, T; Soh, I; Takehara, T; Hagihara, A

2011-12-01

Very little is known about dentist-patient communicative behaviours in actual practice. This study evaluated dentist and patient perceptions of dentist-patient communication and patient outcome. The subjects were 171 dentist-patient pairs in Kitakyushu, Japan. Dentists and patients answered the same questionnaire items using the same response categories to evaluate dentist-patient communication. Based on the scores of patient and dentist perceptions with respect to dentist-patient communication, patient-dentist pairs were categorised into one of 3 groups. Data analyses used one-way ANOVA, multiple linear regression analysis, and multiple logistic regression analysis. We found that, with respect to dentist-patient communication, patients in the 'patient better' group (i.e., the patient's evaluation was more positive than the dentist's evaluation) were more likely to have a positive outcome (e.g., 'improvement of health and fear,' 'satisfaction with care') than those in the other two groups. Patients in the 'doctor better' group (i.e., the dentist's evaluation was the more positive) were more likely to have a negative outcome than those in the other two groups. A positive patient outcome is more likely when the patient's evaluation is better than a dentist's evaluation with respect to dentist-patient communicative behaviours. The method based on patient and dentist perceptions with respect to dentist-patient communication might be effective in evaluating dentist-patient communication.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560

Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.

Synthesis, spectral studies and antimicrobial activities of some 2-naphthyl pyrazoline derivatives

NASA Astrophysics Data System (ADS)

Sakthinathan, S. P.; Vanangamudi, G.; Thirunarayanan, G.

A series of 2-naphthyl pyrazolines were synthesized by the cyclization of 2-naphthyl chalcones and phenylhydrazine hydrochloride in the presence of sodium acetate. The yields of pyrazoline derivatives are more than 80%. The synthesized pyrazolines were characterized by their physical constants, IR, 1H, 13C and MS spectra. From the IR and NMR spectra the Cdbnd N (cm-1) stretches, the pyrazoline ring proton chemical shifts (ppm) of δ, Hb and Hc and also the carbon chemical shifts (ppm) of δCdbnd N are correlated with Hammett substituent constants, F and R, and Swain-Lupton's parameters using single and multi-regression analyses. From the results of linear regression analysis, the effect of substituents on the group frequencies has been predicted. The antimicrobial activities of all synthesized pyrazolines have been studied.

Machine learning in the string landscape

NASA Astrophysics Data System (ADS)

Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

2017-09-01

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

Linear mixed-effects modeling approach to FMRI group analysis

PubMed Central

Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.

2013-01-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. PMID:23376789

Linear mixed-effects modeling approach to FMRI group analysis.

PubMed

Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

2013-06-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. Published by Elsevier Inc.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.

PubMed

Kong, Shengchun; Nan, Bin

2014-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso

PubMed Central

Kong, Shengchun; Nan, Bin

2013-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328

The Elbow-EpiTrainer: a method of delivering graded resistance to the extensor carpi radialis brevis. Effectiveness of a prototype device in a healthy population.

PubMed

Navsaria, Rishi; Ryder, Dionne M; Lewis, Jeremy S; Alexander, Caroline M

2015-03-01

Tennis elbow or lateral epicondylopathy (LE) is experienced as the lateral elbow has a reported prevalence of 1.3%, with symptoms lasting up to 18 months. LE is most commonly attributed to tendinopathy involving the extensor carpi radialis brevis (ECRB) tendon. The aim of tendinopathy management is to alleviate symptoms and restore function that initially involves relative rest followed by progressive therapeutic exercise. To assess the effectiveness of two prototype exercises using commonly available clinical equipment to progressively increase resistance and activity of the ECRB. Eighteen healthy participants undertook two exercise progressions. Surface electromyography was used to record ECRB activity during the two progressions, involving eccentric exercises of the wrist extensors and elbow pronation exercises using a prototype device. The two progressions were assessed for their linearity of progression using repeated ANOVA and linear regression analysis. Five participants repeated the study to assess reliability. The exercise progressions led to an increase in ECRB electromyographic (EMG) activity (p<0.001). A select progression of exercises combining the two protocols increased EMG activity in a linear fashion (p<0.001). The ICC values indicated good reliability (ICC>0.7) between the first and second tests for five participants. Manipulation of resistance and leverage with the prototype exercises was effective in creating significant increases of ECRB normalised EMG activity in a linear manner that may, with future research, become useful to clinicians treating LE. In addition, between trial reliability for the device to generate a consistent load was acceptable. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Novel point estimation from a semiparametric ratio estimator (SPRE): long-term health outcomes from short-term linear data, with application to weight loss in obesity.

PubMed

Weissman-Miller, Deborah

2013-11-02

Point estimation is particularly important in predicting weight loss in individuals or small groups. In this analysis, a new health response function is based on a model of human response over time to estimate long-term health outcomes from a change point in short-term linear regression. This important estimation capability is addressed for small groups and single-subject designs in pilot studies for clinical trials, medical and therapeutic clinical practice. These estimations are based on a change point given by parameters derived from short-term participant data in ordinary least squares (OLS) regression. The development of the change point in initial OLS data and the point estimations are given in a new semiparametric ratio estimator (SPRE) model. The new response function is taken as a ratio of two-parameter Weibull distributions times a prior outcome value that steps estimated outcomes forward in time, where the shape and scale parameters are estimated at the change point. The Weibull distributions used in this ratio are derived from a Kelvin model in mechanics taken here to represent human beings. A distinct feature of the SPRE model in this article is that initial treatment response for a small group or a single subject is reflected in long-term response to treatment. This model is applied to weight loss in obesity in a secondary analysis of data from a classic weight loss study, which has been selected due to the dramatic increase in obesity in the United States over the past 20 years. A very small relative error of estimated to test data is shown for obesity treatment with the weight loss medication phentermine or placebo for the test dataset. An application of SPRE in clinical medicine or occupational therapy is to estimate long-term weight loss for a single subject or a small group near the beginning of treatment.

Deficient serum 25-hydroxyvitamin D is associated with an atherogenic lipid profile: The Very Large Database of Lipids (VLDL-3) study.

PubMed

Lupton, Joshua R; Faridi, Kamil F; Martin, Seth S; Sharma, Sristi; Kulkarni, Krishnaji; Jones, Steven R; Michos, Erin D

2016-01-01

Cross-sectional studies have found an association between deficiencies in serum vitamin D, as measured by 25-hydroxyvitamin D (25[OH]D), and an atherogenic lipid profile. These studies have focused on a limited panel of lipid values including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Our study examines the relationship between serum 25(OH)D and an extended lipid panel (Vertical Auto Profile) while controlling for age, gender, glycemic status, and kidney function. We used the Very Large Database of Lipids, which includes US adults clinically referred for analysis of their lipid profile from 2009 to 2011. Our study focused on 20,360 subjects who had data for lipids, 25(OH)D, age, gender, hemoglobin A1c, insulin, creatinine, and blood urea nitrogen. Subjects were split into groups based on serum 25(OH)D: deficient (<20 ng/mL), intermediate (≥ 20-30 ng/mL), and optimal (≥ 30 ng/mL). The deficient group was compared to the optimal group using multivariable linear regression. In multivariable-adjusted linear regression, deficient serum 25(OH)D was associated with significantly lower serum HDL-C (-5.1%) and higher total cholesterol (+9.4%), non-HDL-C (+15.4%), directly measured LDL-C (+13.5%), intermediate-density lipoprotein cholesterol (+23.7%), very low-density lipoprotein cholesterol (+19.0%), remnant lipoprotein cholesterol (+18.4%), and TG (+26.4%) when compared with the optimal group. Deficient serum 25(OH)D is associated with significantly lower HDL-C and higher directly measured LDL-C, intermediate-density lipoprotein cholesterol, very low-density lipoproteins cholesterol, remnant lipoprotein cholesterol, and TG. Future trials examining vitamin D supplementation and cardiovascular disease risk should consider using changes in an extended lipid panel as an additional outcome measurement. Copyright © 2016 National Lipid Association. Published by Elsevier Inc. All rights reserved.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

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

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

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

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

A low-fat dietary pattern and risk of metabolic syndrome in postmenopausal women: The Women’s Health Initiative

PubMed Central

Neuhouser, Marian L.; Howard, Barbara; Lu, Jingmin; Tinker, Lesley F.; Van Horn, Linda; Caan, Bette; Rohan, Thomas; Stefanick, Marcia L.; Thomson, Cynthia A.

2012-01-01

Objective Nutrition plays an important role in metabolic syndrome etiology. We examined whether the Women’s Health Initiative (WHI) Dietary Modification Trial influenced metabolic syndrome risk. Materials/Methods 48,835 postmenopausal women aged 50–79 years were randomized to a low-fat (20% energy from fat) diet (intervention) or usual diet (comparison) for a mean of 8.1 years. Blood pressure, waist circumference and fasting blood measures of glucose, HDL-cholesterol and triglycerides were measured on a subsample (n= 2816) at baseline and years 1, 3 and 6 post-randomization. Logistic regression estimated associations of the intervention with metabolic syndrome risk and use of cholesterol-lowering and hypertension medications. Multivariate linear regression tested associations between the intervention and metabolic syndrome components. Results At year 3, but not years 1 or 6, women in the intervention group (vs. comparison) had a non-statistically significant lower risk of metabolic syndrome (OR=0.83, 95% CI 0.59–1.18). Linear regression models simultaneously modeling the five metabolic syndrome components revealed significant associations of the intervention with metabolic syndrome at year 1 (p<0.0001), but not years 3 (p=0.19) and 6 (p=0.17). Analyses restricted to intervention-adherent participants strengthened associations at years 3 (p=0.05) and 6 (p=0.06). Cholesterol-lowering and hypertension medication use was 19% lower at year 1 for intervention vs. comparison group women (OR=0.81, 95% CI 0.60–1.09). Over the entire trial, fewer intervention vs. comparison participants used these medications (26.0% vs. 29.9%), although results were not statistically significant (p=0.89). Conclusions The WHI low-fat diet may influence metabolic syndrome risk and decrease use of hypertension and cholesterol-lowering medications. Findings have potential for meaningful clinical translation. PMID:22633601

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

PubMed

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical programming language and the Python program HeatMapWrapper [ https://doi.org/10.5281/zenodo.495163 ] for heat map generation.

Development of the Korean Adult Reading Test (KART) to estimate premorbid intelligence in dementia patients

PubMed Central

Seo, Eun Hyun; Han, Ji Young; Sohn, Bo Kyung; Byun, Min Soo; Lee, Jun Ho; Choe, Young Min; Ahn, Suzy; Woo, Jong Inn; Jun, Jongho; Lee, Dong Young

2017-01-01

We aimed to develop a word-reading test for Korean-speaking adults using irregularly pronounced words that would be useful for estimation of premorbid intelligence. A linguist who specialized in Korean phonology selected 94 words that have irregular relationship between orthography and phonology. Sixty cognitively normal elderly (CN) and 31 patients with Alzheimer’s disease (AD) were asked to read out loud the words and were administered the Wechsler Adult Intelligence Scale, 4th edition, Korean version (K-WAIS-IV). Among the 94 words, 50 words that did not show a significant difference between the CN and the AD group were selected and constituted the KART. Using the 30 CN calculation group (CNc), a linear regression equation was obtained in which the observed full-scale IQ (FSIQ) was regressed on the reading errors of the KART, where education was included as an additional variable. When the regressed equation computed from the CNc was applied to 30 CN individuals of the validation group (CNv), the predicted FSIQ adequately fit the observed FSIQ (R2 = 0.63). In addition, independent sample t-test showed that the KART-predicted IQs were not significantly different between the CNv and AD groups, whereas the performance of the AD group was significantly worse in the observed IQs. In addition, an extended validation of the KART was performed with a separate sample consisted of 84 CN, 56 elderly with mild cognitive impairment (MCI), and 43 AD patients who were administered comprehensive neuropsychological assessments in addition to the KART. When the equation obtained from the CNc was applied to the extended validation sample, the KART-predicted IQs of the AD, MCI and the CN groups did not significantly differ, whereas their current global cognition scores significantly differed between the groups. In conclusion, the results support the validity of KART-predicted IQ as an index of premorbid IQ in individuals with AD. PMID:28723964

Neonatal MRI is associated with future cognition and academic achievement in preterm children

PubMed Central

Spencer-Smith, Megan; Thompson, Deanne K.; Doyle, Lex W.; Inder, Terrie E.; Anderson, Peter J.; Klingberg, Torkel

2015-01-01

School-age children born preterm are particularly at risk for low mathematical achievement, associated with reduced working memory and number skills. Early identification of preterm children at risk for future impairments using brain markers might assist in referral for early intervention. This study aimed to examine the use of neonatal magnetic resonance imaging measures derived from automated methods (Jacobian maps from deformation-based morphometry; fractional anisotropy maps from diffusion tensor images) to predict skills important for mathematical achievement (working memory, early mathematical skills) at 5 and 7 years in a cohort of preterm children using both univariable (general linear model) and multivariable models (support vector regression). Participants were preterm children born <30 weeks’ gestational age and healthy control children born ≥37 weeks’ gestational age at the Royal Women’s Hospital in Melbourne, Australia between July 2001 and December 2003 and recruited into a prospective longitudinal cohort study. At term-equivalent age ( ±2 weeks) 224 preterm and 46 control infants were recruited for magnetic resonance imaging. Working memory and early mathematics skills were assessed at 5 years (n = 195 preterm; n = 40 controls) and 7 years (n = 197 preterm; n = 43 controls). In the preterm group, results identified localized regions around the insula and putamen in the neonatal Jacobian map that were positively associated with early mathematics at 5 and 7 years (both P < 0.05), even after covarying for important perinatal clinical factors using general linear model but not support vector regression. The neonatal Jacobian map showed the same trend for association with working memory at 7 years (models ranging from P = 0.07 to P = 0.05). Neonatal fractional anisotropy was positively associated with working memory and early mathematics at 5 years (both P < 0.001) even after covarying for clinical factors using support vector regression but not general linear model. These significant relationships were not observed in the control group. In summary, we identified, in the preterm brain, regions around the insula and putamen using neonatal deformation-based morphometry, and brain microstructural organization using neonatal diffusion tensor imaging, associated with skills important for childhood mathematical achievement. Results contribute to the growing evidence for the clinical utility of neonatal magnetic resonance imaging for early identification of preterm infants at risk for childhood cognitive and academic impairment. PMID:26329284

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

PubMed

Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A

2005-04-15

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.

The relationship between body mass index and gross motor development in children aged 3 to 5 years.

PubMed

Nervik, Deborah; Martin, Kathy; Rundquist, Peter; Cleland, Joshua

2011-01-01

To investigate the relationship between obesity and gross motor development in children who are developing typically and determine whether body mass index (BMI) predicts difficulty in gross motor skills. BMIs were calculated and gross motor skills examined in 50 children who were healthy aged 3 to 5 years using the Peabody Developmental Motor Scales, 2nd edition (PDMS-2). Pearson chi-square statistic and stepwise linear hierarchical regression were used for analysis. A total of 24% of the children were overweight/obese, whereas 76% were found not to be overweight/obese. Fifty-eight percent of the overweight/obese group scored below average on the PDMS-2 compared to 15% of the nonoverweight group. Association between BMI and gross motor quotients was identified with significance of less than 0.002. Regression results were nonsignificant with all 50 subjects, yet showed significance (P = 0.018) when an outlier was excluded. Children aged 3 to 5 years with high BMIs may have difficulty with their gross motor skills. Further research is needed.

Esthetic Assessment of the Effect of Gingival Exposure in the Smile of Patients with Unilateral and Bilateral Maxillary Incisor Agenesis.

PubMed

Pinho, Teresa; Bellot-Arcís, Carlos; Montiel-Company, José María; Neves, Manuel

2015-07-01

The aim of this study was to determine the dental esthetic perception of the smile of patients with maxillary lateral incisor agenesis (MLIA); the perceptions were examined pre- and post-treatment. Esthetic determinations were made with regard to the gingival exposure in the patients' smile by orthodontists, general dentists, and laypersons. Three hundred eighty one people (80 orthodontists, 181 general dentists, 120 laypersons) rated the attractiveness of the smile in four cases before and after treatment, comprising two cases with unilateral MLIA and contralateral microdontia and two with bilateral MLIA. For each case, the buccal photograph was adjusted using a computer to apply standard lips to create high, medium, and low smiles. A numeric scale was used to measure the esthetic rating perceived by the judges. The resulting arithmetic means were compared using an ANOVA test, a linear trend, and a Student's t-test, applying a significance level of p < 0.05. The predictive capability of the variables, unilateral, or bilateral MLIA, symmetry of the treatment, gingival exposure of the smile, group, and gender were assessed using a multivariable linear regression model. In the pre- and post-treatment cases, medium smile photographs received higher scores than the same cases with high or low smiles, with significant differences between them. In all cases, orthodontists were the least-tolerant evaluation group (assigning lowest scores), followed by general dentists. In a predictive linear regression model, bilateral MLIA was the more predictive variable in pretreatment cases. The gingival exposure of the smile was a predictive variable in post-treatment cases only. The medium-height smile was considered to be more attractive. In all cases, orthodontists gave the lowest scores, followed by general dentists. Laypersons and male evaluators gave the highest scores. Symmetrical treatments scored higher than asymmetrical treatments. The gingival exposure had a significant influence on the esthetic perception of smiles in post-treatment cases. © 2014 by the American College of Prosthodontists.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Interlaboratory comparison of Taq Nuclease Assays for the quantification of the toxic cyanobacteria Microcystis sp

PubMed Central

Schober, Eva; Werndl, Michael; Laakso, Kati; Korschineck, Irina; Sivonen, Kaarina; Kurmayer, Rainer

2011-01-01

Summary The application of quantitative real time PCR has been proposed for the quantification of toxic genotypes of cyanobacteria. We have compared the Taq Nuclease Assay (TNA) in quantifying the toxic cyanobacteria Microcystis sp. via the intergenic spacer region of the phycocyanin operon (PC) and mcyB indicative of the production of the toxic heptapeptide microcystin between three research groups employing three instruments (ABI7300, GeneAmp5700, ABI7500). The estimates of mcyB genotypes were compared using (i) DNA of a mcyB containing strain and a non-mcyB containing strain supplied in different mixtures across a low range of variation (0-10% of mcyB) and across a high range of variation (20-100%), and (ii) DNA from field samples containing Microcystis sp. For all three instruments highly significant linear regression curves between the proportion of the mcyB containing strain and the percentage of mcyB genotypes both within the low range and within the high range of mcyB variation were obtained. The regression curves derived from the three instruments differed in slope and within the high range of mcyB variation mcyB proportions were either underestimated (0-50%) or overestimated (0-72%). For field samples cell numbers estimated via both TNAs as well as mcyB proportions showed significant linear relationships between the instruments. For all instruments a linear relationship between the cell numbers estimated as PC genotypes and the cell numbers estimated as mcyB genotypes was observed. The proportions of mcyB varied from 2-28% and did not differ between the instruments. It is concluded that the TNA is able to provide quantitative estimates on mcyB genotype numbers that are reproducible between research groups and is useful to follow variation in mcyB genotype proportion occurring within weeks to months. PMID:17258828

Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S

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

PubMed

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

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

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors.

PubMed

Wu, Lingtao; Lord, Dominique

2017-05-01

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Objective structured assessment of technical skills evaluation of theoretical compared with hands-on training of shoulder dystocia management: a randomized controlled trial.

PubMed

Buerkle, Bernd; Pueth, Julia; Hefler, Lukas A; Tempfer-Bentz, Eva-Katrin; Tempfer, Clemens B

2012-10-01

To compare the skills of performing a shoulder dystocia management algorithm after hands-on training compared with demonstration. We randomized medical students to a 30-minute hands-on (group 1) and a 30-minute demonstration (group 2) training session teaching a standardized shoulder dystocia management scheme on a pelvic training model. Participants were tested with a 22-item Objective Structured Assessment of Technical Skills scoring system after training and 72 hours thereafter. Objective Structured Assessment of Technical Skills scores were the primary outcome. Performance time, self-assessment, confidence, and global rating scale were the secondary outcomes. Statistics were performed using Mann-Whitney U test, χ test, and multiple linear regression analysis. Two hundred three participants were randomized. Objective Structured Assessment of Technical Skills scores were significantly higher in group 1 (n=103) compared with group 2 (n=100) (17.95±3.14 compared with 15.67±3.18, respectively; P<.001). The secondary outcomes global rating scale (GRS; 10.94±2.71 compared with 8.57±2.61, respectively; P<.001), self-assessment (3.15±0.94 compared with 2.72±1.01; P=.002), and confidence (3.72±0.98 compared with 3.34±0.90, respectively; P=.005), but not performance time (3:19±0:48 minutes compared with 3:31±1:05 minutes; P=.1), were also significantly different, favoring group 1. After 72 hours, Objective Structured Assessment of Technical Skills scores were still significantly higher in group 1 (n=67) compared with group 2 (n=60) (18.17±2.76 compared with 14.98±3.03, respectively; P<.001) as were GRS (10.80±2.62 compared with 8.15±2.59; P<.001) and self assessment (SA; 3.44±0.87 compared with 2.95±0.94; P=.003). In a multiple linear regression analysis, group assignment (group 1 compared with 2; P<.001) and sex (P=.002) independently influenced Objective Structured Assessment of Technical Skills scores. Hands-on training helps to achieve a significant improvement of shoulder dystocia management on a pelvic training model. www.ClinicalTrials.gov, NCT01618565. I.

General anesthesia in orthognathic surgeries: does it affect horizontal jaw relations?

PubMed

Yaghmaei, Masoud; Ejlali, Masoud; Nikzad, Sekieneh; Sayyedi, Ashraf; Shafaeifard, Shahrouz; Pourdanesh, Fereydoun

2013-10-01

The aim of this study was to evaluate the influence of general anesthesia on centric jaw relation (CR) records of orthognathic surgical patients in different postural positions. Fifty patients undergoing orthognathic surgery at Taleghani Hospital (Tehran, Iran) in 2008 were prospectively studied. CR records were obtained in conscious patients in 2 different positions (upright and supine) 1 day before surgery and in the supine position under general anesthesia. The impressions were made and the corresponding casts were mounted on a semiadjustable articulator. Differences were measured to the nearest 0.10 mm using a caliper. Paired t test and a general linear regression model were used for statistical analysis. Fifty patients (27 women and 23 men; mean age, 22.5 ± 3.5 yr) were enrolled. Angle Class I (group I), Class II (group II), and Class III (group III) malocclusions were detected in 16% (n = 8), 54% (n = 27), and 30% (n = 15) of patients, respectively. Although mean changes were smaller than 2 mm, statistically significant differences were found by paired t test in all Angle classification groups. No significant differences were found between the supine and conscious and the supine and unconscious patient positions in groups I and III (P > .05). However, in group II, this difference was statistically significant (P = .001). Regarding the impact of anesthesia on CR records of patients with different Angle classes, this study showed a significant effect, particularly in group II. Assessment of the outcome of interest (difference between the supine and conscious and the upright and conscious positions) versus position after adjustment for Angle class using a general linear regression model showed that the difference was significant only for Angle class (β = +0.29; t = 3.05; P = .003). General anesthesia may not adversely affect the mandibular condylar position in orthognathic patients in a supine position compared with a supine and conscious position. However, among all study groups, group II showed more significant changes in CR records under general anesthesia. Oral and maxillofacial surgeons should be well aware of such changes in these particular positions and avoid possible mismanagement and potential complications. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Linear regression models for solvent accessibility prediction in proteins.

PubMed

Wagner, Michael; Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

2005-04-01

The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Using Linear Regression To Determine the Number of Factors To Retain in Factor Analysis and the Number of Issues To Retain in Delphi Studies and Other Surveys.

ERIC Educational Resources Information Center

Jurs, Stephen; And Others

The scree test and its linear regression technique are reviewed, and results of its use in factor analysis and Delphi data sets are described. The scree test was originally a visual approach for making judgments about eigenvalues, which considered the relationships of the eigenvalues to one another as well as their actual values. The graph that is…

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

PubMed

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA.

PubMed

Al-Hamdan, Ashraf Z; Albashaireh, Reem N; Al-Hamdan, Mohammad Z; Crosson, William L

2017-05-12

This study aimed to assess the association between exposure to fine particulate matter (PM 2.5 ) and respiratory system cancer incidence in the US population (n = 295,404,580) using a satellite-derived estimate of PM 2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM 2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM 2.5 concentrations and the age-adjusted RSC incidence rates for all groups (Males, Females, Whites, and Blacks) except for Asians and American Indians. The linear relationships between PM 2.5 and RSC incidence rate per 1 μg/m 3 PM 2.5 increase for Males, Females, Whites, Blacks, and all categories combined had slopes of, respectively, 7.02 (R 2 = 0.36), 2.14 (R 2 = 0.14), 3.92 (R 2 = 0.23), 5.02 (R 2 = 0.21), and 4.15 (R 2 = 0.28). Similarly, the logistic regression odds ratios per 10 μg/m 3 increase of PM 2.5 were greater than one for all categories except for Asians and American Indians, indicating that PM 2.5 is related to the odds of RSC incidence. The age-adjusted odds ratio for males (OR = 2.16, 95% CI = 1.56-3.01) was higher than that for females (OR = 1.50, 95% CI = 1.09-2.06), and it was higher for Blacks (OR = 2.12, 95% CI = 1.43-3.14) than for Whites (OR = 1.72, 95% CI = 1.23-2.42). The odds ratios for all categories were attenuated with the inclusion of the smoking covariate, reflecting the effect of smoking on RSC incidence besides PM 2.5 .

Evaluating exposures to complex mixtures of chemicals during a new production process in the plastics industry.

PubMed

Meijster, Tim; Burstyn, Igor; Van Wendel De Joode, Berna; Posthumus, Maarten A; Kromhout, Hans

2004-08-01

The goal of this study was to monitor emission of chemicals at a factory where plastics products were fabricated by a new robotic (impregnated tape winding) production process. Stationary and personal air measurements were taken to determine which chemicals were released and at what concentrations. Principal component analyses (PCA) and linear regression were used to determine the emission sources of different chemicals found in the air samples. We showed that complex mixtures of chemicals were released, but most concentrations were below Dutch exposure limits. Based on the results of the principal component analyses, the chemicals found were divided into three groups. The first group consisted of short chain aliphatic hydrocarbons (C2-C6). The second group included larger hydrocarbons (C9-C11) and some cyclic hydrocarbons. The third group contained all aromatic and two aliphatic hydrocarbons. Regression analyses showed that emission of the first group of chemicals was associated with cleaning activities and the use of epoxy resins. The second and third group showed strong association with the type of tape used in the new tape winding process. High levels of CO and HCN (above exposure limits) were measured on one occasion when a different brand of impregnated polypropylene sulphide tape was used in the tape winding process. Plans exist to drastically increase production with the new tape winding process. This will cause exposure levels to rise and therefore further control measures should be installed to reduce release of these chemicals.

Effects of a randomized controlled trial to assess the six-months effects of a school based smoking prevention program in Saudi Arabia.

PubMed

Mohammed, Mutaz; Eggers, Sander Matthijs; Alotaiby, Fahad F; de Vries, Nanne; de Vries, Hein

2016-09-01

To examine the efficacy of a smoking prevention program which aimed to address smoking related cognitions and smoking behavior among Saudi adolescents age 13 to 15. A randomized controlled trial was used. Respondents in the experimental group (N=698) received five in-school sessions, while those in the control group (N=683) received no smoking prevention information (usual curriculum). Post-intervention data was collected six months after baseline. Logistic regression analysis was applied to assess effects on smoking initiation, and linear regression analysis was applied to assess changes in beliefs and analysis of covariance (ANCOVA) was used to assess intervention effects. All analyses were adjusted for the nested structure of students within schools. At post-intervention respondents from the experimental group reported in comparison with those from the control group a significantly more negative attitude towards smoking, stronger social norms against smoking, higher self-efficacy towards non-smoking, more action planning to remain a non-smoker, and lower intentions to smoke in the future. Smoking initiation was 3.2% in the experimental group and 8.8% in the control group (p<0.01). The prevention program reinforced non-smoking cognitions and non-smoking behavior. Therefore it is recommended to implement the program at a national level in Saudi-Arabia. Future studies are recommended to assess long term program effects and the conditions favoring national implementation of the program. Copyright © 2016 Elsevier Inc. All rights reserved.

Using adrenaline during neonatal resuscitation may have an impact on serum cardiac troponin-T levels.

PubMed

Helmer, Caroline; Skranes, Janne H; Liestøl, Knut; Fugelseth, Drude

2015-09-01

It has been suggested that serum cardiac troponin-T (cTnT) can predict the severity of neonatal hypoxic-ischaemic encephalopathy. We evaluated whether cTnT was better correlated with adrenaline during cardiopulmonary resuscitation (CPR) than with the severity of the insult itself, based on the Apgar scores. Serum cTnT was analysed in 47 asphyxiated newborn infants treated with hypothermia. Blood samples and resuscitation data were collected from medical records, and multiple linear regressions were used to evaluate the effect of the treatment and the Apgar scores on cTnT levels. The infants were divided into three groups: the no CPR group (n = 29) just received stimulation and ventilation, the CPR minus adrenaline group (n = 9) received cardiac compression and ventilation and the CPR plus adrenaline group (n = 9) received complete CPR, including adrenaline. In the univariate analysis, the five and ten-minute Apgar scores were significantly lower in the CPR plus adrenaline group and the cTnT was significantly higher. Multiple regression analysis showed significantly higher cTnT values in the CPR plus adrenaline group, but no significant relationship between cTnT and the Apgar scores. Although cTnT correlated with the severity of the insult in neonatal hypoxic-ischaemic encephalopathy, the levels may have been affected by adrenaline administered during CPR. ©2015 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Prospective Associations Among Assets and Successful Transition to Early Adulthood

PubMed Central

Vesely, Sara K.; Aspy, Cheryl B.; Tolma, Eleni L.

2015-01-01

Objectives. We investigated prospective associations among assets (e.g., family communication), which research has shown to protect youths from risk behavior, and successful transition to early adulthood (STEA). Methods. We included participants (n = 651) aged 18 years and older at study wave 5 (2007–2008) of the Youth Asset Study, in the Oklahoma City, Oklahoma, metro area, in the analyses. We categorized 14 assets into individual-, family-, or community-level groups. We included asset groups assessed at wave 1 (2003–2004) in linear regression analyses to predict STEA 4 years later at wave 5. Results. Individual- and community-level assets significantly (P < .05) predicted STEA 4 years later and the associations were generally linear, indicating that the more assets participants possessed the better the STEA outcome. There was a gender interaction for family-level assets suggesting that family-level assets were significant predictors of STEA for males but not for females. Conclusions. Public health programming should focus on community- and family-level youth assets as well as individual-level youth assets to promote positive health outcomes in early adulthood. PMID:25393188

How could multimedia information about dental implant surgery effects patients' anxiety level?

PubMed

Kazancioglu, H-O; Dahhan, A-S; Acar, A-H

2017-01-01

To evaluate the effects of different patient education techniques on patients' anxiety levels before and after dental implant surgery. Sixty patients were randomized into three groups; each contained 20 patients; [group 1, basic information given verbally, with details of operation and recovery; group 2 (study group), basic information given verbally with details of operative procedures and recovery, and by watching a movie on single implant surgery]; and a control group [basic information given verbally "but it was devoid of the details of the operative procedures and recovery"]. Anxiety levels were assessed using the Spielberger's State-Trait Anxiety Inventory (STAI) and Modified Dental Anxiety Scale (MDAS). Pain was assessed with a visual analog scale (VAS). The most significant changes were observed in the movie group (P < 0.05). Patients who were more anxious also used more analgesic medication. Linear regression analysis showed that female patients had higher levels of anxiety (P < 0.05). Preoperative multimedia information increases anxiety level.

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

PubMed

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

2015-01-01

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

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

PubMed

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

What are the predictor variables of social well-being among the medical science students?

PubMed

Javadi-Pashaki, Nazila; Darvishpour, Azar

2018-01-01

Individuals with social well-being can cope more successfully with major problems of social roles. Due to the social nature of human life, it cannot be ignored to pay attention the social aspect of health. The purpose of this study was to identify variables that predict the social well-being of medical students. A descriptive-analytical study was conducted on 489 medical science students of Gilan Province, the North of Iran, during May to September 2016. The samples were selected using quota sampling method. Research instrument was a questionnaire consisting of two parts: demographic section and Keyes social well-being questionnaire. Data analysis was done using SPSS software version 19 and with descriptive and inferential statistics (t-test, ANOVA, and linear regression). The results showed that majority of the students had average social well-being. Furthermore, a significant relationship between the academic degree ( P = 0.009), major ( P = 0.0001), the interest and field's satisfaction ( P = 0.0001), and social well-being was seen. The results of linear regression model showed that four variables (academic degree, major, group membership, and the interest and field's satisfaction) were significantly associated with the social well-being ( P < 0.05). The findings demonstrate that the different effects of the demographic factors on social well-being and the need for further consideration of these factors are obvious. Thus, health and education authorities are advised to pay attention students' academic degree, major, group membership, and the interest and field's satisfaction to upgrade and maintain the level of their social well-being.

Lead-induced anemia: Dose-response relationships and evidence for a threshold

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

Schwartz, J.; Landrigan, P.J.; Baker, E.L. Jr.

1990-02-01

We conducted a cross-sectional epidemiologic study to assess the association between blood lead level and hematocrit in 579 one to five year-old children living near a primary lead smelter in 1974. Blood lead levels ranged from 0.53 to 7.91 mumol/L (11 to 164 micrograms/dl). To predict hematocrit as a function of blood lead level and age, we derived non-linear regression models and fit percentile curves. We used logistic regression to predict the probability of hematocrit values less than 35 per cent. We found a strong non-linear, dose-response relationship between blood lead level and hematocrit. This relationship was influenced by age,more » but (in this age group) not by sex; the effect was strongest in youngest children. In one year-olds, the age group most severely affected, the risk of an hematocrit value below 35 percent was 2 percent above background at blood lead levels between 0.97 and 1.88 mumol/L (20 and 39 micrograms/dl), 18 percent above background at lead levels of 1.93 to 2.85 mumol/L (40 to 59 micrograms/dl), and 40 percent above background at lead levels of 2.9 mumol/L (60 micrograms/dl) and greater; background was defined as a blood lead level below 1.88 mumol/L (20 micrograms/dl). This effect appeared independent of iron deficiency. These findings suggest that blood lead levels close to the currently recommended limit value of 1.21 mumol/L (25 micrograms/dl) are associated with dose-related depression of hematocrit in young children.« less

The relationship between vitronectin and hepatic insulin resistance in type 2 diabetes mellitus.

PubMed

Cao, Yan; Li, Xinyu; Lu, Chong; Zhan, Xiaorong

2018-05-18

The World Health Organization (WHO) estimates that approximately 300 million people will suffer from diabetes mellitus by 2025. Type 2 diabetes mellitus (T2DM) is much more prevalent. T2DM comprises approximately 90% of diabetes mellitus cases, and it is caused by a combination of insulin resistance and inadequate compensatory insulin secretory response. In this study, we aimed to compare the plasma vitronectin (VN) levels between patients with T2DM and insulin resistance (IR) and healthy controls. Seventy patients with IR and 70 age- and body mass index (BMI)-matched healthy controls were included in the study. The insulin, Waist-to-Hip Ratio (WHR), C-peptide (CP) and VN levels of all participants were examined. The homeostasis model of assessment for insulin resistence index (HOMA-IR (CP)) formula was used to calculate insulin resistance. The levels of BMI, fasting plasma gluose (FPG), 2-hour postprandial glucose (2hPG), glycated hemoglobins (HbA1c), and HOMA-IR (CP) were significantly elevated in case group compared with controls. VN was found to be significantly decreased in case group. (VN Mean (Std): 8.55 (2.92) versus 12.88 (1.26) ng/mL p < 0.001). Multiple linear regression analysis was performed. This model explained 43.42% of the total variability of VN. Multiple linear regression analysis showed that HOMA-IR (CP) and age independently predicted VN levels. The VN may be a candidate target for the appraisal of hepatic insulin resistance in patients with T2DM.

Liver stiffness measurements with supersonic shear wave elastography in the diagnosis of biliary atresia: a comparative study with grey-scale US.

PubMed

Zhou, Lu-Yao; Jiang, Hong; Shan, Quan-Yuan; Chen, Dong; Lin, Xiao-Na; Liu, Bao-Xian; Xie, Xiao-Yan

2017-08-01

To prospectively assess the diagnostic performance of supersonic shear wave elastography (SSWE) in identifying biliary atresia (BA) among infants with conjugated hyperbilirubinaemia by comparing this approach with grey-scale ultrasonography (US). Forty infants were analysed as the control group to determine normal liver stiffness values. The use of SSWE values for identifying BA was investigated in 172 infants suspected of having BA, and results were compared with the results obtained by grey-scale US. The Mann-Whitney U test, unpaired t-test, Spearman correlation and linear regression were also performed. The success rates of SSWE measurements in the control and study group were 100% (40/40) and 96.4% (244/253), respectively. Age, direct bilirubin, and indirect bilirubin all significantly correlated with SSWE in the liver (all P < 0.001). Linear regression showed that age had a greater effect on SSWE values than direct or indirect bilirubin. The diagnostic performance of liver stiffness values in identifying BA was lower than that of grey-scale US (area under the receiver operating characteristic curve [AUC], 0.790 vs 0.893, P < 0.001). SSWE is feasible and valuable in differentiating BA from non-BA. However, its diagnostic performance does not exceed that of grey-scale US. • SSWE could be successfully performed in an infant population. • For infants, the liver stiffness will increase as age increases. • SSWE is potentially useful in assessing infants suspected of biliary atresia. • SSWE is inferior to grey-scale US in identifying biliary atresia.

Identification of indicator congeners and evaluation of emission pattern of polychlorinated naphthalenes in industrial stack gas emissions by statistical analyses.

PubMed

Liu, Guorui; Cai, Zongwei; Zheng, Minghui; Jiang, Xiaoxu; Nie, Zhiqiang; Wang, Mei

2015-01-01

Identifying marker congeners of unintentionally produced polychlorinated naphthalenes (PCNs) from industrial thermal sources might be useful for predicting total PCN (∑2-8PCN) emissions by the determination of only indicator congeners. In this study, potential indicator congeners were identified based on the PCN data in 122 stack gas samples from over 60 plants involved in more than ten industrial thermal sources reported in our previous case studies. Linear regression analyses identified that the concentrations of CN27/30, CN52/60, and CN66/67 correlated significantly with ∑2-8PCN (R(2)=0.77, 0.80, and 0.58, respectively; n=122, p<0.05), which might be good candidates for indicator congeners. Equations describing relationships between indicators and ∑2-8PCN were established. The linear regression analyses involving 122 samples showed that the relationships between the indicator congeners and ∑2-8PCN were not significantly affected by factors such as industry types, raw materials used, or operating conditions. Hierarchical cluster analysis and similarity calculations for the 122 stack gas samples were adopted to group those samples and evaluating their similarity and difference based on the PCN homolog distributions from different industrial thermal sources. Generally, the fractions of less chlorinated homologs comprised of di-, tri-, and tetra-homologs were much higher than that of more chlorinated homologs for up to 111 stack gas samples contained in group 1 and 2, which indicating the dominance of lower chlorinated homologs in stack gas from industrial thermal sources. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sources of variability in satellite-derived estimates of Phytoplankton production in the eastern tropical Pacific

NASA Astrophysics Data System (ADS)

Banse, Karl; Yong, Marina

1990-05-01

As a proxy for satellite (coastal zone color scanner) observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore, eastern tropical Pacific were analyzed in terms of six temporal groups and four current regimes. In multiple linear regressions on column production Pt, we found that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate pmax is added, the coefficient of determination (r2) ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the pmax proxy is the best predictor for Pt. Pt can be calculated fairly accurately (on the average, within 10-20%) from satellite pigment, the 10% light depth, and station values (but not from regional or seasonal means) of the pmax proxy; for individual stations the precision is 35-84% (median of 57% for the 10 groupings; p = 0.05) of the means of observed values. At present, pmax cannot be estimated from space; in the data set it is not even highly correlated with irradiance, temperature, and nitrate at depth of occurrence. Therefore extant models for calculating Pt in this tropical ocean have inherent limits of accuracy as well as of precision owing to ignorance about a physiological parameter.

Do depression treatments reduce suicidal ideation? The effects of CBT, IPT, pharmacotherapy, and placebo on suicidality.

PubMed

Weitz, Erica; Hollon, Steven D; Kerkhof, Ad; Cuijpers, Pim

2014-01-01

Many well-researched treatments for depression exist. However, there is not yet enough evidence on whether these therapies, designed for the treatment of depression, are also effective for reducing suicidal ideation. This research provides valuable information for researchers, clinicians, and suicide prevention policy makers. Analysis was conducted on the Treatment for Depression Research Collaborative (TDCRP) sample, which included CBT, IPT, medication, and placebo treatment groups. Participants were included in the analysis if they reported suicidal ideation on the HRSD or BDI (score of ≥1). Multivariate linear regression indicated that both IPT (b=.41, p<.05) and medication (b =.47, p<.05) yielded a significant reduction in suicide symptoms compared to placebo on the HRSD. Multivariate linear regression indicated that after adjustment for change in depression these treatment effects were no longer significant. Moderate Cohen׳s d effect sizes from baseline to post-test differences in suicide score by treatment group are reported. These analyses were completed on a single suicide item from each of the measures. Moreover, the TDCRP excluded participants with moderate to severe suicidal ideation. This study demonstrates the specific effectiveness of IPT and medications in reducing suicidal ideation (relative to placebo), albeit largely as a consequence of their more general effects on depression. This adds to the growing body of evidence that depression treatments, specifically IPT and medication, can also reduce suicidal ideation and serves to further our understanding of the complex relationship between depression and suicide. Copyright © 2014 Elsevier B.V. All rights reserved.

Diagnosis of Enzyme Inhibition Using Excel Solver: A Combined Dry and Wet Laboratory Exercise

ERIC Educational Resources Information Center

Dias, Albino A.; Pinto, Paula A.; Fraga, Irene; Bezerra, Rui M. F.

2014-01-01

In enzyme kinetic studies, linear transformations of the Michaelis-Menten equation, such as the Lineweaver-Burk double-reciprocal transformation, present some constraints. The linear transformation distorts the experimental error and the relationship between "x" and "y" axes; consequently, linear regression of transformed data…

In vivo measurement of the average refractive index of the human crystalline lens using optical coherence tomography.

PubMed

de Freitas, Carolina; Ruggeri, Marco; Manns, Fabrice; Ho, Arthur; Parel, Jean-Marie

2013-01-15

We present a method for measuring the average group refractive index of the human crystalline lens in vivo using an optical coherence tomography (OCT) system which, allows full-length biometry of the eye. A series of OCT images of the eye including the anterior segment and retina were recorded during accommodation. Optical lengths of the anterior chamber, lens, and vitreous were measured dynamically along the central axis on the OCT images. The group refractive index of the crystalline lens along the central axis was determined using linear regression analysis of the intraocular optical length measurements. Measurements were acquired on three subjects of age 21, 24, and 35 years. The average group refractive index for the three subjects was, respectively, n=1.41, 1.43, and 1.39 at 835 nm.

Characterizing the scientific potential of satellite sensors. [San Francisco, California

NASA Technical Reports Server (NTRS)

1984-01-01

Eleven thematic mapper (TM) radiometric calibration programs were tested and evaluated in support of the task to characterize the potential of LANDSAT TM digital imagery for scientific investigations in the Earth sciences and terrestrial physics. Three software errors related to integer overflow, divide by zero, and nonexist file group were found and solved. Raw, calibrated, and corrected image groups that were created and stored on the Barker2 disk are enumerated. Black and white pixel print files were created for various subscenes of a San Francisco scene (ID 40392-18152). The development of linear regression software is discussed. The output of the software and its function are described. Future work in TM radiometric calibration, image processing, and software development is outlined.

A practical radial basis function equalizer.

PubMed

Lee, J; Beach, C; Tepedelenlioglu, N

1999-01-01

A radial basis function (RBF) equalizer design process has been developed in which the number of basis function centers used is substantially fewer than conventionally required. The reduction of centers is accomplished in two-steps. First an algorithm is used to select a reduced set of centers that lie close to the decision boundary. Then the centers in this reduced set are grouped, and an average position is chosen to represent each group. Channel order and delay, which are determining factors in setting the initial number of centers, are estimated from regression analysis. In simulation studies, an RBF equalizer with more than 2000-to-1 reduction in centers performed as well as the RBF equalizer without reduction in centers, and better than a conventional linear equalizer.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

PubMed

Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

2016-05-01

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

Ethnicity and acculturation: do they predict weight status in a longitudinal study among Asian, Hispanic, and non-Hispanic White early adolescent females?

PubMed Central

Fialkowski, Marie K; Ettienne, Reynolette; Shvetsov, Yurii B; Rivera, Rebecca L; Van Loan, Marta D; Savaiano, Dennis A; Boushey, Carol J

2015-01-01

Background The prevalence of overweight and obesity among adolescents has increased over the past decade. Prevalence rates are disparate among certain racial and ethnic groups. This study sought to longitudinally examine the relationship between overweight status (≥85th percentile according to the Centers for Disease Control and Prevention growth charts) and ethnic group, as well as acculturation (generation and language spoken in the home) in a sample of adolescent females. Methods Asian (n=160), Hispanic (n=217), and non-Hispanic White (n=304) early adolescent girls participating in the multistate calcium intervention study with complete information on weight, ethnicity, and acculturation were included. Multiple methods of assessing longitudinal relationships (binary logistic regression model, linear regression model, Cox proportional-hazards regression analysis, and Kaplan–Meier survival analysis) were used to examine the relationship. Results The total proportion of girls overweight at baseline was 36%. When examining by ethnic group, the proportion varied with Hispanic girls having the highest percentage (46%) in comparison to their Asian (23%) and Non-Hispanic White (35%) counterparts. Although the total proportion of overweight was 36% at 18 months, the variation across the ethnic groups remained with the proportion of Hispanic girls becoming overweight (55%) being greater than their Asian (18%) and non-Hispanic White (34%) counterparts. However, regardless of the statistical approach used, there were no significant associations between overweight status and acculturation over time. Conclusion These unexpected results warrant further exploration into factors associated with overweight, especially among Hispanic girls, and further investigation of acculturation’s role is warranted. Identifying these risk factors will be important for developing targeted obesity prevention initiatives. PMID:25624775

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

Faecal nitrogen excretion as an approach to estimate forage intake of wethers.

PubMed

Kozloski, G V; Oliveira, L; Poli, C H E C; Azevedo, E B; David, D B; Ribeiro Filho, H M N; Collet, S G

2014-08-01

Data from twenty-two digestibility trials were compiled to examine the relationship between faecal N concentration and organic matter (OM) digestibility (OMD), and between faecal N excretion and OM intake (OMI) by wethers fed tropical or temperate forages alone or with supplements. Data set was grouped by diet type as follows: only tropical grass (n = 204), only temperate grass (n = 160), tropical grass plus supplement (n = 216), temperate grass plus supplement (n = 48), tropical grass plus tropical legume (n = 60) and temperate grass with ruminal infusion of tannins (n = 16). Positive correlation between OMD and either total faecal N concentration (Nfc, % of OM) or metabolic faecal N concentration (Nmetfc, % of OM) was significant for most diet types. Exceptions were the diet that included a tropical legume, where both relationships were negative, and the diet that included tannin extract, where the correlation between OMD and Nfc was not significant. Pearson correlation and linear regressions between OM intake (OMI, g/day) and faecal N excretion (Nf, g/day) were significant for all diet types. When OMI was estimated from the OM faecal excretion and Nfc-based OMD values, the linear comparison between observed and estimated OMI values showed intercept different from 0 and slope different from 1. When OMI was estimated using the Nf-based linear regressions, the linear comparison between observed and estimated OMI values showed neither intercept different from 0 nor slope different from 1. Both linear comparisons showed similar R(2) values (i.e. 0.78 vs. 0.79). In conclusion, linear equations are suitable for directly estimating OM intake by wethers, fed only forage or forage plus supplements, from the amount of N excreted in faeces. The use of this approach in experiments with grazing wethers has the advantage of accounting for individual variations in diet selection and digestion processes and precludes the use of techniques to estimate forage digestibility. Journal of Animal Physiology and Animal Nutrition © 2013 Blackwell Verlag GmbH.

A regression technique for evaluation and quantification for water quality parameters from remote sensing data

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.

1979-01-01

The objective of this paper is to define optical physics and/or environmental conditions under which the linear multiple-regression should be applicable. An investigation of the signal-response equations is conducted and the concept is tested by application to actual remote sensing data from a laboratory experiment performed under controlled conditions. Investigation of the signal-response equations shows that the exact solution for a number of optical physics conditions is of the same form as a linearized multiple-regression equation, even if nonlinear contributions from surface reflections, atmospheric constituents, or other water pollutants are included. Limitations on achieving this type of solution are defined.

Limited Sampling Strategy for Estimating Mycophenolic Acid Exposure on Day 7 Post-Transplant for Two Mycophenolate Mofetil Formulations Derived From 20 Chinese Renal Transplant Recipients.

PubMed

Cai, W; Cai, Q; Xiong, N; Qin, Y; Lai, L; Sun, X; Hu, Y

2018-06-01

To assess the pharmacokinetic properties of mycophenolate mofetil (MMF) dispersible tablets and capsules by the enzyme multiplied immunoassay technique (EMIT) in Chinese kidney transplant recipients in the early post-transplantation phase and to develop the equations to predict mycophenolic acid (MPA) area under the 12-hour concentration-time curve (AUC 0-12h ) using a limited sampling strategy (LSS). Forty patients who underwent renal transplantation from brain-dead donors were randomly divided into dispersible tablets (Sai KE Ping; Hangzhou Zhongmei Huadong Pharma) and capsules (Cellcept; Roche Pharma, Why, NSW, Australia) groups, and treated with MMF combined with combination tacrolimus and prednisone as a basic immunosuppressive regimen. Blood samples were collected before treatment (0) and at 0.5,1, 1.5, 2, 4, 6, 8, 10, and 12 hours post-treatment and 7 days after renal transplantation. Plasma MPA concentrations were measured using EMIT. LSS equations were identified using multiple stepwise linear regression analysis. The peak concentration (C max ) in the MMF dispersible tablets (MMFdt) group (7.0 ± 2.8) mg/L was reduced compared with that in the MMF capsules (MMFc) group (10.8 ± 6.2 mg/L; P = .012); time to peak concentration in the MMFdt group was 3.2 ± 2.3 hours, which was nonsignificantly elevated compared with that of the MMFc group (2.2 ± 1.7 hours). Three-point estimation formulas were generated by multiple linear regression for both groups: MPA-AUC MMFdt  = 3.542 + 3.332C 0.5h  + 1.117C 1.5h  + 3.946C 4h (adjusted r 2  = 0.90, P < .001); MPA-AUC MMFc  = 8.149 + 1.442C 2h  + 1.056C 4h  + 7.133C 6h (adjusted r 2  = 0.88, P < .001). Both predicted and measured AUCs showed good consistency. After treatment with MMF dispersible tables or MMF capsules, the C max of MPA for the MMFdt group was significantly lower than that of the MMFc group; there was no significant difference in other pharmacokinetic parameters. Three-time point equations can be used as a predictable measure of the AUC 0-12h of MPA. Copyright © 2018. Published by Elsevier Inc.