Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

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

PubMed

Beckstead, Jason W

2012-03-30

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 strategy to isolate, examine, and remove suppression effects has been offered. In this article such an approach, rooted in confirmatory factor analysis theory and employing matrix algebra, is developed. Suppression is viewed as the result of criterion-irrelevant variance operating among predictors. Decomposition of predictor variables into criterion-relevant and criterion-irrelevant components using structural equation modeling permits derivation of regression weights with the effects of criterion-irrelevant variance omitted. Three examples with data from applied research are used to illustrate the approach: the first assesses child and parent characteristics to explain why some parents of children with obsessive-compulsive disorder accommodate their child's compulsions more so than do others, the second examines various dimensions of personal health to explain individual differences in global quality of life among patients following heart surgery, and the third deals with quantifying the relative importance of various aptitudes for explaining academic performance in a sample of nursing students. The approach is offered as an analytic tool for investigators interested in understanding predictor-criterion relationships when complex patterns of intercorrelation among predictors are present and is shown to augment dominance analysis.

Analyzing multicomponent receptive fields from neural responses to natural stimuli

PubMed Central

Rowekamp, Ryan; Sharpee, Tatyana O

2011-01-01

The challenge of building increasingly better models of neural responses to natural stimuli is to accurately estimate the multiple stimulus features that may jointly affect the neural spike probability. The selectivity for combinations of features is thought to be crucial for achieving classical properties of neural responses such as contrast invariance. The joint search for these multiple stimulus features is difficult because estimating spike probability as a multidimensional function of stimulus projections onto candidate relevant dimensions is subject to the curse of dimensionality. An attractive alternative is to search for relevant dimensions sequentially, as in projection pursuit regression. Here we demonstrate using analytic arguments and simulations of model cells that different types of sequential search strategies exhibit systematic biases when used with natural stimuli. Simulations show that joint optimization is feasible for up to three dimensions with current algorithms. When applied to the responses of V1 neurons to natural scenes, models based on three jointly optimized dimensions had better predictive power in a majority of cases compared to dimensions optimized sequentially, with different sequential methods yielding comparable results. Thus, although the curse of dimensionality remains, at least several relevant dimensions can be estimated by joint information maximization. PMID:21780916

Effectiveness and relevant factors of 2% rebamipide ophthalmic suspension treatment in dry eye.

PubMed

Ueda, Kaori; Matsumiya, Wataru; Otsuka, Keiko; Maeda, Yoshifumi; Nagai, Takayuki; Nakamura, Makoto

2015-06-06

Rebamipide with mucin secretagogue activity was recently approved for the treatment of dry eye. The efficacy and safety in the treatment of rebamipide were shown in two pivotal clinical trials. It was the aim of this study to evaluate the effect of 2% rebamipide ophthalmic suspension in patients with dry eye and analyze relevant factors for favorable effects of rebamipide in clinical practice. This was a retrospective cohort study of 48 eyes from 24 patients with dry eye treated with 2% rebamipide ophthalmic suspension. Dry eye-related symptom score, tear film break-up time (TBUT), fluorescein ocular surface staining score (FOS) and the Schirmer test were used to collect the data from patients at baseline, and at 2, 4, 8, and 12 week visits. To determine the relevant factors, multiple regression analyses were then performed. Mean dry eye-related symptom score showed a significant improvement from the baseline (14.5 points) at 2, 4, 8 and 12 weeks (9.80, 7.04, 7.04 and 7.83 points, corrected P value < 0.001, respectively). Median FOS showed a significant improvement from the baseline (3.0 points) at 2, 4, 8 and 12 weeks (2.0, 2.0, 1.0 and 1.0 points, corrected P value < 0.001, respectively). TBUT and Schirmer test values were not significantly improved after the treatment. For ocular symptoms, three parameters (foreign body sensation, dry eye sensation and ocular discomfort) showed significant improvements at all visits. The multiple regression analyses showed that the fluorescein conjunctiva staining score was significantly correlated with the changes of dry eye-related symptom score at 12 weeks (P value = 0.017) and dry eye-related symptom score was significantly correlated with independent variables for the changes of FOS at 12 weeks (P value = 0.0097). Two percent rebamipide ophthalmic suspension was an effective therapy for dry eye patients. Moreover the fluorescein conjunctiva staining score and dry eye-related symptom score might be good relevant factors for favorable effects of rebamipide.

Clinical relevance of valgus deformity of proximal femur in cerebral palsy.

PubMed

Lee, Kyoung Min; Kang, Jong Yeol; Chung, Chin Youb; Kwon, Dae Gyu; Lee, Sang Hyeong; Choi, In Ho; Cho, Tae-Joon; Yoo, Won Joon; Park, Moon Seok

2010-01-01

Proximal femoral deformity related to physis has not been studied in patients with cerebral palsy (CP). This study was performed to investigate the clinical relevance of neck shaft angle (NSA), head shaft angle (HSA), and proximal femoral epiphyseal shape in patients with CP, which represent the deformities of metaphysis, physis, and epiphysis, respectively. Three hundred eighty-four patients with CP (mean age 9.1 y, 249 males and 135 females) were included. Extent of involvement and functional states [Gross Motor Function Classification System (GMFCS) level] were obtained. Radiographic measurements including NSA, HSA, and qualitative shape of the proximal femoral epiphysis were evaluated and analyzed according to extent of involvement and GMFCS level. Reliability and correlation with each measurement were assessed. Multiple regression test was performed to examine the significant contributing factors to migration percentage (MP) that represents hip instability. NSA showed excellent interobserver reliability with intraclass correlation coefficients of 0.976. Correlation with the MP was higher in the NSA (r=0.419, P<0.001) than in the HSA (r=0.256, P<0.001). NSA, HSA, and MP tended to increase with increasing GMFCS level, and proportion of valgus deformed proximal femoral epiphysis also increased with increasing GMFCS level, which means valgus deformity and unstable hips in the less favorable functional states. Multiple regression analysis revealed NSA, GMFCS level, and shape of the proximal femoral epiphysis to be significant factors affecting MP. NSA appeared to be more clinically relevant than HSA in evaluating proximal femoral deformity in patients with CP. Shape of proximal femoral epiphysis is believed to have clinical implications in terms of hip instability. Diagnostic level II.

Evaluating emotional sensitivity and tolerance factors in the prediction of panic-relevant responding to a biological challenge.

PubMed

Kutz, Amanda; Marshall, Erin; Bernstein, Amit; Zvolensky, Michael J

2010-01-01

The current study investigated anxiety sensitivity, distress tolerance (Simons & Gaher, 2005), and discomfort intolerance (Schmidt, Richey, Cromer, & Buckner, 2007) in relation to panic-relevant responding (i.e., panic attack symptoms and panic-relevant cognitions) to a 10% carbon dioxide enriched air challenge. Participants were 216 adults (52.6% female; M(age)=22.4, SD=9.0). A series of hierarchical multiple regressions was conducted with covariates of negative affectivity and past year panic attack history in step one of the model, and anxiety sensitivity, discomfort intolerance, and distress tolerance entered simultaneously into step two. Results indicated that anxiety sensitivity, but not distress tolerance or discomfort intolerance, was significantly incrementally predictive of physical panic attack symptoms and cognitive panic attack symptoms. Additionally, anxiety sensitivity was significantly predictive of variance in panic attack status during the challenge. These findings emphasize the important, unique role of anxiety sensitivity in predicting risk for panic psychopathology, even when considered in the context of other theoretically relevant emotion vulnerability variables.

A methodology for the design of experiments in computational intelligence with multiple regression models.

PubMed

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

PubMed Central

Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2015-06-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11-year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (support vector regression, neural networks) besides the multiple linear regression approach. The analysis was applied to several current reanalysis data sets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how these types of data resolve especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the tropical stratosphere were found to be in qualitative agreement with previous attribution studies, although the agreement with observational results was incomplete, especially for JRA-55. The analysis also pointed to the solar signal in the ozone data sets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. The results obtained by linear regression were confirmed by the nonlinear approach through all data sets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. The seasonal evolution of the solar response was also discussed in terms of dynamical causalities in the winter hemispheres. The hypothetical mechanism of a weaker Brewer-Dobson circulation at solar maxima was reviewed together with a discussion of polar vortex behaviour.

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.

Modeling the dynamics of urban growth using multinomial logistic regression: a case study of Jiayu County, Hubei Province, China

NASA Astrophysics Data System (ADS)

Nong, Yu; Du, Qingyun; Wang, Kun; Miao, Lei; Zhang, Weiwei

2008-10-01

Urban growth modeling, one of the most important aspects of land use and land cover change study, has attracted substantial attention because it helps to comprehend the mechanisms of land use change thus helps relevant policies made. This study applied multinomial logistic regression to model urban growth in the Jiayu county of Hubei province, China to discover the relationship between urban growth and the driving forces of which biophysical and social-economic factors are selected as independent variables. This type of regression is similar to binary logistic regression, but it is more general because the dependent variable is not restricted to two categories, as those previous studies did. The multinomial one can simulate the process of multiple land use competition between urban land, bare land, cultivated land and orchard land. Taking the land use type of Urban as reference category, parameters could be estimated with odds ratio. A probability map is generated from the model to predict where urban growth will occur as a result of the computation.

A simple measure of cognitive reserve is relevant for cognitive performance in MS patients.

PubMed

Della Corte, Marida; Santangelo, Gabriella; Bisecco, Alvino; Sacco, Rosaria; Siciliano, Mattia; d'Ambrosio, Alessandro; Docimo, Renato; Cuomo, Teresa; Lavorgna, Luigi; Bonavita, Simona; Tedeschi, Gioacchino; Gallo, Antonio

2018-05-04

Cognitive reserve (CR) contributes to preserve cognition despite brain damage. This theory has been applied to multiple sclerosis (MS) to explain the partial relationship between cognition and MRI markers of brain pathology. Our aim was to determine the relationship between two measures of CR and cognition in MS. One hundred and forty-seven MS patients were enrolled. Cognition was assessed using the Rao's Brief Repeatable Battery and the Stroop Test. CR was measured as the vocabulary subtest of the WAIS-R score (VOC) and the number of years of formal education (EDU). Regression analysis included raw score data on each neuropsychological (NP) test as dependent variables and demographic/clinical parameters, VOC, and EDU as independent predictors. A binary logistic regression analysis including clinical/CR parameters as covariates and absence/presence of cognitive deficits as dependent variables was performed too. VOC, but not EDU, was strongly correlated with performances at all ten NP tests. EDU was correlated with executive performances. The binary logistic regression showed that only the Expanded Disability Status Scale (EDSS) and VOC were independently correlated with the presence/absence of CD. The lower the VOC and/or the higher the EDSS, the higher the frequency of CD. In conclusion, our study supports the relevance of CR in subtending cognitive performances and the presence of CD in MS patients.

Knowledge, attitudes and practices survey on organ donation among a selected adult population of Pakistan

PubMed Central

Saleem, Taimur; Ishaque, Sidra; Habib, Nida; Hussain, Syedda Saadia; Jawed, Areeba; Khan, Aamir Ali; Ahmad, Muhammad Imran; Iftikhar, Mian Omer; Mughal, Hamza Pervez; Jehan, Imtiaz

2009-01-01

Background To determine the knowledge, attitudes and practices regarding organ donation in a selected adult population in Pakistan. Methods Convenience sampling was used to generate a sample of 440; 408 interviews were successfully completed and used for analysis. Data collection was carried out via a face to face interview based on a pre-tested questionnaire in selected public areas of Karachi, Pakistan. Data was analyzed using SPSS v.15 and associations were tested using the Pearson's Chi square test. Multiple logistic regression was used to find independent predictors of knowledge status and motivation of organ donation. Results Knowledge about organ donation was significantly associated with education (p = 0.000) and socioeconomic status (p = 0.038). 70/198 (35.3%) people expressed a high motivation to donate. Allowance of organ donation in religion was significantly associated with the motivation to donate (p = 0.000). Multiple logistic regression analysis revealed that higher level of education and higher socioeconomic status were significant (p < 0.05) independent predictors of knowledge status of organ donation. For motivation, multiple logistic regression revealed that higher socioeconomic status, adequate knowledge score and belief that organ donation is allowed in religion were significant (p < 0.05) independent predictors. Television emerged as the major source of information. Only 3.5% had themselves donated an organ; with only one person being an actual kidney donor. Conclusion Better knowledge may ultimately translate into the act of donation. Effective measures should be taken to educate people with relevant information with the involvement of media, doctors and religious scholars. PMID:19534793

Impact of divorce on the quality of life in school-age children.

PubMed

Eymann, Alfredo; Busaniche, Julio; Llera, Julián; De Cunto, Carmen; Wahren, Carlos

2009-01-01

To assess psychosocial quality of life in school-age children of divorced parents. A cross-sectional survey was conducted at the pediatric outpatient clinic of a community hospital. Children 5 to 12 years old from married families and divorced families were included. Child quality of life was assessed through maternal reports using a Child Health Questionnaire-Parent Form 50. A multiple linear regression model was constructed including clinically relevant variables significant on univariate analysis (beta coefficient and 95%CI). Three hundred and thirty families were invited to participate and 313 completed the questionnaire. Univariate analysis showed that quality of life was significantly associated with parental separation, child sex, time spent with the father, standard of living, and maternal education. In a multiple linear regression model, quality of life scores decreased in boys -4.5 (-6.8 to -2.3) and increased for time spent with the father 0.09 (0.01 to 0.2). In divorced families, multiple linear regression showed that quality of life scores increased when parents had separated by mutual agreement 6.1 (2.7 to 9.4), when the mother had university level education 5.9 (1.7 to 10.1) and for each year elapsed since separation 0.6 (0.2 to 1.1), whereas scores decreased in boys -5.4 (-9.5 to -1.3) and for each one-year increment of maternal age -0.4 (-0.7 to -0.05). Children's psychosocial quality of life was affected by divorce. The Child Health Questionnaire can be useful to detect a decline in the psychosocial quality of life.

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.

Anti-citrullinated peptide antibodies are the strongest predictor of clinically relevant radiographic progression in rheumatoid arthritis patients achieving remission or low disease activity: A post hoc analysis of a nationwide cohort in Japan

PubMed Central

Okada, Akitomo; Fukuda, Takaaki; Hidaka, Toshihiko; Ishii, Tomonori; Ueki, Yukitaka; Kodera, Takao; Nakashima, Munetoshi; Takahashi, Yuichi; Honda, Seiyo; Horai, Yoshiro; Watanabe, Ryu; Okuno, Hiroshi; Aramaki, Toshiyuki; Izumiyama, Tomomasa; Takai, Osamu; Miyashita, Taiichiro; Sato, Shuntaro; Kawashiri, Shin-ya; Iwamoto, Naoki; Ichinose, Kunihiro; Tamai, Mami; Origuchi, Tomoki; Nakamura, Hideki; Aoyagi, Kiyoshi; Eguchi, Katsumi; Kawakami, Atsushi

2017-01-01

Objectives To determine prognostic factors of clinically relevant radiographic progression (CRRP) in patients with rheumatoid arthritis (RA) achieving remission or low disease activity (LDA) in clinical practice. Methods Using data from a nationwide, multicenter, prospective study in Japan, we evaluated 198 biological disease-modifying antirheumatic drug (bDMARD)-naïve RA patients who were in remission or had LDA at study entry after being treated with conventional synthetic DMARDs (csDMARDs). CRRP was defined as the yearly progression of modified total Sharp score (mTSS) >3.0 U. We performed a multiple logistic regression analysis to explore the factors to predict CRRP at 1 year. We used receiver operating characteristic (ROC) curve to estimate the performance of relevant variables for predicting CRRP. Results The mean Disease Activity Score in 28 joints-erythrocyte sedimentation rate (DAS28-ESR) was 2.32 ± 0.58 at study entry. During the 1-year observation, remission or LDA persisted in 72% of the patients. CRRP was observed in 7.6% of the patients. The multiple logistic regression analysis revealed that the independent variables to predict the development of CRRP were: anti-citrullinated peptide antibodies (ACPA) positivity at baseline (OR = 15.2, 95%CI 2.64–299), time-integrated DAS28-ESR during the 1 year post-baseline (7.85-unit increase, OR = 1.83, 95%CI 1.03–3.45), and the mTSS at baseline (13-unit increase, OR = 1.22, 95%CI 1.06–1.42). Conclusions ACPA positivity was the strongest independent predictor of CRRP in patients with RA in remission or LDA. Physicians should recognize ACPA as a poor-prognosis factor regarding the radiographic outcome of RA, even among patients showing a clinically favorable response to DMARDs. PMID:28505163

Predicting ecological flow regime at ungaged sites: A comparison of methods

USGS Publications Warehouse

Murphy, Jennifer C.; Knight, Rodney R.; Wolfe, William J.; Gain, W. Scott

2012-01-01

Nineteen ecologically relevant streamflow characteristics were estimated using published rainfall–runoff and regional regression models for six sites with observed daily streamflow records in Kentucky. The regional regression model produced median estimates closer to the observed median for all but two characteristics. The variability of predictions from both models was generally less than the observed variability. The variability of the predictions from the rainfall–runoff model was greater than that from the regional regression model for all but three characteristics. Eight characteristics predicted by the rainfall–runoff model display positive or negative bias across all six sites; biases are not as pronounced for the regional regression model. Results suggest that a rainfall–runoff model calibrated on a single characteristic is less likely to perform well as a predictor of a range of other characteristics (flow regime) when compared with a regional regression model calibrated individually on multiple characteristics used to represent the flow regime. Poor model performance may misrepresent hydrologic conditions, potentially distorting the perceived risk of ecological degradation. Without prior selection of streamflow characteristics, targeted calibration, and error quantification, the widespread application of general hydrologic models to ecological flow studies is problematic. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Multi-Target Regression via Robust Low-Rank Learning.

PubMed

Zhen, Xiantong; Yu, Mengyang; He, Xiaofei; Li, Shuo

2018-02-01

Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

Application of Multiple Regression and Design of Experiments for Modelling the Effect of Monoethylene Glycol in the Calcium Carbonate Scaling Process.

PubMed

Kartnaller, Vinicius; Venâncio, Fabrício; F do Rosário, Francisca; Cajaiba, João

2018-04-10

To avoid gas hydrate formation during oil and gas production, companies usually employ thermodynamic inhibitors consisting of hydroxyl compounds, such as monoethylene glycol (MEG). However, these inhibitors may cause other types of fouling during production such as inorganic salt deposits (scale). Calcium carbonate is one of the main scaling salts and is a great concern, especially for the new pre-salt wells being explored in Brazil. Hence, it is important to understand how using inhibitors to control gas hydrate formation may be interacting with the scale formation process. Multiple regression and design of experiments were used to mathematically model the calcium carbonate scaling process and its evolution in the presence of MEG. It was seen that MEG, although inducing the precipitation by increasing the supersaturation ratio, actually works as a scale inhibitor for calcium carbonate in concentrations over 40%. This effect was not due to changes in the viscosity, as suggested in the literature, but possibly to the binding of MEG to the CaCO₃ particles' surface. The interaction of the MEG inhibition effect with the system's variables was also assessed, when temperature' and calcium concentration were more relevant.

Predictive ability of a comprehensive incremental test in mountain bike marathon.

PubMed

Ahrend, Marc-Daniel; Schneeweiss, Patrick; Martus, Peter; Niess, Andreas M; Krauss, Inga

2018-01-01

Traditional performance tests in mountain bike marathon (XCM) primarily quantify aerobic metabolism and may not describe the relevant capacities in XCM. We aimed to validate a comprehensive test protocol quantifying its intermittent demands. Forty-nine athletes (38.8±9.1 years; 38 male; 11 female) performed a laboratory performance test, including an incremental test, to determine individual anaerobic threshold (IAT), peak power output (PPO) and three maximal efforts (10 s all-out sprint, 1 min maximal effort and 5 min maximal effort). Within 2 weeks, the athletes participated in one of three XCM races (n=15, n=9 and n=25). Correlations between test variables and race times were calculated separately. In addition, multiple regression models of the predictive value of laboratory outcomes were calculated for race 3 and across all races (z-transformed data). All variables were correlated with race times 1, 2 and 3: 10 s all-out sprint (r=-0.72; r=-0.59; r=-0.61), 1 min maximal effort (r=-0.85; r=-0.84; r=-0.82), 5 min maximal effort (r=-0.57; r=-0.85; r=-0.76), PPO (r=-0.77; r=-0.73; r=-0.76) and IAT (r=-0.71; r=-0.67; r=-0.68). The best-fitting multiple regression models for race 3 (r 2 =0.868) and across all races (r 2 =0.757) comprised 1 min maximal effort, IAT and body weight. Aerobic and intermittent variables correlated least strongly with race times. Their use in a multiple regression model confirmed additional explanatory power to predict XCM performance. These findings underline the usefulness of the comprehensive incremental test to predict performance in that sport more precisely.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

PubMed

Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

2013-01-01

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

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

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

ERIC Educational Resources Information Center

Jaccard, James; And Others

1990-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

ERIC Educational Resources Information Center

Warne, Russell T.

2011-01-01

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

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

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

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

Comment on "Cosmic-ray-driven reaction and greenhouse effect of halogenated molecules: Culprits for atmospheric ozone depletion and global climate change"

NASA Astrophysics Data System (ADS)

Nuccitelli, Dana; Cowtan, Kevin; Jacobs, Peter; Richardson, Mark; Way, Robert G.; Blackburn, Anne-Marie; Stolpe, Martin B.; Cook, John

2014-04-01

Lu (2013) (L13) argued that solar effects and anthropogenic halogenated gases can explain most of the observed warming of global mean surface air temperatures since 1850, with virtually no contribution from atmospheric carbon dioxide (CO2) concentrations. Here we show that this conclusion is based on assumptions about the saturation of the CO2-induced greenhouse effect that have been experimentally falsified. L13 also confuses equilibrium and transient response, and relies on data sources that have been superseeded due to known inaccuracies. Furthermore, the statistical approach of sequential linear regression artificially shifts variance onto the first predictor. L13's artificial choice of regression order and neglect of other relevant data is the fundamental cause of the incorrect main conclusion. Consideration of more modern data and a more parsimonious multiple regression model leads to contradiction with L13's statistical results. Finally, the correlation arguments in L13 are falsified by considering either the more appropriate metric of global heat accumulation, or data on longer timescales.

Mortality rates in OECD countries converged during the period 1990-2010.

PubMed

Bremberg, Sven G

2017-06-01

Since the scientific revolution of the 18th century, human health has gradually improved, but there is no unifying theory that explains this improvement in health. Studies of macrodeterminants have produced conflicting results. Most studies have analysed health at a given point in time as the outcome; however, the rate of improvement in health might be a more appropriate outcome. Twenty-eight OECD member countries were selected for analysis in the period 1990-2010. The main outcomes studied, in six age groups, were the national rates of decrease in mortality in the period 1990-2010. The effects of seven potential determinants on the rates of decrease in mortality were analysed in linear multiple regression models using least squares, controlling for country-specific history constants, which represent the mortality rate in 1990. The multiple regression analyses started with models that only included mortality rates in 1990 as determinants. These models explained 87% of the intercountry variation in the children aged 1-4 years and 51% in adults aged 55-74 years. When added to the regression equations, the seven determinants did not seem to significantly increase the explanatory power of the equations. The analyses indicated a decrease in mortality in all nations and in all age groups. The development of mortality rates in the different nations demonstrated significant catch-up effects. Therefore an important objective of the national public health sector seems to be to reduce the delay between international research findings and the universal implementation of relevant innovations.

Looking for the Perfect Mentor.

PubMed

Sá, Ana Pinheiro; Teixeira-Pinto, Cristina; Veríssimo, Rafaela; Vilas-Boas, Andreia; Firmino-Machado, João

2015-01-01

The authors established the profile of the Internal Medicine clinical teachers in Portugal aiming to define a future interventional strategy plan as adequate as possible to the target group and to the problems identified by the residents. Observational, transversal, analytic study. An online anonymous questionnaire was defined, evaluating the demographic characteristics of the clinical teachers, their path in Internal Medicine and their involvement in the residents learning process. We collected 213 valid questionnaires, making for an estimated response rate of 28.4%. Median global satisfaction with the clinical teacher was 4.52 (± 1.33 points) and the classification of the relationship between resident and clinical teacher was 4.86 ± 1.04 points. The perfect clinical teacher is defined by high standards of dedication and responsibility (4.9 ± 1.37 points), practical (4.8 ± 1.12 points) and theoretical skills (4.8 ± 1.07 points). The multiple linear regression model allowed to determine predictors of the residentâs satisfaction with their clinical teacher, justifying 82,5% of the variation of satisfaction with the clinical teacher (R2 = 0.83; R2 a = 0.82). Postgraduate medical education consists of an interaction between several areas of knowledge and intervening variables in the learning process having the clinical teacher in the central role. Overall, the pedagogical abilities were the most valued by the Internal Medicine residents regarding their clinical teacher, as determinants of a quality residentship. This study demonstrates the critical relevance of the clinical teacher in the satisfaction of residents with their residentship. The established multiple linear regression model highlights the impact of the clinical and pedagogical relantionship with the clinical teacher in a relevant increase in the satisfaction with the latter.

The Value of Performance Measurement in Promoting Improvements in Women's Health.

PubMed

Siu, Emily C Y; Levinton, Carey; Brown, Adalsteinn D

2009-11-01

To determine the factors associated with the use and impact of performance data relevant to women's health. We developed a survey on six levels of information use based on Knott and Wildavsky's (1980) policy utilization framework and used this survey to determine Ontario hospital administrators' use of women's health report indicators. We related responses to this survey to six potentially relevant organizational factors, such as women's health as a written hospital priority, a women's health program and hospital budget size, using correlation and multiple-regression analysis. Only women's health in a written hospital priority (p=0.01) and hospital budget (p=0.02, log transformed) were significantly associated with the highest level of use when all organizational factors were considered. These findings suggest that the use of women's health performance indicators is strongly related to the size of the hospital budget and to organizational commitment to women's health.

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

PubMed Central

Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-01-01

Background Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Methods Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. Results A ‘Rich Picture’ was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. Conclusions When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration. PMID:28062603

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).

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

PubMed

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

2014-12-01

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

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation

PubMed Central

MANEL, STÉPHANIE; GUGERLI, FELIX; THUILLER, WILFRIED; ALVAREZ, NADIR; LEGENDRE, PIERRE; HOLDEREGGER, ROLF; GIELLY, LUDOVIC; TABERLET, PIERRE

2014-01-01

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran’s eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps. PMID:22680783

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

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

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

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

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

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

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

Treesearch

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

1994-01-01

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

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

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

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

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

1979-01-01

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

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.

PubMed

Liu, Gang; Mukherjee, Bhramar; Lee, Seunggeun; Lee, Alice W; Wu, Anna H; Bandera, Elisa V; Jensen, Allan; Rossing, Mary Anne; Moysich, Kirsten B; Chang-Claude, Jenny; Doherty, Jennifer A; Gentry-Maharaj, Aleksandra; Kiemeney, Lambertus; Gayther, Simon A; Modugno, Francesmary; Massuger, Leon; Goode, Ellen L; Fridley, Brooke L; Terry, Kathryn L; Cramer, Daniel W; Ramus, Susan J; Anton-Culver, Hoda; Ziogas, Argyrios; Tyrer, Jonathan P; Schildkraut, Joellen M; Kjaer, Susanne K; Webb, Penelope M; Ness, Roberta B; Menon, Usha; Berchuck, Andrew; Pharoah, Paul D; Risch, Harvey; Pearce, Celeste Leigh

2018-02-01

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Personality and self-reported use of mobile phones for games.

PubMed

Phillips, James G; Butt, Sarah; Blaszczynski, Alex

2006-12-01

Mobile phones are popular devices that may generate problems for a section of the community. A previous study using the Eysenck Personality Questionnaire found that extraverts with low self-esteem reported more problems with their mobile phone use. The present study used the NEO FI and Coopersmith Self-Esteem Inventory to predict the self reported mobile phone use of 112 participants. Multiple regression found that people low on agreeableness were more likely to use their mobile phones to play games. The findings imply an interplay between personality traits and excessive or problematic use on mobile phones that is relevant to proposed innovations such as gambling on mobile phones.

Multiple Peer Group Self-Identification and Adolescent Tobacco Use

PubMed Central

Fuqua, Juliana L.; Gallaher, Peggy E.; Unger, Jennifer B.; Trinidad, Dennis R.; Sussman, Steve; Ortega, Enrique; Johnson, C. Anderson

2014-01-01

Associations between peer group self-identification and smoking were examined among 2,698 ethnically diverse middle school students in Los Angeles who self-identified with groups such as Rockers, Skaters, and Gamers. The sample was 47.1% male, 54.7% Latino, 25.4% Asian, 10.8% White, 9.1% Other ethnicity, and 59.3% children of immigrant parents. Multiple group self-identification was common: 84% identified with two or more groups and 65% identified with three or more groups. Logistic regression analyses indicated that as students endorsed more high-risk groups, the greater their risk of tobacco use. A classification tree analysis identified risk groups based on interactions among ethnicity, gender, and group self-identification. Psychographic targeting based on group self-identification could be useful to design more relevant smoking prevention messages for adolescents who identify with high-risk peer groups. PMID:22458850

Using Flow-Ecology Relationships to Evaluate Ecosystem Service Trade-Offs and Complementarities in the Nation's Largest River Swamp.

PubMed

Kozak, Justin P; Bennett, Micah G; Hayden-Lesmeister, Anne; Fritz, Kelley A; Nickolotsky, Aaron

2015-06-01

Large river systems are inextricably linked with social systems; consequently, management decisions must be made within a given ecological, social, and political framework that often defies objective, technical resolution. Understanding flow-ecology relationships in rivers is necessary to assess potential impacts of management decisions, but translating complex flow-ecology relationships into stakeholder-relevant information remains a struggle. The concept of ecosystem services provides a bridge between flow-ecology relationships and stakeholder-relevant data. Flow-ecology relationships were used to explore complementary and trade-off relationships among 12 ecosystem services and related variables in the Atchafalaya River Basin, Louisiana. Results from Indicators of Hydrologic Alteration were reduced to four management-relevant hydrologic variables using principal components analysis. Multiple regression was used to determine flow-ecology relationships and Pearson correlation coefficients, along with regression results, were used to determine complementary and trade-off relationships among ecosystem services and related variables that were induced by flow. Seven ecosystem service variables had significant flow-ecology relationships for at least one hydrologic variable (R (2) = 0.19-0.64). River transportation and blue crab (Callinectes sapidus) landings exhibited a complementary relationship mediated by flow; whereas transportation and crawfish landings, crawfish landings and crappie (Pomoxis spp.) abundance, and blue crab landings and blue catfish (Ictalurus furcatus) abundance exhibited trade-off relationships. Other trade-off and complementary relationships among ecosystem services and related variables, however, were not related to flow. These results give insight into potential conflicts among stakeholders, can reduce the dimensions of management decisions, and provide initial hypotheses for experimental flow modifications.

Using Flow-Ecology Relationships to Evaluate Ecosystem Service Trade-Offs and Complementarities in the Nation's Largest River Swamp

NASA Astrophysics Data System (ADS)

Kozak, Justin P.; Bennett, Micah G.; Hayden-Lesmeister, Anne; Fritz, Kelley A.; Nickolotsky, Aaron

2015-06-01

Large river systems are inextricably linked with social systems; consequently, management decisions must be made within a given ecological, social, and political framework that often defies objective, technical resolution. Understanding flow-ecology relationships in rivers is necessary to assess potential impacts of management decisions, but translating complex flow-ecology relationships into stakeholder-relevant information remains a struggle. The concept of ecosystem services provides a bridge between flow-ecology relationships and stakeholder-relevant data. Flow-ecology relationships were used to explore complementary and trade-off relationships among 12 ecosystem services and related variables in the Atchafalaya River Basin, Louisiana. Results from Indicators of Hydrologic Alteration were reduced to four management-relevant hydrologic variables using principal components analysis. Multiple regression was used to determine flow-ecology relationships and Pearson correlation coefficients, along with regression results, were used to determine complementary and trade-off relationships among ecosystem services and related variables that were induced by flow. Seven ecosystem service variables had significant flow-ecology relationships for at least one hydrologic variable ( R 2 = 0.19-0.64). River transportation and blue crab ( Callinectes sapidus) landings exhibited a complementary relationship mediated by flow; whereas transportation and crawfish landings, crawfish landings and crappie ( Pomoxis spp.) abundance, and blue crab landings and blue catfish ( Ictalurus furcatus) abundance exhibited trade-off relationships. Other trade-off and complementary relationships among ecosystem services and related variables, however, were not related to flow. These results give insight into potential conflicts among stakeholders, can reduce the dimensions of management decisions, and provide initial hypotheses for experimental flow modifications.

Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study

PubMed Central

Rosso, Luigi; Riatti, Riccardo

2016-01-01

The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6–16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, −0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases. PMID:27995136

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study.

PubMed

Perinetti, Giuseppe; Rosso, Luigi; Riatti, Riccardo; Contardo, Luca

2016-01-01

The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6-16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, -0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases.

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

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

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

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

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

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

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

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

2010-01-01

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

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

NASA Astrophysics Data System (ADS)

Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

2017-09-01

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

Health-relevant personality is associated with sensitivity to sound (hyperacusis).

PubMed

Villaume, Karin; Hasson, Dan

2017-04-01

Hyperacusis, over-sensitivity to sounds, causes distress and disability and the etiology is not fully understood. The study aims to explore possible associations between health-relevant personality traits and hyperacusis. Hyperacusis was assessed using the Hyperacusis Questionnaire (HQ), and clinical uncomfortable loudness levels (ULL). Personality was measured with the Health-relevant Personality (HP5i) Inventory. The study sample was 348 (140 men and 208 women; age 23-71 years). Moderate correlations were found between the personality trait negative affectivity (NA; a facet of neuroticism) and dimensions of the HQ and weak correlations were found with the ULLs. Hedonic capacity (a facet of extraversion) was significantly correlated with the HQ but not with the ULLs. Impulsivity (a facet of conscientiousness) was correlated with the HQ and the ULLs. A significant difference in mean values was found in all hyperacusis measures and different levels of NA - those with higher levels displayed more severe signs of hyperacusis. A multiple logistic regression analysis showed that higher levels of NA increases the odds of having hyperacusis on average 4.6 times for men and 2.4 times for women. These findings imply that health-relevant personality traits should be considered in the diagnosis and treatment of hyperacusis. © 2017 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

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

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

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

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

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

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

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

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

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

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

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

Personality and Healthy Sleep: The Importance of Conscientiousness and Neuroticism

PubMed Central

Duggan, Katherine A.; Friedman, Howard S.; McDevitt, Elizabeth A.; Mednick, Sara C.

2014-01-01

Although previous research has shown personality and sleep are each substantial predictors of health throughout the lifespan, little is known about links between personality and healthy sleep patterns. This study examined Big Five personality traits and a range of factors related to sleep health in 436 university students (M age = 19.88, SD = 1.50, 50% Male). Valid self-report measures of personality, chronotype, sleep hygiene, sleep quality, and sleepiness were analyzed. To remove multicollinearity between personality factors, each sleep domain was regressed on relevant demographic and principal component-derived personality factors in multiple linear regressions. Results showed that low conscientiousness and high neuroticism were the best predictors of poor sleep (poor sleep hygiene, low sleep quality, and increased sleepiness), consistent with other research on predictors of poor health and mortality risk. In this first comprehensive study of the topic, the findings suggest that personality has a significant association with sleep health, and researchers could profitably examine both personality and sleep in models of health and well-being. PMID:24651274

Gambling disorder-related illegal acts: Regression model of associated factors

PubMed Central

Gorsane, Mohamed Ali; Reynaud, Michel; Vénisse, Jean-Luc; Legauffre, Cindy; Valleur, Marc; Magalon, David; Fatséas, Mélina; Chéreau-Boudet, Isabelle; Guilleux, Alice; JEU Group; Challet-Bouju, Gaëlle; Grall-Bronnec, Marie

2017-01-01

Background and aims Gambling disorder-related illegal acts (GDRIA) are often crucial events for gamblers and/or their entourage. This study was designed to determine the predictive factors of GDRIA. Methods Participants were 372 gamblers reporting at least three DSM-IV-TR (American Psychiatric Association, 2000) criteria. They were assessed on the basis of sociodemographic characteristics, gambling-related characteristics, their personality profile, and psychiatric comorbidities. A multiple logistic regression was performed to identify the relevant predictors of GDRIA and their relative contribution to the prediction of the presence of GDRIA. Results Multivariate analysis revealed a higher South Oaks Gambling Scale score, comorbid addictive disorders, and a lower level of income as GDRIA predictors. Discussion and conclusion An original finding of this study was that the comorbid addictive disorder effect might be mediated by a disinhibiting effect of stimulant substances on GDRIA. Further studies are necessary to replicate these results, especially in a longitudinal design, and to explore specific therapeutic interventions. PMID:28198636

Gambling disorder-related illegal acts: Regression model of associated factors.

PubMed

Gorsane, Mohamed Ali; Reynaud, Michel; Vénisse, Jean-Luc; Legauffre, Cindy; Valleur, Marc; Magalon, David; Fatséas, Mélina; Chéreau-Boudet, Isabelle; Guilleux, Alice; Challet-Bouju, Gaëlle; Grall-Bronnec, Marie

2017-03-01

Background and aims Gambling disorder-related illegal acts (GDRIA) are often crucial events for gamblers and/or their entourage. This study was designed to determine the predictive factors of GDRIA. Methods Participants were 372 gamblers reporting at least three DSM-IV-TR (American Psychiatric Association, 2000) criteria. They were assessed on the basis of sociodemographic characteristics, gambling-related characteristics, their personality profile, and psychiatric comorbidities. A multiple logistic regression was performed to identify the relevant predictors of GDRIA and their relative contribution to the prediction of the presence of GDRIA. Results Multivariate analysis revealed a higher South Oaks Gambling Scale score, comorbid addictive disorders, and a lower level of income as GDRIA predictors. Discussion and conclusion An original finding of this study was that the comorbid addictive disorder effect might be mediated by a disinhibiting effect of stimulant substances on GDRIA. Further studies are necessary to replicate these results, especially in a longitudinal design, and to explore specific therapeutic interventions.

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

Relationship of negative self-schemas and attachment styles with appearance schemas.

PubMed

Ledoux, Tracey; Winterowd, Carrie; Richardson, Tamara; Clark, Julie Dorton

2010-06-01

The purpose was to test, among women, the relationship between negative self-schemas and styles of attachment with men and women and two types of appearance investment (Self-evaluative and Motivational Salience). Predominantly Caucasian undergraduate women (N=194) completed a modified version of the Relationship Questionnaire, the Young Schema Questionnaire-Short Form, and the Appearance Schemas Inventory-Revised. Linear multiple regression analyses were conducted with Motivational Salience and Self-evaluative Salience of appearance serving as dependent variables and relevant demographic variables, negative self-schemas, and styles of attachment to men serving as independent variables. Styles of attachment to women were not entered into these regression models because Pearson correlations indicated they were not related to either dependent variable. Self-evaluative Salience of appearance was related to impaired autonomy and performance negative self-schema and the preoccupation style of attachment with men, while Motivational Salience of appearance was related only to the preoccupation style of attachment with men. 2010 Elsevier Ltd. All rights reserved.

Predictive ability of a comprehensive incremental test in mountain bike marathon

PubMed Central

Schneeweiss, Patrick; Martus, Peter; Niess, Andreas M; Krauss, Inga

2018-01-01

Objectives Traditional performance tests in mountain bike marathon (XCM) primarily quantify aerobic metabolism and may not describe the relevant capacities in XCM. We aimed to validate a comprehensive test protocol quantifying its intermittent demands. Methods Forty-nine athletes (38.8±9.1 years; 38 male; 11 female) performed a laboratory performance test, including an incremental test, to determine individual anaerobic threshold (IAT), peak power output (PPO) and three maximal efforts (10 s all-out sprint, 1 min maximal effort and 5 min maximal effort). Within 2 weeks, the athletes participated in one of three XCM races (n=15, n=9 and n=25). Correlations between test variables and race times were calculated separately. In addition, multiple regression models of the predictive value of laboratory outcomes were calculated for race 3 and across all races (z-transformed data). Results All variables were correlated with race times 1, 2 and 3: 10 s all-out sprint (r=−0.72; r=−0.59; r=−0.61), 1 min maximal effort (r=−0.85; r=−0.84; r=−0.82), 5 min maximal effort (r=−0.57; r=−0.85; r=−0.76), PPO (r=−0.77; r=−0.73; r=−0.76) and IAT (r=−0.71; r=−0.67; r=−0.68). The best-fitting multiple regression models for race 3 (r2=0.868) and across all races (r2=0.757) comprised 1 min maximal effort, IAT and body weight. Conclusion Aerobic and intermittent variables correlated least strongly with race times. Their use in a multiple regression model confirmed additional explanatory power to predict XCM performance. These findings underline the usefulness of the comprehensive incremental test to predict performance in that sport more precisely. PMID:29387445

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

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

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

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

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

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

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

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

PubMed

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

2018-02-12

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

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

ERIC Educational Resources Information Center

Kromrey, Jeffrey D.; Hines, Constance V.

1995-01-01

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

Enhance-Synergism and Suppression Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, W. Michael

2004-01-01

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

Subjective social status and readiness to quit among homeless smokers.

PubMed

Garey, Lorra; Reitzel, Lorraine R; Bakhshaie, Jafar; Kendzor, Darla E; Zvolensky, Michael J; Businelle, Michael S

2015-03-01

To explore the predictive value of subjective social status (SSS-US and SSS-Community) on readiness to quit among 245 homeless smokers. Hierarchical multiple regression analyses were conducted (stratified by sex). Higher SSS-US (p = .02) and SSS-Community (p < .001) predicted greater readiness to quit in the total sample. These relationships upheld for men (p's <. 01), but only SSS-Community predicted readiness to quit for women (p = .02). Higher SSS is associated with greater readiness to quit among homeless smokers. SSS-Community may be a more relevant index of SSS for women relative to SSS-US. Results suggest SSS may be a factor that contributes to smoking, disease, and health disparities.

Premorbid personality characteristics and attachment style moderate the effect of injury severity on occupational outcome in traumatic brain injury: another aspect of reserve.

PubMed

Sela-Kaufman, Michal; Rassovsky, Yuri; Agranov, Eugenia; Levi, Yifat; Vakil, Eli

2013-01-01

The concept of "reserve" has been proposed to account for the mismatch between brain pathology and its clinical expression. Prior efforts to characterize this concept focused mostly on brain or cognitive reserve measures. The present study was a preliminary attempt to evaluate premorbid personality and emotional aspects as potential moderators in moderate-to-severe traumatic brain injury. Using structural equation modeling and multiple regression analyses, we found that premorbid personality characteristics provided the most robust moderator of injury severity on occupational outcome. Findings offer preliminary support for premorbid personality features as another relevant reserve construct in predicting outcome in this population.

Predicting the presence and cover of management relevant invasive plant species on protected areas.

PubMed

Iacona, Gwenllian; Price, Franklin D; Armsworth, Paul R

2016-01-15

Invasive species are a management concern on protected areas worldwide. Conservation managers need to predict infestations of invasive plants they aim to treat if they want to plan for long term management. Many studies predict the presence of invasive species, but predictions of cover are more relevant for management. Here we examined how predictors of invasive plant presence and cover differ across species that vary in their management priority. To do so, we used data on management effort and cover of invasive plant species on central Florida protected areas. Using a zero-inflated multiple regression framework, we showed that protected area features can predict the presence and cover of the focal species but the same features rarely explain both. There were several predictors of either presence or cover that were important across multiple species. Protected areas with three days of frost per year or fewer were more likely to have occurrences of four of the six focal species. When invasive plants were present, their proportional cover was greater on small preserves for all species, and varied with surrounding household density for three species. None of the predictive features were clearly related to whether species were prioritized for management or not. Our results suggest that predictors of cover and presence can differ both within and across species but do not covary with management priority. We conclude that conservation managers need to select predictors of invasion with care as species identity can determine the relationship between predictors of presence and the more management relevant predictors of cover. Copyright © 2015 Elsevier Ltd. All rights reserved.

Are you interested? A meta-analysis of relations between vocational interests and employee performance and turnover.

PubMed

Van Iddekinge, Chad H; Roth, Philip L; Putka, Dan J; Lanivich, Stephen E

2011-11-01

A common belief among researchers is that vocational interests have limited value for personnel selection. However, no comprehensive quantitative summaries of interests validity research have been conducted to substantiate claims for or against the use of interests. To help address this gap, we conducted a meta-analysis of relations between interests and employee performance and turnover using data from 74 studies and 141 independent samples. Overall validity estimates (corrected for measurement error in the criterion but not for range restriction) for single interest scales were .14 for job performance, .26 for training performance, -.19 for turnover intentions, and -.15 for actual turnover. Several factors appeared to moderate interest-criterion relations. For example, validity estimates were larger when interests were theoretically relevant to the work performed in the target job. The type of interest scale also moderated validity, such that corrected validities were larger for scales designed to assess interests relevant to a particular job or vocation (e.g., .23 for job performance) than for scales designed to assess a single, job-relevant realistic, investigative, artistic, social, enterprising, or conventional (i.e., RIASEC) interest (.10) or a basic interest (.11). Finally, validity estimates were largest when studies used multiple interests for prediction, either by using a single job or vocation focused scale (which tend to tap multiple interests) or by using a regression-weighted composite of several RIASEC or basic interest scales. Overall, the results suggest that vocational interests may hold more promise for predicting employee performance and turnover than researchers may have thought. (c) 2011 APA, all rights reserved.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

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

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

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

2008-01-01

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

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

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

Incremental Net Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, Michael

2005-01-01

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

Floating Data and the Problem with Illustrating Multiple Regression.

ERIC Educational Resources Information Center

Sachau, Daniel A.

2000-01-01

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

Ego and spiritual transcendence: relevance to psychological resilience and the role of age.

PubMed

Hanfstingl, Barbara

2013-01-01

The paper investigates different approaches of transcendence in the sense of spiritual experience as predictors for general psychological resilience. This issue is based on the theoretical assumption that resilience does play a role for physical health. Furthermore, there is a lack of empirical evidence about the extent to which spirituality does play a role for resilience. As potential predictors for resilience, ego transcendence, spiritual transcendence, and meaning in life were measured in a sample of 265 people. The main result of a multiple regression analysis is that, in the subsample with people below 29 years, only one rather secular scale that is associated with ego transcendence predicts resilience, whereas for the older subsample of 29 years and above, spiritual transcendence gains both a positive (oneness and timelessness) and a negative (spiritual insight) relevance to psychological resilience. On the one hand, these results concur with previous studies that also found age-related differences. On the other hand, it is surprising that the MOS spiritual insight predicts psychological resilience negatively, the effect is increasing with age. One possible explanation concerns wisdom research. Here, an adaptive way of dealing with the age-related loss of control is assumed to be relevant to successful aging.

Acceptance of health information technology in health professionals: an application of the revised technology acceptance model.

PubMed

Ketikidis, Panayiotis; Dimitrovski, Tomislav; Lazuras, Lambros; Bath, Peter A

2012-06-01

The response of health professionals to the use of health information technology (HIT) is an important research topic that can partly explain the success or failure of any HIT application. The present study applied a modified version of the revised technology acceptance model (TAM) to assess the relevant beliefs and acceptance of HIT systems in a sample of health professionals (n = 133). Structured anonymous questionnaires were used and a cross-sectional design was employed. The main outcome measure was the intention to use HIT systems. ANOVA was employed to examine differences in TAM-related variables between nurses and medical doctors, and no significant differences were found. Multiple linear regression analysis was used to assess the predictors of HIT usage intentions. The findings showed that perceived ease of use, but not usefulness, relevance and subjective norms directly predicted HIT usage intentions. The present findings suggest that a modification of the original TAM approach is needed to better understand health professionals' support and endorsement of HIT. Perceived ease of use, relevance of HIT to the medical and nursing professions, as well as social influences, should be tapped by information campaigns aiming to enhance support for HIT in healthcare settings.

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

DTIC Science & Technology

2017-03-23

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

Neuroanatomical and resting state EEG power correlates of central hearing loss in older adults.

PubMed

Giroud, Nathalie; Hirsiger, Sarah; Muri, Raphaela; Kegel, Andrea; Dillier, Norbert; Meyer, Martin

2018-01-01

To gain more insight into central hearing loss, we investigated the relationship between cortical thickness and surface area, speech-relevant resting state EEG power, and above-threshold auditory measures in older adults and younger controls. Twenty-three older adults and 13 younger controls were tested with an adaptive auditory test battery to measure not only traditional pure-tone thresholds, but also above individual thresholds of temporal and spectral processing. The participants' speech recognition in noise (SiN) was evaluated, and a T1-weighted MRI image obtained for each participant. We then determined the cortical thickness (CT) and mean cortical surface area (CSA) of auditory and higher speech-relevant regions of interest (ROIs) with FreeSurfer. Further, we obtained resting state EEG from all participants as well as data on the intrinsic theta and gamma power lateralization, the latter in accordance with predictions of the Asymmetric Sampling in Time hypothesis regarding speech processing (Poeppel, Speech Commun 41:245-255, 2003). Methodological steps involved the calculation of age-related differences in behavior, anatomy and EEG power lateralization, followed by multiple regressions with anatomical ROIs as predictors for auditory performance. We then determined anatomical regressors for theta and gamma lateralization, and further constructed all regressions to investigate age as a moderator variable. Behavioral results indicated that older adults performed worse in temporal and spectral auditory tasks, and in SiN, despite having normal peripheral hearing as signaled by the audiogram. These behavioral age-related distinctions were accompanied by lower CT in all ROIs, while CSA was not different between the two age groups. Age modulated the regressions specifically in right auditory areas, where a thicker cortex was associated with better auditory performance in older adults. Moreover, a thicker right supratemporal sulcus predicted more rightward theta lateralization, indicating the functional relevance of the right auditory areas in older adults. The question how age-related cortical thinning and intrinsic EEG architecture relates to central hearing loss has so far not been addressed. Here, we provide the first neuroanatomical and neurofunctional evidence that cortical thinning and lateralization of speech-relevant frequency band power relates to the extent of age-related central hearing loss in older adults. The results are discussed within the current frameworks of speech processing and aging.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

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

2012-01-01

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

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

PubMed

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

2012-01-01

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

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings.

PubMed

Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-08-01

Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. A 'Rich Picture' was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration. 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/.

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Alpha-synuclein levels in patients with multiple system atrophy: a meta-analysis.

PubMed

Yang, Fei; Li, Wan-Jun; Huang, Xu-Sheng

2018-05-01

This study evaluates the relationship between multiple system atrophy and α-synuclein levels in the cerebrospinal fluid, plasma and neural tissue. Literature search for relevant research articles was undertaken in electronic databases and study selection was based on a priori eligibility criteria. Random-effects meta-analyses of standardized mean differences in α-synuclein levels between multiple system atrophy patients and normal controls were conducted to obtain the overall and subgroup effect sizes. Meta-regression analyses were performed to evaluate the effect of age, gender and disease severity on standardized mean differences. Data were obtained from 11 studies involving 378 multiple system atrophy patients and 637 healthy controls (age: multiple system atrophy patients 64.14 [95% confidence interval 62.05, 66.23] years; controls 64.16 [60.06, 68.25] years; disease duration: 44.41 [26.44, 62.38] months). Cerebrospinal fluid α-synuclein levels were significantly lower in multiple system atrophy patients than in controls but in plasma and neural tissue, α-synuclein levels were significantly higher in multiple system atrophy patients (standardized mean difference: -0.99 [-1.65, -0.32]; p = 0.001). Percentage of male multiple system atrophy patients was significantly positively associated with the standardized mean differences of cerebrospinal fluid α-synuclein levels (p = 0.029) whereas the percentage of healthy males was not associated with the standardized mean differences of cerebrospinal fluid α-synuclein levels (p = 0.920). In multiple system atrophy patients, α-synuclein levels were significantly lower in the cerebrospinal fluid and were positively associated with the male gender.

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.

Air Pollutants, Climate, and the Prevalence of Pediatric Asthma in Urban Areas of China

PubMed Central

Zhang, Juanjuan; Yan, Li; Fu, Wenlong; Yi, Jing; Chen, Yuzhi; Liu, Chuanhe; Xu, Dongqun; Wang, Qiang

2016-01-01

Background. Prevalence of childhood asthma varies significantly among regions, while its reasons are not clear yet with only a few studies reporting relevant causes for this variation. Objective. To investigate the potential role of city-average levels of air pollutants and climatic factors in order to distinguish differences in asthma prevalence in China and explain their reasons. Methods. Data pertaining to 10,777 asthmatic patients were obtained from the third nationwide survey of childhood asthma in China's urban areas. Annual mean concentrations of air pollutants and other climatic factors were obtained for the same period from several government departments. Data analysis was implemented with descriptive statistics, Pearson correlation coefficient, and multiple regression analysis. Results. Pearson correlation analysis showed that the situation of childhood asthma was strongly linked with SO2, relative humidity, and hours of sunshine (p < 0.05). Multiple regression analysis indicated that, among the predictor variables in the final step, SO2 was found to be the most powerful predictor variable amongst all (β = −19.572, p < 0.05). Furthermore, results had shown that hours of sunshine (β = −0.014, p < 0.05) was a significant component summary predictor variable. Conclusion. The findings of this study do not suggest that air pollutants or climate, at least in terms of children, plays a major role in explaining regional differences in asthma prevalence in China. PMID:27556031

Factors Associated with Dental Caries in Primary Dentition in a Non-Fluoridated Rural Community of New South Wales, Australia

PubMed Central

Arora, Amit; Manohar, Narendar

2017-01-01

Dental caries persists as one of the most prevalent chronic diseases among children worldwide. This study aims to determine factors that influence dental caries in primary dentition among primary school children residing in the rural non-fluoridated community of Lithgow, New South Wales, Australia. A total of 495 children aged 5–10 years old from all the six primary schools in Lithgow were approached to participate in a cross-sectional survey prior to implementation of water fluoridation in 2014. Following parental consent, children were clinically examined for caries in their primary teeth, and parents were requested to complete a questionnaire on previous fluoride exposure, diet and relevant socio-demographic characteristics that influence oral health. Multiple logistic regression analysis was employed to examine the independent risk factors of primary dentition caries. Overall, 51 percent of children had dental caries in one or more teeth. In the multiple logistic regression analysis, child’s age (Adjusted Odd’s Ratio (AOR) = 1.30, 95% CI: 1.14–1.49) and mother’s extraction history (AOR = 2.05, 95% CI: 1.40–3.00) were significantly associated with caries experience in the child’s primary teeth. In addition, each serve of chocolate consumption was associated with 52 percent higher odds (AOR = 1.52, 95% CI: 1.19–1.93) of primary dentition caries. PMID:29168780

Factors associated to clinical learning in nursing students in primary health care: an analytical cross-sectional study

PubMed Central

Serrano-Gallardo, Pilar; Martínez-Marcos, Mercedes; Espejo-Matorrales, Flora; Arakawa, Tiemi; Magnabosco, Gabriela Tavares; Pinto, Ione Carvalho

2016-01-01

ABSTRACT Objective: to identify the students' perception about the quality of clinical placements and asses the influence of the different tutoring processes in clinical learning. Methods: analytical cross-sectional study on second and third year nursing students (n=122) about clinical learning in primary health care. The Clinical Placement Evaluation Tool and a synthetic index of attitudes and skills were computed to give scores to the clinical learning (scale 0-10). Univariate, bivariate and multivariate (multiple linear regression) analyses were performed. Results: the response rate was 91.8%. The most commonly identified tutoring process was "preceptor-professor" (45.2%). The clinical placement was assessed as "optimal" by 55.1%, relationship with team-preceptor was considered good by 80.4% of the cases and the average grade for clinical learning was 7.89. The multiple linear regression model with more explanatory capacity included the variables "Academic year" (beta coefficient = 1.042 for third-year students), "Primary Health Care Area (PHC)" (beta coefficient = 0.308 for Area B) and "Clinical placement perception" (beta coefficient = - 0.204 for a suboptimal perception). Conclusions: timeframe within the academic program, location and clinical placement perception were associated with students' clinical learning. Students' perceptions of setting quality were positive and a good team-preceptor relationship is a matter of relevance. PMID:27627124

Factors Associated with Dental Caries in Primary Dentition in a Non-Fluoridated Rural Community of New South Wales, Australia.

PubMed

Arora, Amit; Manohar, Narendar; John, James Rufus

2017-11-23

Dental caries persists as one of the most prevalent chronic diseases among children worldwide. This study aims to determine factors that influence dental caries in primary dentition among primary school children residing in the rural non-fluoridated community of Lithgow, New South Wales, Australia. A total of 495 children aged 5-10 years old from all the six primary schools in Lithgow were approached to participate in a cross-sectional survey prior to implementation of water fluoridation in 2014. Following parental consent, children were clinically examined for caries in their primary teeth, and parents were requested to complete a questionnaire on previous fluoride exposure, diet and relevant socio-demographic characteristics that influence oral health. Multiple logistic regression analysis was employed to examine the independent risk factors of primary dentition caries. Overall, 51 percent of children had dental caries in one or more teeth. In the multiple logistic regression analysis, child's age (Adjusted Odd's Ratio (AOR) = 1.30, 95% CI: 1.14-1.49) and mother's extraction history (AOR = 2.05, 95% CI: 1.40-3.00) were significantly associated with caries experience in the child's primary teeth. In addition, each serve of chocolate consumption was associated with 52 percent higher odds (AOR = 1.52, 95% CI: 1.19-1.93) of primary dentition caries.

Correlates of willingness to engage in residential gardening: implications for health optimization in ibadan, Nigeria.

PubMed

Motunrayo Ibrahim, Fausat

2013-01-01

Gardening is a worthwhile adventure which engenders health op-timization. Yet, a dearth of evidences that highlights motivations to engage in gardening exists. This study examined willingness to engage in gardening and its correlates, including some socio-psychological, health related and socio-demographic variables. In this cross-sectional survey, 508 copies of a structured questionnaire were randomly self administered among a group of civil servants of Oyo State, Nigeria. Multi-item measures were used to assess variables. Step wise multiple regression analysis was used to identify predictors of willingness to engage in gar-dening Results: Simple percentile analysis shows that 71.1% of respondents do not own a garden. Results of step wise multiple regression analysis indicate that descriptive norm of gardening is a good predictor, social support for gardening is better while gardening self efficacy is the best predictor of willingness to engage in gardening (P< 0.001). Health consciousness, gardening response efficacy, education and age are not predictors of this willingness (P> 0.05). Results of t-test and ANOVA respectively shows that gender is not associated with this willingness (P> 0.05), but marital status is (P< 0.05).  Socio-psychological characteristics and being married are very rele-vant in motivations to engage in gardening. The nexus between gardening and health optimization appears to be highly obscured in this population.

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

ERIC Educational Resources Information Center

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

2010-01-01

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

Does Study Design Affect Redislocation Rates After Primary Shoulder Dislocations? A Systematic Review Comparing Prospective and Retrospective Studies.

PubMed

Gohal, Chetan; Rofaiel, James; Abouali, Jihad; Ayeni, Olufemi R; Pinsker, Ellie; Whelan, Daniel

2017-10-01

To compare recurrence rates between prospectively collected and retrospectively collected data on primary anterior shoulder dislocations, as this could influence the timing of surgical decision making. A comprehensive literature search of Medline, Embase, CINAHL, and hand searches was performed. Recurrence rates of anterior shoulder dislocations were collected from relevant articles, along with follow-up length, age, and gender. An independent sample t test was conducted to evaluate our hypothesis. A multiple linear regression model was used to examine the variance in recurrence rates while controlling for covariates. A total of 1,379 articles were identified, of which 25 were relevant to our study-16 prospective and 9 retrospective. The average rate of recurrence of anterior shoulder dislocations in retrospective studies (mean [M] = 45.2, standard deviation [SD] = 31.67) was not significantly different from that in prospective studies (M = 56.7, SD = 22.55). The 95% confidence interval for the difference of the means ranged from -34.05 to 10.91. After controlling for covariates with the multiple linear regression, only 1.9% of the variance in recurrence rates was due to study type and was not significant (P = .42). The t test performed to evaluate our hypothesis was also not significant t(23) = -1.07, P = .298. When comparing prospective and retrospective studies, there was no significant difference in recurrence rates of primary anterior shoulder dislocations treated nonoperatively. The average redislocation rate was 56.7% in prospective studies and 45.2% in retrospective studies. Furthermore, the majority of this difference was accounted for by varying rates between age groups. Further research is needed to determine the risk of redislocation in specific age groups, to guide treatment decisions based on varying risk. Systematic review of Level II and III studies. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2017-05-01

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

Age-, sex-, and education-specific norms for an extended CERAD Neuropsychological Assessment Battery-Results from the population-based LIFE-Adult-Study.

PubMed

Luck, Tobias; Pabst, Alexander; Rodriguez, Francisca S; Schroeter, Matthias L; Witte, Veronica; Hinz, Andreas; Mehnert, Anja; Engel, Christoph; Loeffler, Markus; Thiery, Joachim; Villringer, Arno; Riedel-Heller, Steffi G

2018-05-01

To provide new age-, sex-, and education-specific reference values for an extended version of the well-established Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery (CERAD-NAB) that additionally includes the Trail Making Test and the Verbal Fluency Test-S-Words. Norms were calculated based on the cognitive performances of n = 1,888 dementia-free participants (60-79 years) from the population-based German LIFE-Adult-Study. Multiple regressions were used to examine the association of the CERAD-NAB scores with age, sex, and education. In order to calculate the norms, quantile and censored quantile regression analyses were performed estimating marginal means of the test scores at 2.28, 6.68, 10, 15.87, 25, 50, 75, and 90 percentiles for age-, sex-, and education-specific subgroups. Multiple regression analyses revealed that younger age was significantly associated with better cognitive performance in 15 CERAD-NAB measures and higher education with better cognitive performance in all 17 measures. Women performed significantly better than men in 12 measures and men than women in four measures. The determined norms indicate ceiling effects for the cognitive performances in the Boston Naming, Word List Recognition, Constructional Praxis Copying, and Constructional Praxis Recall tests. The new norms for the extended CERAD-NAB will be useful for evaluating dementia-free German-speaking adults in a broad variety of relevant cognitive domains. The extended CERAD-NAB follows more closely the criteria for the new DSM-5 Mild and Major Neurocognitive Disorder. Additionally, it could be further developed to include a test for social cognition. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Biologically-controlled multiple equilibria of tidal landforms and the fate of the Venice lagoon

NASA Astrophysics Data System (ADS)

Marani, Marco; D'Alpaos, Andrea; Lanzoni, Stefano; Carniello, Luca; Rinaldo, Andrea

2007-06-01

Looking across a tidal landscape, can one foresee the signs of impending shifts among different geomorphological structures? This is a question of paramount importance considering the ecological, cultural and socio-economic relevance of tidal environments and their worldwide decline. In this Letter we argue affirmatively by introducing a model of the coupled tidal physical and biological processes. Multiple equilibria, and transitions among them, appear in the evolutionary dynamics of tidal landforms. Vegetation type, disturbances of the benthic biofilm, sediment availability and marine transgressions or regressions drive the bio-geomorphic evolution of the system. Our approach provides general quantitative routes to model the fate of tidal landforms, which we illustrate in the case of the Venice lagoon (Italy), for which a large body of empirical observations exists spanning at least five centuries. Such observations are reproduced by the model, which also predicts that salt marshes in the Venice lagoon may not survive climatic changes in the next century if IPCC's scenarios of high relative sea level rise occur.

The Geometry of Enhancement in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.

2011-01-01

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

[Factors associated with incidence of dengue in Costa Rica].

PubMed

Mena, Nelson; Troyo, Adriana; Bonilla-Carrión, Roger; Calderón-Arguedas, Olger

2011-04-01

Determine the extent to which socioeconomic, demographic, geographic, and climate variables affected the incidence of dengue and dengue hemorrhagic fever (D/DH) in Costa Rica during the period 1999-2007. A correlational epidemiologic study was conducted that analyzed the cumulative incidence of D/DH from 1999 to 2007 and its association with different variables in the country's 81 cantons. Information was obtained from secondary sources, and the independent variables used for the analysis were selected on the basis of their representativeness in terms of sociodemographic, environmental, and health coverage factors that affect the epidemiology of D/DH. These variables were divided into four groups of indicators: demographic, socioeconomic, housing, and climate and geographical. The data were analyzed by means of simple and multiple Poisson regressions. The Costa Rican cantons with a higher incidence of D/DH were located primarily near the coast, coinciding with some of the variables studied. Temperature, altitude, and the human poverty index were the most relevant variables in explaining the incidence of D/DH, while temperature was the most significant variable in the multiple analyses. The analyses made it possible to correlate a higher incidence of D/DH with lower-altitude cantons, higher temperature, and a high human poverty index ranking. This information is relevant as a first step toward prioritizing and optimizing actions for the prevention and control of this disease.

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

PubMed

Marill, Keith A

2004-01-01

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

Squeezing observational data for better causal inference: Methods and examples for prevention research.

PubMed

Garcia-Huidobro, Diego; Michael Oakes, J

2017-04-01

Randomised controlled trials (RCTs) are typically viewed as the gold standard for causal inference. This is because effects of interest can be identified with the fewest assumptions, especially imbalance in background characteristics. Yet because conducting RCTs are expensive, time consuming and sometimes unethical, observational studies are frequently used to study causal associations. In these studies, imbalance, or confounding, is usually controlled with multiple regression, which entails strong assumptions. The purpose of this manuscript is to describe strengths and weaknesses of several methods to control for confounding in observational studies, and to demonstrate their use in cross-sectional dataset that use patient registration data from the Juan Pablo II Primary Care Clinic in La Pintana-Chile. The dataset contains responses from 5855 families who provided complete information on family socio-demographics, family functioning and health problems among their family members. We employ regression adjustment, stratification, restriction, matching, propensity score matching, standardisation and inverse probability weighting to illustrate the approaches to better causal inference in non-experimental data and compare results. By applying study design and data analysis techniques that control for confounding in different ways than regression adjustment, researchers may strengthen the scientific relevance of observational studies. © 2016 International Union of Psychological Science.

Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

It is now feasible to examine the composition and diversity of microbial communities (i.e., “microbiomes”) that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology “Metastats” across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained on a small to moderate number of samples. PMID:26734061

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

NASA Astrophysics Data System (ADS)

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

2011-08-01

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

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Visual feature extraction from voxel-weighted averaging of stimulus images in 2 fMRI studies.

PubMed

Hart, Corey B; Rose, William J

2013-11-01

Multiple studies have provided evidence for distributed object representation in the brain, with several recent experiments leveraging basis function estimates for partial image reconstruction from fMRI data. Using a novel combination of statistical decomposition, generalized linear models, and stimulus averaging on previously examined image sets and Bayesian regression of recorded fMRI activity during presentation of these data sets, we identify a subset of relevant voxels that appear to code for covarying object features. Using a technique we term "voxel-weighted averaging," we isolate image filters that these voxels appear to implement. The results, though very cursory, appear to have significant implications for hierarchical and deep-learning-type approaches toward the understanding of neural coding and representation.

Uric acid, lung function, physical capacity and exacerbation frequency in patients with COPD: a multi-dimensional approach.

PubMed

Kahnert, Kathrin; Alter, Peter; Welte, Tobias; Huber, Rudolf M; Behr, Jürgen; Biertz, Frank; Watz, Henrik; Bals, Robert; Vogelmeier, Claus F; Jörres, Rudolf A

2018-06-04

Recent investigations showed single associations between uric acid levels, functional parameters, exacerbations and mortality in COPD patients. The aim of this study was to describe the role of uric acid within the network of multiple relationships between function, exacerbation and comorbidities. We used baseline data from the German COPD cohort COSYCONET which were evaluated by standard multiple regression analyses as well as path analysis to quantify the network of relations between parameters, particularly uric acid. Data from 1966 patients were analyzed. Uric acid was significantly associated with reduced FEV 1 , reduced 6-MWD, higher burden of exacerbations (GOLD criteria) and cardiovascular comorbidities, in addition to risk factors such as BMI and packyears. These associations remained significant after taking into account their multiple interdependences. Compared to uric acid levels the diagnosis of hyperuricemia and its medication played a minor role. Within the limits of a cross-sectional approach, our results strongly suggest that uric acid is a biomarker of high impact in COPD and plays a genuine role for relevant outcomes such as physical capacity and exacerbations. These findings suggest that more attention should be paid to uric acid in the evaluation of COPD disease status.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

ERP correlates of word production predictors in picture naming: a trial by trial multiple regression analysis from stimulus onset to response.

PubMed

Valente, Andrea; Bürki, Audrey; Laganaro, Marina

2014-01-01

A major effort in cognitive neuroscience of language is to define the temporal and spatial characteristics of the core cognitive processes involved in word production. One approach consists in studying the effects of linguistic and pre-linguistic variables in picture naming tasks. So far, studies have analyzed event-related potentials (ERPs) during word production by examining one or two variables with factorial designs. Here we extended this approach by investigating simultaneously the effects of multiple theoretical relevant predictors in a picture naming task. High density EEG was recorded on 31 participants during overt naming of 100 pictures. ERPs were extracted on a trial by trial basis from picture onset to 100 ms before the onset of articulation. Mixed-effects regression models were conducted to examine which variables affected production latencies and the duration of periods of stable electrophysiological patterns (topographic maps). Results revealed an effect of a pre-linguistic variable, visual complexity, on an early period of stable electric field at scalp, from 140 to 180 ms after picture presentation, a result consistent with the proposal that this time period is associated with visual object recognition processes. Three other variables, word Age of Acquisition, Name Agreement, and Image Agreement influenced response latencies and modulated ERPs from ~380 ms to the end of the analyzed period. These results demonstrate that a topographic analysis fitted into the single trial ERPs and covering the entire processing period allows one to associate the cost generated by psycholinguistic variables to the duration of specific stable electrophysiological processes and to pinpoint the precise time-course of multiple word production predictors at once.

The M Word: Multicollinearity in Multiple Regression.

ERIC Educational Resources Information Center

Morrow-Howell, Nancy

1994-01-01

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

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

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

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

Kinetic therapy in multiple trauma patients with severe blunt chest trauma: an analysis at a level-1 trauma center.

PubMed

Zeckey, C; Wendt, K; Mommsen, P; Winkelmann, M; Frömke, C; Weidemann, J; Stübig, T; Krettek, C; Hildebrand, F

2015-01-01

Chest trauma is a relevant risk factor for mortality after multiple trauma. Kinetic therapy (KT) represents a potential treatment option in order to restore pulmonary function. Decision criteria for performing kinetic therapy are not fully elucidated. The purpose of this study was to investigate the decision making process to initiate kinetic therapy in a well defined multiple trauma cohort. A retrospective analysis (2000-2009) of polytrauma patients (age > 16 years, ISS ⩾ 16) with severe chest trauma (AIS(Chest) ⩾ 3) was performed. Patients with AIS(Head) ⩾ 3 were excluded. Patients receiving either kinetic (KT+) or lung protective ventilation strategy (KT-) were compared. Chest trauma was classified according to the AIS(Chest), Pulmonary Contusion Score (PCS), Wagner Jamieson Score and Thoracic Trauma Severity Score (TTS). There were multiple outcome parameters investigated included mortality, posttraumatic complications and clinical data. A multivariate regression analysis was performed. Two hundred and eighty-three patients were included (KT+: n=160; KT-: n=123). AIS(Chest), age and gender were comparable in both groups. There were significant higher values of the ISS, PCS, Wagner Jamieson Score and TTS in group KT+. The incidence of posttraumatic complications and mortality was increased compared to group KT- (p< 0.05). Despite that, kinetic therapy failed to be an independent risk factor for mortality in multivariate logistic regression analysis. Kinetic therapy is an option in severely injured patients with severe chest trauma. Decision making is not only based on anatomical aspects such as the AIS(Chest), but on overall injury severity, pulmonary contusions and physiological deterioration. It could be assumed that the increased mortality in patients receiving KT is primarily caused by these factors and does not reflect an independent adverse effect of KT. Furthermore, KT was not shown to be an independent risk factor for mortality.

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

PubMed

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

2017-11-01

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

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

ERIC Educational Resources Information Center

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

Ego and Spiritual Transcendence: Relevance to Psychological Resilience and the Role of Age

PubMed Central

2013-01-01

The paper investigates different approaches of transcendence in the sense of spiritual experience as predictors for general psychological resilience. This issue is based on the theoretical assumption that resilience does play a role for physical health. Furthermore, there is a lack of empirical evidence about the extent to which spirituality does play a role for resilience. As potential predictors for resilience, ego transcendence, spiritual transcendence, and meaning in life were measured in a sample of 265 people. The main result of a multiple regression analysis is that, in the subsample with people below 29 years, only one rather secular scale that is associated with ego transcendence predicts resilience, whereas for the older subsample of 29 years and above, spiritual transcendence gains both a positive (oneness and timelessness) and a negative (spiritual insight) relevance to psychological resilience. On the one hand, these results concur with previous studies that also found age-related differences. On the other hand, it is surprising that the MOS spiritual insight predicts psychological resilience negatively, the effect is increasing with age. One possible explanation concerns wisdom research. Here, an adaptive way of dealing with the age-related loss of control is assumed to be relevant to successful aging. PMID:24223619

Emergency diagnosis of cancer and previous general practice consultations: insights from linked patient survey data.

PubMed

Abel, Gary A; Mendonca, Silvia C; McPhail, Sean; Zhou, Yin; Elliss-Brookes, Lucy; Lyratzopoulos, Georgios

2017-06-01

Emergency diagnosis of cancer is common and aetiologically complex. The proportion of emergency presenters who have consulted previously with relevant symptoms is uncertain. To examine how many patients with cancer, who were diagnosed as emergencies, have had previous primary care consultations with relevant symptoms; and among those, to examine how many had multiple consultations. Secondary analysis of patient survey data from the 2010 English Cancer Patient Experience Survey (CPES), previously linked to population-based data on diagnostic route. For emergency presenters with 18 different cancers, associations were examined for two outcomes (prior GP consultation status; and 'three or more consultations' among prior consultees) using logistic regression. Among 4647 emergency presenters, 1349 (29%) reported no prior consultations, being more common in males (32% versus 25% in females, P <0.001), older (44% in ≥85 versus 30% in 65-74-year-olds, P <0.001), and the most deprived (35% versus 25% least deprived, P = 0.001) patients; and highest/lowest for patients with brain cancer (46%) and mesothelioma (13%), respectively ( P <0.001 for overall variation by cancer site). Among 3298 emergency presenters with prior consultations, 1356 (41%) had three or more consultations, which were more likely in females ( P <0.001), younger ( P <0.001), and non-white patients ( P = 0.017) and those with multiple myeloma, and least likely for patients with leukaemia ( P <0.001). Contrary to suggestions that emergency presentations represent missed diagnoses, about one-third of emergency presenters (particularly those in older and more deprived groups) have no prior GP consultations. Furthermore, only about one-third report multiple (three or more) consultations, which are more likely in 'harder-to-suspect' groups. © British Journal of General Practice 2017.

Emergency diagnosis of cancer and previous general practice consultations: insights from linked patient survey data

PubMed Central

Abel, Gary A; Mendonca, Silvia C; McPhail, Sean; Zhou, Yin; Elliss-Brookes, Lucy; Lyratzopoulos, Georgios

2017-01-01

Background Emergency diagnosis of cancer is common and aetiologically complex. The proportion of emergency presenters who have consulted previously with relevant symptoms is uncertain. Aim To examine how many patients with cancer, who were diagnosed as emergencies, have had previous primary care consultations with relevant symptoms; and among those, to examine how many had multiple consultations. Design and setting Secondary analysis of patient survey data from the 2010 English Cancer Patient Experience Survey (CPES), previously linked to population-based data on diagnostic route. Method For emergency presenters with 18 different cancers, associations were examined for two outcomes (prior GP consultation status; and ‘three or more consultations’ among prior consultees) using logistic regression. Results Among 4647 emergency presenters, 1349 (29%) reported no prior consultations, being more common in males (32% versus 25% in females, P<0.001), older (44% in ≥85 versus 30% in 65–74-year-olds, P<0.001), and the most deprived (35% versus 25% least deprived, P = 0.001) patients; and highest/lowest for patients with brain cancer (46%) and mesothelioma (13%), respectively (P<0.001 for overall variation by cancer site). Among 3298 emergency presenters with prior consultations, 1356 (41%) had three or more consultations, which were more likely in females (P<0.001), younger (P<0.001), and non-white patients (P = 0.017) and those with multiple myeloma, and least likely for patients with leukaemia (P<0.001). Conclusion Contrary to suggestions that emergency presentations represent missed diagnoses, about one-third of emergency presenters (particularly those in older and more deprived groups) have no prior GP consultations. Furthermore, only about one-third report multiple (three or more) consultations, which are more likely in ‘harder-to-suspect’ groups. PMID:28438775

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

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

EPA Science Inventory

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

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

EPA Science Inventory

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

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

PubMed Central

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

2016-01-01

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

HIV-related ocular microangiopathic syndrome and color contrast sensitivity.

PubMed

Geier, S A; Hammel, G; Bogner, J R; Kronawitter, U; Berninger, T; Goebel, F D

1994-06-01

Color vision deficits in patients with acquired immunodeficiency syndrome (AIDS) or human immunodeficiency virus (HIV) disease were reported, and a retinal pathogenic mechanism was proposed. The purpose of this study was to evaluate the association of color vision deficits with HIV-related retinal microangiopathy. A computer graphics system was used to measure protan, deutan, and tritan color contrast sensitivity (CCS) thresholds in 60 HIV-infected patients. Retinal microangiopathy was measured by counting the number of cotton-wool spots, and conjunctival blood-flow sludging was determined. Additional predictors were CD4+ count, age, time on aerosolized pentamidine, time on zidovudine, and Walter Reed staging. The relative influence of each predictor was calculated by stepwise multiple regression analysis (inclusion criterion; incremental P value = < 0.05) using data for the right eyes (RE). The results were validated by using data for the left eyes (LE) and both eyes (BE). The only included predictors in multiple regression analyses for the RE were number of cotton-wool spots (tritan: R = .70; deutan: R = .46; and protan: R = .58; P < .0001 for all axes) and age (tritan: increment of R [Ri] = .05, P = .002; deutan: Ri = .10, P = .004; and protan: Ri = .05, P = .002). The predictors time on zidovudine (Ri = .05, P = .002) and Walter Reed staging (Ri = .03, P = .01) were additionally included in multiple regression analysis for tritan LE. The results for deutan LE were comparable to those for the RE. In the analysis for protan LE, the only included predictor was number of cotton-wool spots. In the analyses for BE, no further predictors were included. The predictors Walter Reed staging and CD4+ count showed a significant association with all three criteria in univariate analysis. Additionally, tritan CCS was significantly associated with conjunctival blood-flow sludging. CCS deficits in patients with HIV disease are primarily associated with the number of cotton-wool spots. Results of this study are in accordance with the hypothesis that CCS deficits are in a relevant part caused by neuroretinal damage secondary to HIV-related microangiopathy.

Assessment of relevant factors and relationships concerning human dermal exposure to pesticides in greenhouse applications.

PubMed

Martínez Vidal, Jose L; Egea González, Francisco J; Garrido Frenich, Antonia; Martínez Galera, María; Aguilera, Pedro A; López Carrique, Enrique

2002-08-01

Principal component analysis (PCA) was applied to the gas chromatographic data obtained from 23 different greenhouse trials. This was used to establish which factors, including application technique (very small, small, medium and large drop-size), crop characteristics (short/tall, thin/dense) and pattern application of the operator (walking towards or away from the treated area) are relevant to the dermal exposure levels of greenhouse applicators. The results showed that the highest exposure by pesticides during field applications in greenhouses, in the climatic conditions and in the crop conditions typical of a southern European country, occurs on the lower legs and front thighs of the applicators. Similar results were obtained by hierarchical cluster analysis (HCA). Drop-size seems to be very important in determining total exposure, while height and density of crops have little influence on total exposure under the conditions of the present study. No pesticide type is a major factor in total exposure. The application of multiple regression analysis (MRA) allowed assessment of the relationships between the pesticide exposure of the less affected parts of the body with the most affected parts.

[Relevance of drug use in clinical manifestations of schizophrenia].

PubMed

Arias Horcajadas, F; Sánchez Romero, S; Padín Calo, J J

2002-01-01

To study the association between drugs use with schizophrenia clinical manifestations. The sample consists of 82 out-patients with schizophrenia, between 18 and 45 years old. They were evaluated with Addiction Severity Index (ASI) and with Positive and Negative Syndrome Scale (PANSS). A 6 months follow up was carried out. 37,8% patients had lifetime drug dependence (including alcohol and others drugs except for tobacco). The prevalence of dependence for the different drugs were: opioids 9,8%, cocaine 11%, alcohol 29,3%, cannabis 24,4%, tobacco 68,3%, caffeine 15,9%. Drug dependent had more family and legal problems. At the multiple regression analysis it was observed that cannabis and tobacco dependence was associated with a decrease in the PANSS negative symptoms subscale, and on the contrary, alcohol dependence produces a similar intensity increase at that scoring. We don't detect any clinical relevance effects over positive symptoms. Cannabis and tobacco may improve schizophrenia negative symptoms or neuroleptic secondary effects or patients with few negative symptoms may have more predisposition to the use, on the contrary alcohol use can impairment those symptoms.

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

PubMed

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

2017-02-01

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

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

DTIC Science & Technology

2013-01-01

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

The Impact of Financial Conflict of Interest on Surgical Research: An Observational Study of Published Manuscripts.

PubMed

Cherla, Deepa V; Viso, Cristina P; Olavarria, Oscar A; Bernardi, Karla; Holihan, Julie L; Mueck, Krislynn M; Flores-Gonzalez, Juan; Liang, Mike K; Adams, Sasha D

2018-02-09

Substantial discrepancies exist between industry-reported and self-reported conflicts of interest (COI). Although authors with relevant, self-reported financial COI are more likely to write studies favorable to industry sponsors, it is unknown whether undisclosed COI have the same effect. We hypothesized that surgeons who fail to disclose COI are more likely to publish findings that are favorable to industry than surgeons with no COI. PubMed was searched for articles in multiple surgical specialties. Financial COI reported by surgeons and industry were compared. COI were considered to be relevant if they were associated with the product(s) mentioned by an article. Primary outcome was favorability, which was defined as an impression favorable to the product(s) discussed by an article and was determined by 3 independent, blinded clinicians for each article. Primary analysis compared incomplete self-disclosure to no COI. Ordered logistic multivariable regression modeling was used to assess factors associated with favorability. Overall, 337 articles were reviewed. There was a high rate of discordance in the reporting of COI (70.3%). When surgeons failed to disclose COI, their conclusions were significantly more likely to favor industry than surgeons without COI (RR 1.2, 95% CI 1.1-1.4, p < 0.001). On multivariable analysis, any COI (regardless of relevance, disclosure, or monetary amount) were significantly associated with favorability. Any financial COI (disclosed or undisclosed, relevant or not relevant) significantly influence whether studies report findings favorable to industry. More attention must be paid to improving research design, maximizing transparency in medical research, and insisting that surgeons disclose all COI, regardless of perceived relevance.

Do drug treatment variables predict cognitive performance in multidrug-treated opioid-dependent patients? A regression analysis study

PubMed Central

2012-01-01

Background Cognitive deficits and multiple psychoactive drug regimens are both common in patients treated for opioid-dependence. Therefore, we examined whether the cognitive performance of patients in opioid-substitution treatment (OST) is associated with their drug treatment variables. Methods Opioid-dependent patients (N = 104) who were treated either with buprenorphine or methadone (n = 52 in both groups) were given attention, working memory, verbal, and visual memory tests after they had been a minimum of six months in treatment. Group-wise results were analysed by analysis of variance. Predictors of cognitive performance were examined by hierarchical regression analysis. Results Buprenorphine-treated patients performed statistically significantly better in a simple reaction time test than methadone-treated ones. No other significant differences between groups in cognitive performance were found. In each OST drug group, approximately 10% of the attention performance could be predicted by drug treatment variables. Use of benzodiazepine medication predicted about 10% of performance variance in working memory. Treatment with more than one other psychoactive drug (than opioid or BZD) and frequent substance abuse during the past month predicted about 20% of verbal memory performance. Conclusions Although this study does not prove a causal relationship between multiple prescription drug use and poor cognitive functioning, the results are relevant for psychosocial recovery, vocational rehabilitation, and psychological treatment of OST patients. Especially for patients with BZD treatment, other treatment options should be actively sought. PMID:23121989

Relation between burnout syndrome and job satisfaction among mental health workers.

PubMed

Ogresta, Jelena; Rusac, Silvia; Zorec, Lea

2008-06-01

To identify predictors of burnout syndrome, such as job satisfaction and manifestations of occupational stress, in mental health workers. The study included a snowball sample of 174 mental health workers in Croatia. The following measurement instruments were used: Maslach Burnout Inventory, Manifestations of Occupational Stress Survey, and Job Satisfaction Survey. We correlated dimensions of burnout syndrome with job satisfaction and manifestations of occupational stress dimensions. We also performed multiple regression analysis using three dimensions of burnout syndrome--emotional exhaustion, depersonalization, and personal accomplishment. Stepwise multiple regression analysis showed that pay and rewards satisfaction (beta=-0.37), work climate (beta=-0.18), advancement opportunities (beta=0.17), the degree of psychological (beta=0.41), and physical manifestations of occupational stress (beta=0.29) were significant predictors of emotional exhaustion (R=0.76; F=30.02; P<0.001). The frequency of negative emotional and behavioral reactions toward patients and colleagues (beta=0.48), psychological (beta=0.27) and physical manifestations of occupational stress (beta=0.24), and pay and rewards satisfaction (beta=0.22) were significant predictors of depersonalization (R=0.57; F=13,01; P<0.001). Satisfaction with the work climate (beta=-0.20) was a significant predictor of lower levels of personal accomplishment (R=0.20; F=5.06; P<0.005). Mental health workers exhibited a moderate degree of burnout syndrome, but there were no significant differences regarding their occupation. Generally, both dimensions of job satisfaction and manifestations of occupational stress proved to be relevant predictors of burnout syndrome.

Normality of raw data in general linear models: The most widespread myth in statistics

USGS Publications Warehouse

Kery, Marc; Hatfield, Jeff S.

2003-01-01

In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.

Relationship between dietary sodium, potassium, and calcium, anthropometric indexes, and blood pressure in young and middle aged Korean adults.

PubMed

Park, Juyeon; Lee, Jung-Sug; Kim, Jeongseon

2010-04-01

Epidemiological evidence of the effects of dietary sodium, calcium, and potassium, and anthropometric indexes on blood pressure is still inconsistent. To investigate the relationship between dietary factors or anthropometric indexes and hypertension risk, we examined the association of systolic and diastolic blood pressure (SBP and DBP) with sodium, calcium, and potassium intakes and anthropometric indexes in 19~49-year-olds using data from Korean National Health and Nutrition Examination Survey (KNHANES) III. Total of 2,761 young and middle aged adults (574 aged 19~29 years and 2,187 aged 30~49 years) were selected from KNHANES III. General information, nutritional status, and anthropometric data were compared between two age groups (19~29 years old and 30~49 years old). The relevance of blood pressure and risk factors such as age, sex, body mass index (BMI), weight, waist circumference, and the intakes of sodium, potassium, and calcium was determined by multiple regression analysis. Multiple regression models showed that waist circumference, weight, and BMI were positively associated with SBP and DBP in both age groups. Sodium and potassium intakes were not associated with either SBP or DBP. Among 30~49-year-olds, calcium was inversely associated with both SBP and DBP (P = 0.012 and 0.010, respectively). Our findings suggest that encouraging calcium consumption and weight control may play an important role in the primary prevention and management of hypertension in early adulthood.

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

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

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

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

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

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

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

Undergraduate Student Motivation in Modularized Developmental Mathematics Courses

ERIC Educational Resources Information Center

Pachlhofer, Keith A.

2017-01-01

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

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

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

Categorical Variables in Multiple Regression: Some Cautions.

ERIC Educational Resources Information Center

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

1988-01-01

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

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Advanced Statistics for Exotic Animal Practitioners.

PubMed

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

2017-09-01

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

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

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

1984-01-01

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

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

EPA Science Inventory

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

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

EPA Science Inventory

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

Tracking the Gender Pay Gap: A Case Study

ERIC Educational Resources Information Center

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

2009-01-01

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

Estimating air drying times of lumber with multiple regression

Treesearch

William T. Simpson

2004-01-01

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

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

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

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

Multiple Regression: A Leisurely Primer.

ERIC Educational Resources Information Center

Daniel, Larry G.; Onwuegbuzie, Anthony J.

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

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

ERIC Educational Resources Information Center

Vaughan, Timothy S.; Berry, Kelly E.

2005-01-01

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

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

ERIC Educational Resources Information Center

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

1999-01-01

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

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Impulsive responses to emotion as a transdiagnostic vulnerability to internalizing and externalizing symptoms.

PubMed

Johnson, Sheri L; Carver, Charles S; Joormann, Jutta

2013-09-25

This study explored the hypothesis that impulsive reactions to heightened emotion may reflect a transdiagnostic vulnerability to both externalizing and internalizing symptoms. A sample of undergraduates completed self-report measures of aggression, borderline personality disorder symptoms, anxiety symptoms, and alcohol problems, and a subset completed interviews that assessed suicidality. All participants also completed self-report measures relating to impulsivity. We predicted that emotion-reactive impulsivity, but not other aspects of impulsivity, would be related to the set of psychopathology symptoms. Multiple regression analyses found that emotion-reactive impulsivity was uniquely related to each of the psychopathology scales, whereas non-emotion-relevant impulsivity was uniquely related only to alcohol problems. Discussion focuses on limitations and clinical implications. © 2013 Elsevier B.V. All rights reserved.

Effects of integration time on in-water radiometric profiles.

PubMed

D'Alimonte, Davide; Zibordi, Giuseppe; Kajiyama, Tamito

2018-03-05

This work investigates the effects of integration time on in-water downward irradiance E d , upward irradiance E u and upwelling radiance L u profile data acquired with free-fall hyperspectral systems. Analyzed quantities are the subsurface value and the diffuse attenuation coefficient derived by applying linear and non-linear regression schemes. Case studies include oligotrophic waters (Case-1), as well as waters dominated by Colored Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP). Assuming a 24-bit digitization, measurements resulting from the accumulation of photons over integration times varying between 8 and 2048ms are evaluated at depths corresponding to: 1) the beginning of each integration interval (Fst); 2) the end of each integration interval (Lst); 3) the averages of Fst and Lst values (Avg); and finally 4) the values weighted accounting for the diffuse attenuation coefficient of water (Wgt). Statistical figures show that the effects of integration time can bias results well above 5% as a function of the depth definition. Results indicate the validity of the Wgt depth definition and the fair applicability of the Avg one. Instead, both the Fst and Lst depths should not be adopted since they may introduce pronounced biases in E u and L u regression products for highly absorbing waters. Finally, the study reconfirms the relevance of combining multiple radiometric casts into a single profile to increase precision of regression products.

The relationship between central corneal thickness and optic disc size in patients with primary open-angle glaucoma in a hospital-based population.

PubMed

Terai, Naim; Spoerl, Eberhard; Pillunat, Lutz E; Kuhlisch, Eberhard; Schmidt, Eckart; Boehm, Andreas G

2011-09-01

To investigate the relationship between central corneal thickness (CCT) and optic disc size in patients with primary open-angle glaucoma (POAG) in a hospital-based population. Data for the right eyes of 1435 White patients with POAG were included in a retrospective hospital-based study. All eyes underwent optic nerve head imaging using Heidelberg Retina Tomograph II (HRT II; Heidelberg Engineering, Heidelberg, Germany) and CCT measurement by ultrasound corneal pachymetry. Eyes with prior intraocular or corneal surgery were excluded. Low-quality HRT II images were also excluded. The impact of age, gender, CCT, intraocular pressure, cylindrical and spherical refractive error as independent factors on optic disc size was investigated in a multiple linear regression analysis. The data for 1104 right eyes qualified for participation in the study. The median age of these patients was 65 years. The median CCT was 547 μm (25th-75th percentile 522-575 μm). The median optic disc size was 2.21 mm(2) (25th-75th percentile 1.89-2.60 mm(2)). Multiple linear regression analysis revealed that age (p = 0.001), CCT (p = 0.001) and spherical equivalent (p = 0.049) were correlated to disc size according to the following formula: disc area = -0.004 × age - 0.001 × CCT - 0.014 × spherical equivalent +3.290. R(2) of the whole model was 0.021. Univariate regression analysis between age and disc area provided R(2) = 0.008 with p = 0.002. Univariate regression analysis between disc area and CCT provided R(2) = 0.005 with p = 0.02. In this retrospective hospital-based study we could not detect a clinically relevant correlation between optic disc size and CCT. The correlation between CCT and disc size and between age and disc size were statistically significant, but the R(2) values were very low. The results of the study are biased because of its hospital-based design, so the results of the study need to be confirmed in a large population-based study. © 2009 The Authors. Journal compilation © 2009 Acta Ophthalmol.

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.

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

NASA Astrophysics Data System (ADS)

He, Song-Bing; Ben Hu; Kuang, Zheng-Kun; Wang, Dong; Kong, De-Xin

2016-11-01

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2B vs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2A vs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

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

PubMed

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

2017-01-01

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

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

PubMed

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

2016-05-01

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

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

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

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

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

ERIC Educational Resources Information Center

Li, Spencer D.

2011-01-01

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

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…

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

ERIC Educational Resources Information Center

Accordino, Denise B.; Accordino, Michael P.

2011-01-01

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

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

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

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

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

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

1996-01-01

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

Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

ERIC Educational Resources Information Center

Rowell, R. Kevin

In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

The Effects of Clinically Relevant Multiple-Choice Items on the Statistical Discrimination of Physician Clinical Competence.

ERIC Educational Resources Information Center

Downing, Steven M.; Maatsch, Jack L.

To test the effect of clinically relevant multiple-choice item content on the validity of statistical discriminations of physicians' clinical competence, data were collected from a field test of the Emergency Medicine Examination, test items for the certification of specialists in emergency medicine. Two 91-item multiple-choice subscales were…

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

PubMed Central

Zhang, Shujun

2018-01-01

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study. PMID:29377896

Which tinnitus-related aspects are relevant for quality of life and depression: results from a large international multicentre sample

PubMed Central

2014-01-01

Background The aim of the present study was to investigate, which aspects of tinnitus are most relevant for impairment of quality of life. For this purpose we analysed how responses to the Tinnitus Handicap Inventory (THI) and to the question “How much of a problem is your tinnitus at present” correlate with the different aspects of quality of life and depression. Methods 1274 patients of the Tinnitus Research Initiative database were eligible for analysis. The Tinnitus Research Initiative database is composed of eight study centres from five countries. We assessed to which extent the Tinnitus Handicap Inventory (THI) and its subscales and single items as well as the tinnitus severity correlate with Beck Depression Inventory (BDI) score and different domains of the short version of the WHO-Quality of Life questionnaire (WHO-QoL Bref) by means of simple and multiple linear regression models. Results The THI explained considerable portions of the variance of the WHO-QoL Physical Health (R2 = 0.39) and Psychological Health (R2 = 0.40) and the BDI (R2 = 0.46). Furthermore, multiple linear regression models which included each THI item separately explained an additional 5% of the variance compared to the THI total score. The items feeling confused from tinnitus, the trouble of falling asleep at night, the interference with job or household responsibilities, getting upset from tinnitus, and the feeling of being depressed were those with the highest influence on quality of life and depression. The single question with regard to tinnitus severity explained 18%, 16%, and 20% of the variance of Physical Health, Psychological Health, and BDI respectively. Conclusions In the context of a cross-sectional correlation analysis, our findings confirmed the strong and consistent relationships between self-reported tinnitus burden and both quality of life, and depression. The single question “How much of a problem is your tinnitus” reflects tinnitus-related impairment in quality of life and can thus be recommended for use in clinical routine. PMID:24422941

Which tinnitus-related aspects are relevant for quality of life and depression: results from a large international multicentre sample.

PubMed

Zeman, Florian; Koller, Michael; Langguth, Berthold; Landgrebe, Michael

2014-01-14

The aim of the present study was to investigate, which aspects of tinnitus are most relevant for impairment of quality of life. For this purpose we analysed how responses to the Tinnitus Handicap Inventory (THI) and to the question "How much of a problem is your tinnitus at present" correlate with the different aspects of quality of life and depression. 1274 patients of the Tinnitus Research Initiative database were eligible for analysis. The Tinnitus Research Initiative database is composed of eight study centres from five countries. We assessed to which extent the Tinnitus Handicap Inventory (THI) and its subscales and single items as well as the tinnitus severity correlate with Beck Depression Inventory (BDI) score and different domains of the short version of the WHO-Quality of Life questionnaire (WHO-QoL Bref) by means of simple and multiple linear regression models. The THI explained considerable portions of the variance of the WHO-QoL Physical Health (R2 = 0.39) and Psychological Health (R2 = 0.40) and the BDI (R2 = 0.46). Furthermore, multiple linear regression models which included each THI item separately explained an additional 5% of the variance compared to the THI total score. The items feeling confused from tinnitus, the trouble of falling asleep at night, the interference with job or household responsibilities, getting upset from tinnitus, and the feeling of being depressed were those with the highest influence on quality of life and depression. The single question with regard to tinnitus severity explained 18%, 16%, and 20% of the variance of Physical Health, Psychological Health, and BDI respectively. In the context of a cross-sectional correlation analysis, our findings confirmed the strong and consistent relationships between self-reported tinnitus burden and both quality of life, and depression. The single question "How much of a problem is your tinnitus" reflects tinnitus-related impairment in quality of life and can thus be recommended for use in clinical routine.

Ridge: a computer program for calculating ridge regression estimates

Treesearch

Donald E. Hilt; Donald W. Seegrist

1977-01-01

Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.

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

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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

ERIC Educational Resources Information Center

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

2014-01-01

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

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

ERIC Educational Resources Information Center

Hafner, Lawrence E.

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

Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

ERIC Educational Resources Information Center

Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

2015-01-01

Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

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

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

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

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

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

A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

ERIC Educational Resources Information Center

Martin, David

2008-01-01

This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

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

ERIC Educational Resources Information Center

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

2006-01-01

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

Regression Models for the Analysis of Longitudinal Gaussian Data from Multiple Sources

PubMed Central

O’Brien, Liam M.; Fitzmaurice, Garrett M.

2006-01-01

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. PMID:15726666

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

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

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)

Socioeconomic determinants of childhood overweight and obesity in China: the long arm of institutional power

PubMed Central

Fu, Qiang; George, Linda K.

2018-01-01

Abstract Previous studies have widely reported that the association between socioeconomic status (SES) and childhood overweight and obesity in China is significant and positive, which lends little support to the fundamental-cause perspective. Using multiple waves (1997, 2000, 2004 and 2006) of the China Health and Nutrition Survey (CHNS) (N = 2,556, 2,063, 1,431 and 1,242, respectively) and continuous BMI cut-points obtained from a polynomial method, (mixed-effect) logistic regression analyses show that parental state-sector employment, an important, yet overlooked, indicator of political power during the market transformation has changed from a risk factor for childhood overweight/obesity in 1997 to a protective factor for childhood overweight/obesity in 2006. Results from quantile regression analyses generate the same conclusions and demonstrate that the protective effect of parental state sector employment at high percentiles of BMI is robust under different estimation strategies. By bridging the fundamental causes perspective and theories of market transformation, this research not only documents the effect of political power on childhood overweight/obesity but also calls for the use of multifaceted, culturally-relevant stratification measures in testing the fundamental cause perspective across time and space. PMID:26178452

Is obesity associated with global warming?

PubMed

Squalli, J

2014-12-01

Obesity is a national epidemic that imposes direct medical and indirect economic costs on society. Recent scholarly inquiries contend that obesity also contributes to global warming. The paper investigates the relationship between greenhouse gas emissions and obesity. Cross-sectional state-level data for the year 2010. Multiple regression analysis using least squares with bootstrapped standard errors and quantile regression. States with higher rates of obesity are associated with higher CO2 and CH4 emissions (p < 0.05) and marginally associated with higher N2O emissions (p < 0.10), net of other factors. Reverting to the obesity rates of the year 2000 across the entire United States could decrease greenhouse gas emissions by about two percent, representing more than 136 million metric tons of CO2 equivalent. Future studies should establish clear causality between obesity and emissions by using longitudinal data while controlling for other relevant factors. They should also consider identifying means to net out the potential effects of carbon sinks, conversion of CH4 to energy, cross-state diversion, disposal, and transfer of municipal solid waste, and potentially lower energy consumption from increased sedentariness. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Factors associated with positive adjustment in siblings of children with severe emotional disturbance: the role of family resources and community life.

PubMed

Kilmer, Ryan P; Cook, James R; Munsell, Eylin Palamaro; Salvador, Samantha Kane

2010-10-01

This study builds on the scant research involving siblings of children with severe emotional disturbances (SED) and examines: associations between adversity experiences and adjustment among 5- to 10-year-old siblings, and relations among family resources, community life, and sibling adjustment. Caregivers from 100 families completed standardized indicators of sibling adjustment and scales reflecting multiple contextual variables. Results document negative associations between stress exposure and sibling adjustment. Regression models also indicate positive associations between the caregiver-child relationship and broader family resources on sibling behavioral and emotional strengths, even after accounting for adversity experiences; adversity exposure was the prime correlate in regression models involving sibling oppositional behavior. Analyses also suggest that strain related to parenting a child with SED is associated with sibling adjustment. This work documents the needs of these siblings and their family systems and highlights the relevance of not only core proximal influences (e.g., child-caregiver relationship) but also elements of their broader contexts. Implications and recommendations are described, including the need to support plans of care that involve services, supports, or preventive strategies for these siblings. © 2010 American Orthopsychiatric Association.

Adaptation can explain evidence for encoding of probabilistic information in macaque inferior temporal cortex.

PubMed

Vinken, Kasper; Vogels, Rufin

2017-11-20

In predictive coding theory, the brain is conceptualized as a prediction machine that constantly constructs and updates expectations of the sensory environment [1]. In the context of this theory, Bell et al.[2] recently studied the effect of the probability of task-relevant stimuli on the activity of macaque inferior temporal (IT) neurons and observed a reduced population response to expected faces in face-selective neurons. They concluded that "IT neurons encode long-term, latent probabilistic information about stimulus occurrence", supporting predictive coding. They manipulated expectation by the frequency of face versus fruit stimuli in blocks of trials. With such a design, stimulus repetition is confounded with expectation. As previous studies showed that IT neurons decrease their response with repetition [3], such adaptation (or repetition suppression), instead of expectation suppression as assumed by the authors, could explain their effects. The authors attempted to control for this alternative interpretation with a multiple regression approach. Here we show by using simulation that adaptation can still masquerade as expectation effects reported in [2]. Further, the results from the regression model used for most analyses cannot be trusted, because the model is not uniquely defined. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mangrove vegetation structure in Southeast Brazil from phased array L-band synthetic aperture radar data

NASA Astrophysics Data System (ADS)

de Souza Pereira, Francisca Rocha; Kampel, Milton; Cunha-Lignon, Marilia

2016-07-01

The potential use of phased array type L-band synthetic aperture radar (PALSAR) data for discriminating distinct physiographic mangrove types with different forest structure developments in a subtropical mangrove forest located in Cananéia on the Southern coast of São Paulo, Brazil, is investigated. The basin and fringe physiographic types and the structural development of mangrove vegetation were identified with the application of the Kruskal-Wallis statistical test to the SAR backscatter values of 10 incoherent attributes. The best results to separate basin to fringe types were obtained using copolarized HH, cross-polarized HV, and the biomass index (BMI). Mangrove structural parameters were also estimated using multiple linear regressions. BMI and canopy structure index were used as explanatory variables for canopy height, mean height, and mean diameter at breast height regression models, with significant R2=0.69, 0.73, and 0.67, respectively. The current study indicates that SAR L-band images can be used as a tool to discriminate physiographic types and to characterize mangrove forests. The results are relevant considering the crescent availability of freely distributed SAR images that can be more utilized for analysis, monitoring, and conservation of the mangrove ecosystem.

Associations between overweight, peer problems, and mental health in 12-13-year-old Norwegian children.

PubMed

Hestetun, Ingebjørg; Svendsen, Martin Veel; Oellingrath, Inger Margaret

2015-03-01

Overweight and mental health problems represent two major challenges related to child and adolescent health. More knowledge of a possible relationship between the two problems and the influence of peer problems on the mental health of overweight children is needed. It has previously been hypothesized that peer problems may be an underlying factor in the association between overweight and mental health problems. The purpose of the present study was to investigate the associations between overweight, peer problems, and indications of mental health problems in a sample of 12-13-year-old Norwegian schoolchildren. Children aged 12-13 years were recruited from the seventh grade of primary schools in Telemark County, Norway. Parents gave information about mental health and peer problems by completing the extended version of the Strength and Difficulties Questionnaire (SDQ). Height and weight were objectively measured. Complete data were obtained for 744 children. Fisher's exact probability test and multiple logistic regressions were used. Most children had normal good mental health. Multiple logistic regression analysis showed that overweight children were more likely to have indications of psychiatric disorders (adjusted OR: 1.8, CI: 1.0-3.2) and peer problems (adjusted OR: 2.6, CI: 1.6-4.2) than normal-weight children, when adjusted for relevant background variables. When adjusted for peer problems, the association between overweight and indications of any psychiatric disorder was no longer significant. The results support the hypothesis that peer problems may be an important underlying factor for mental health problems in overweight children.

A gene expression inflammatory signature specifically predicts multiple myeloma evolution and patients survival.

PubMed

Botta, C; Di Martino, M T; Ciliberto, D; Cucè, M; Correale, P; Rossi, M; Tagliaferri, P; Tassone, P

2016-12-16

Multiple myeloma (MM) is closely dependent on cross-talk between malignant plasma cells and cellular components of the inflammatory/immunosuppressive bone marrow milieu, which promotes disease progression, drug resistance, neo-angiogenesis, bone destruction and immune-impairment. We investigated the relevance of inflammatory genes in predicting disease evolution and patient survival. A bioinformatics study by Ingenuity Pathway Analysis on gene expression profiling dataset of monoclonal gammopathy of undetermined significance, smoldering and symptomatic-MM, identified inflammatory and cytokine/chemokine pathways as the most progressively affected during disease evolution. We then selected 20 candidate genes involved in B-cell inflammation and we investigated their role in predicting clinical outcome, through univariate and multivariate analyses (log-rank test, logistic regression and Cox-regression model). We defined an 8-genes signature (IL8, IL10, IL17A, CCL3, CCL5, VEGFA, EBI3 and NOS2) identifying each condition (MGUS/smoldering/symptomatic-MM) with 84% accuracy. Moreover, six genes (IFNG, IL2, LTA, CCL2, VEGFA, CCL3) were found independently correlated with patients' survival. Patients whose MM cells expressed high levels of Th1 cytokines (IFNG/LTA/IL2/CCL2) and low levels of CCL3 and VEGFA, experienced the longest survival. On these six genes, we built a prognostic risk score that was validated in three additional independent datasets. In this study, we provide proof-of-concept that inflammation has a critical role in MM patient progression and survival. The inflammatory-gene prognostic signature validated in different datasets clearly indicates novel opportunities for personalized anti-MM treatment.

Antidopaminergic Medication is Associated with More Rapidly Progressive Huntington's Disease.

PubMed

Tedroff, Joakim; Waters, Susanna; Barker, Roger A; Roos, Raymund; Squitieri, Ferdinando

2015-01-01

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder leading to progressive motor, cognitive and functional decline. Antidopaminergic medications (ADMs) are frequently used to treat chorea and behavioural disturbances in HD. We aimed to assess how the use of such medications was associated with the severity and progression of the motor aspects of the condition, given that there have been concerns that such drugs may actually promote neurological deterioration. Using multiple linear regression, supplemented by principal component analysis to explore the overall correlation patterns and help identify relevant covariates, we assessed severity and progression of motor symptoms and functional decline in 651 manifest patients from the REGISTRY cohort followed for two years. ADM treated versus non-treated subjects were compared with respect to motor impairment at baseline and progression rate by means of multiple regression, adjusting for CAG-repeat and age. Patients treated with ADMs had significantly worse motor scores with greater functional disability at their first visit. They also showed a higher annual rate of progression of motor signs and disability over the next two years. In particular the rate of progression for oculomotor symptoms and bradykinesia was markedly increased whereas the rate of progression of chorea and dystonia was similar for ADM and drug naïve patients. These differences in clinical severity and progression could not be explained by differences in disease burden, duration of disease or other possible prognostic factors. The results from this analysis suggest ADM treatment is associated with more advanced and rapidly progressing HD although whether these drugs are causative in driving this progression requires further, prospective studies.

Health related quality of life in parents of six to eight year old children with Down syndrome.

PubMed

Marchal, Jan Pieter; Maurice-Stam, Heleen; Hatzmann, Janneke; van Trotsenburg, A S Paul; Grootenhuis, Martha A

2013-11-01

Raising a child with Down syndrome (DS) has been found to be associated with lowered health related quality of life (HRQoL) in the domains cognitive functioning, social functioning, daily activities and vitality. We aimed to explore which socio-demographics, child functioning and psychosocial variables were related to these HRQoL domains in parents of children with DS. Parents of 98 children with DS completed the TNO-AZL adult quality of life questionnaire (TAAQOL) and a questionnaire assessing socio-demographic, child functioning and psychosocial predictors. Using multiple linear regression analyses for each category of predictors, we selected relevant predictors for the final models. The final multiple linear regression models revealed that cognitive functioning was best predicted by the sleep of the child (β=.29, p<.01) and by the parent having given up a hobby (β=-.29, p<.01), social functioning by the quality of the partner relation (β=.34, p<.001), daily activities by the parent having to care for an ill friend or family member (β=-.31, p<.01), and vitality by the parent having enough personal time (β=.32, p<.01). Overall, psychosocial variables rather than socio-demographics or child functioning showed most consistent and powerful relations to the HRQoL domains of cognitive functioning, social functioning, daily activities and vitality. These psychosocial variables mainly related to social support and time pressure. Systematic screening of parents to detect problems timely, and interventions targeting the supportive network and the demands in time are recommended. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prediction of human core body temperature using non-invasive measurement methods.

PubMed

Niedermann, Reto; Wyss, Eva; Annaheim, Simon; Psikuta, Agnes; Davey, Sarah; Rossi, René Michel

2014-01-01

The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.

Modeling the impact of COPD on the brain.

PubMed

Borson, Soo; Scanlan, James; Friedman, Seth; Zuhr, Elizabeth; Fields, Julie; Aylward, Elizabeth; Mahurin, Rodney; Richards, Todd; Anzai, Yoshimi; Yukawa, Michi; Yeh, Shingshing

2008-01-01

Previous studies have shown that COPD adversely affects distant organs and body systems, including the brain. This pilot study aims to model the relationships between respiratory insufficiency and domains related to brain function, including low mood, subtly impaired cognition, systemic inflammation, and brain structural and neurochemical abnormalities. Nine healthy controls were compared with 18 age- and education-matched medically stable-COPD patients, half of whom were oxygen-dependent. Measures included depression, anxiety, cognition, health status, spirometry, oximetry at rest and during 6-minute walk, and resting plasma cytokines and soluble receptors, brain MRI, and MR spectroscopy in regions relevant to mood and cognition. ANOVA was used to compare controls with patients and with COPD subgroups (oxygen users [n = 9] and nonusers [n = 9]), and only variables showing group differences at p < or = 0.05 were included in multiple regressions controlling for age, gender, and education to develop the final model. Controls and COPD patients differed significantly in global cognition and memory, mood, and soluble TNFR1 levels but not brain structural or neurochemical measures. Multiple regressions identified pathways linking disease severity with impaired performance on sensitive cognitive processing measures, mediated through oxygen dependence, and with systemic inflammation (TNFR1), related through poor 6-minute walk performance. Oxygen desaturation with activity was related to indicators of brain tissue damage (increased frontal choline, which in turn was associated with subcortical white matter attenuation). This empirically derived model provides a conceptual framework for future studies of clinical interventions to protect the brain in patients with COPD, such as earlier oxygen supplementation for patients with desaturation during everyday activities.

Modeling the impact of COPD on the brain

PubMed Central

Borson, Soo; Scanlan, James; Friedman, Seth; Zuhr, Elizabeth; Fields, Julie; Aylward, Elizabeth; Mahurin, Rodney; Richards, Todd; Anzai, Yoshimi; Yukawa, Michi; Yeh, Shingshing

2008-01-01

Previous studies have shown that COPD adversely affects distant organs and body systems, including the brain. This pilot study aims to model the relationships between respiratory insufficiency and domains related to brain function, including low mood, subtly impaired cognition, systemic inflammation, and brain structural and neurochemical abnormalities. Nine healthy controls were compared with 18 age- and education-matched medically stable COPD patients, half of whom were oxygen-dependent. Measures included depression, anxiety, cognition, health status, spirometry, oximetry at rest and during 6-minute walk, and resting plasma cytokines and soluble receptors, brain MRI, and MR spectroscopy in regions relevant to mood and cognition. ANOVA was used to compare controls with patients and with COPD subgroups (oxygen users [n = 9] and nonusers [n = 9]), and only variables showing group differences at p ≤ 0.05 were included in multiple regressions controlling for age, gender, and education to develop the final model. Controls and COPD patients differed significantly in global cognition and memory, mood, and soluble TNFR1 levels but not brain structural or neurochemical measures. Multiple regressions identified pathways linking disease severity with impaired performance on sensitive cognitive processing measures, mediated through oxygen dependence, and with systemic inflammation (TNFR1), related through poor 6-minute walk performance. Oxygen desaturation with activity was related to indicators of brain tissue damage (increased frontal choline, which in turn was associated with subcortical white matter attenuation). This empirically derived model provides a conceptual framework for future studies of clinical interventions to protect the brain in patients with COPD, such as earlier oxygen supplementation for patients with desaturation during everyday activities. PMID:18990971

Use and Appreciation of a Tailored Self-Management eHealth Intervention for Early Cancer Survivors: Process Evaluation of a Randomized Controlled Trial.

PubMed

Kanera, Iris Maria; Willems, Roy A; Bolman, Catherine A W; Mesters, Ilse; Zambon, Victor; Gijsen, Brigitte Cm; Lechner, Lilian

2016-08-23

A fully automated computer-tailored Web-based self-management intervention, Kanker Nazorg Wijzer (KNW [Cancer Aftercare Guide]), was developed to support early cancer survivors to adequately cope with psychosocial complaints and to promote a healthy lifestyle. The KNW self-management training modules target the following topics: return to work, fatigue, anxiety and depression, relationships, physical activity, diet, and smoking cessation. Participants were guided to relevant modules by personalized module referral advice that was based on participants’ current complaints and identified needs. The aim of this study was to evaluate the adherence to the module referral advice, examine the KNW module use and its predictors, and describe the appreciation of the KNW and its predictors. Additionally, we explored predictors of personal relevance. This process evaluation was conducted as part of a randomized controlled trial. Early cancer survivors with various types of cancer were recruited from 21 Dutch hospitals. Data from online self-report questionnaires and logging data were analyzed from participants allocated to the intervention condition. Chi-square tests were applied to assess the adherence to the module referral advice, negative binominal regression analysis was used to identify predictors of module use, multiple linear regression analysis was applied to identify predictors of the appreciation, and ordered logistic regression analysis was conducted to explore possible predictors of perceived personal relevance. From the respondents (N=231; mean age 55.6, SD 11.5; 79.2% female [183/231]), 98.3% (227/231) were referred to one or more KNW modules (mean 2.9, SD 1.5), and 85.7% (198/231) of participants visited at least one module (mean 2.1, SD 1.6). Significant positive associations were found between the referral to specific modules (range 1-7) and the use of corresponding modules. The likelihoods of visiting modules were higher when respondents were referred to those modules by the module referral advice. Predictors of visiting a higher number of modules were a higher number of referrals by the module referral advice (β=.136, P=.009), and having a partner was significantly related with a lower number of modules used (β=-.256, P=.044). Overall appreciation was high (mean 7.5, SD 1.2; scale 1-10) and was significantly predicted by a higher perceived personal relevance (β=.623, P=.000). None of the demographic and cancer-related characteristics significantly predicted the perceived personal relevance. The KNW in general and more specifically the KNW modules were well used and highly appreciated by early cancer survivors. Indications were found that the module referral advice might be a meaningful intervention component to guide the users in following a preferred selection of modules. These results indicate that the fully automated Web-based KNW provides personal relevant and valuable information and support for early cancer survivors. Therefore, this intervention can complement usual cancer aftercare and may serve as a first step in a stepped-care approach. Nederlands Trial Register: NTR3375; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3375 (Archived by WebCite at http://www.webcitation.org/6jo4jO7kb).

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical-genomic driver associations.

PubMed

Lee, HoJoon; Palm, Jennifer; Grimes, Susan M; Ji, Hanlee P

2015-10-27

The Cancer Genome Atlas (TCGA) project has generated genomic data sets covering over 20 malignancies. These data provide valuable insights into the underlying genetic and genomic basis of cancer. However, exploring the relationship among TCGA genomic results and clinical phenotype remains a challenge, particularly for individuals lacking formal bioinformatics training. Overcoming this hurdle is an important step toward the wider clinical translation of cancer genomic/proteomic data and implementation of precision cancer medicine. Several websites such as the cBio portal or University of California Santa Cruz genome browser make TCGA data accessible but lack interactive features for querying clinically relevant phenotypic associations with cancer drivers. To enable exploration of the clinical-genomic driver associations from TCGA data, we developed the Cancer Genome Atlas Clinical Explorer. The Cancer Genome Atlas Clinical Explorer interface provides a straightforward platform to query TCGA data using one of the following methods: (1) searching for clinically relevant genes, micro RNAs, and proteins by name, cancer types, or clinical parameters; (2) searching for genomic/proteomic profile changes by clinical parameters in a cancer type; or (3) testing two-hit hypotheses. SQL queries run in the background and results are displayed on our portal in an easy-to-navigate interface according to user's input. To derive these associations, we relied on elastic-net estimates of optimal multiple linear regularized regression and clinical parameters in the space of multiple genomic/proteomic features provided by TCGA data. Moreover, we identified and ranked gene/micro RNA/protein predictors of each clinical parameter for each cancer. The robustness of the results was estimated by bootstrapping. Overall, we identify associations of potential clinical relevance among genes/micro RNAs/proteins using our statistical analysis from 25 cancer types and 18 clinical parameters that include clinical stage or smoking history. The Cancer Genome Atlas Clinical Explorer enables the cancer research community and others to explore clinically relevant associations inferred from TCGA data. With its accessible web and mobile interface, users can examine queries and test hypothesis regarding genomic/proteomic alterations across a broad spectrum of malignancies.

Quantifying social development in autism.

PubMed

Volkmar, F R; Carter, A; Sparrow, S S; Cicchetti, D V

1993-05-01

This study was concerned with the development of quantitative measures of social development in autism. Multiple regression equations predicting social, communicative, and daily living skills on the Vineland Adaptive Behavior Scales were derived from a large, normative sample and applied to groups of autistic and nonautistic, developmentally disordered children. Predictive models included either mental or chronological age and other relevant variables. Social skills in the autistic group were more than two standard deviations below those predicted by their mental age; an index derived from the ratio of actual to predicted social skills correctly classified 94% of the autistic and 92% of the nonautistic, developmentally disordered cases. The findings are consistent with the idea that social disturbance is central in the definition of autism. The approach used in this study has potential advantages for providing more precise measures of social development in autism.

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

ERIC Educational Resources Information Center

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

2017-01-01

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

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

PubMed

McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

2017-02-01

Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

Inherited genetic variants associated with occurrence of multiple primary melanoma.

PubMed

Gibbs, David C; Orlow, Irene; Kanetsky, Peter A; Luo, Li; Kricker, Anne; Armstrong, Bruce K; Anton-Culver, Hoda; Gruber, Stephen B; Marrett, Loraine D; Gallagher, Richard P; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J; Cust, Anne E; Ollila, David W; Begg, Colin B; Berwick, Marianne; Thomas, Nancy E

2015-06-01

Recent studies, including genome-wide association studies, have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 SNPs from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% confidence intervals were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma, while NCOA6 rs4911442 approached significance (P = 0.06). The GEM Study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. ©2015 American Association for Cancer Research.

Inherited genetic variants associated with occurrence of multiple primary melanoma

PubMed Central

Gibbs, David C.; Orlow, Irene; Kanetsky, Peter A.; Luo, Li; Kricker, Anne; Armstrong, Bruce K.; Anton-Culver, Hoda; Gruber, Stephen B.; Marrett, Loraine D.; Gallagher, Richard P.; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J.; Cust, Anne E.; Ollila, David W.; Begg, Colin B.; Berwick, Marianne; Thomas, Nancy E.

2015-01-01

Recent studies including genome-wide association studies have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 single nucleotide polymorphisms (SNP) from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% CIs were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma while NCOA6 rs4911442 approached significance (P = 0.06). The GEM study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. PMID:25837821

Alcohol dependence and physical comorbidity: Increased prevalence but reduced relevance of individual comorbidities for hospital-based mortality during a 12.5-year observation period in general hospital admissions in urban North-West England.

PubMed

Schoepf, D; Heun, R

2015-06-01

Alcohol dependence (AD) is associated with an increase in physical comorbidities. The effects of these diseases on general hospital-based mortality are unclear. Consequently, we conducted a mortality study in which we investigated if the burden of physical comorbidities and their relevance on general hospital-based mortality differs between individuals with and without AD during a 12.5-year observation period in general hospital admissions. During 1 January 2000 and 30 June 2012, 23,371 individuals with AD were admitted at least once to seven General Manchester Hospitals. Their physical comorbidities with a prevalence≥1% were compared to those of 233,710 randomly selected hospital controls, group-matched for age and gender (regardless of primary admission diagnosis or specialized treatments). Physical comorbidities that increased the risk of hospital-based mortality (but not outside of the hospital) during the observation period were identified using multiple logistic regression analyses. Hospital-based mortality rates were 20.4% in the AD sample and 8.3% in the control sample. Individuals with AD compared to controls had a higher burden of physical comorbidities, i.e. alcoholic liver and pancreatic diseases, diseases of the conducting airways, neurological and circulatory diseases, diseases of the upper gastrointestinal tract, renal diseases, cellulitis, iron deficiency anemia, fracture neck of femur, and peripheral vascular disease. In contrast, coronary heart related diseases, risk factors of cardiovascular disease, diverticular disease and cataracts were less frequent in individuals with AD than in controls. Thirty-two individual physical comorbidities contributed to the prediction of hospital-based mortality in univariate analyses in the AD sample; alcoholic liver disease (33.7%), hypertension (16.9%), chronic obstructive pulmonary disease (14.1%), and pneumonia (13.3%) were the most frequent diagnoses in deceased individuals with AD. Multiple forward logistic regression analysis, accounting for possible associations of diseases, identified twenty-three physical comorbidities contributing to hospital-based mortality in individuals with AD. However, all these comorbidities had an equal or even lower impact on hospital-based mortality than in the comparison sample. The excess of in-hospital deaths in general hospitals in individuals with AD is due to an increase of multiple physical comorbidities, even though individual diseases have an equal or even reduced impact on general hospital-based mortality in individuals with AD compared to controls. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

[Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

PubMed

Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

2011-12-01

To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

Price Analysis of Railway Freight Transport under Marketing Mechanism

NASA Astrophysics Data System (ADS)

Shi, Ying; Fang, Xiaoping; Chen, Zhiya

Regarding the problems in the reform of the railway tariff system and the pricing of the transport, by means of assaying the influence of the price elasticity on the artifice used for price, this article proposed multiple regressive model which analyzed price elasticity quantitatively. This model conclude multi-factors which influences on the price elasticity, such as the averagely railway freight charge, the averagely freight haulage of proximate supersede transportation mode, the GDP per capita in the point of origin, and a series of dummy variable which can reflect the features of some productive and consume demesne. It can calculate the price elasticity of different classes in different domains, and predict the freight traffic volume on different rate levels. It can calculate confidence-level, and evaluate the relevance of each parameter to get rid of irrelevant or little relevant variables. It supplied a good theoretical basis for directing the pricing of transport enterprises in market economic conditions, which is suitable for railway freight, passenger traffic and other transportation manner as well. SPSS (Statistical Package for the Social Science) software was used to calculate and analysis the example. This article realized the calculation by HYFX system(Ministry of Railways fund).

Determinants of feedback retention in soccer players.

PubMed

Januário, Nuno; Rosado, António; Mesquita, Isabel; Gallego, José; Aguilar-Parra, José M

2016-06-01

This study analyzed soccer players' retention of coaches' feedback during training sessions. We intended to determine if the retention of information was influenced by the athletes' personal characteristic (age, gender and the sports level), the quantity of information included in coach's feedback (the number of ideas and redundancy), athletes' perception of the relevance of the feedback information and athletes' motivation as well as the attention level. The study that was conducted over the course of 18 sessions of soccer practice, involved 12 coaches (8 males, 4 females) and 342 athletes (246 males, 96 females), aged between 10 and 18 years old. All coach and athlete interventions were transposed to a written protocol and submitted to content analysis. Descriptive statistics and multiple linear regression were calculated. The results showed that a substantial part of the information was not retained by the athletes; in 65.5% of cases, athletes experienced difficulty in completely reproducing the ideas of the coaches and, on average, the value of feedback retention was 57.0%. Six variables with a statistically significant value were found: gender, the athletes' sports level, redundancy, the number of transmitted ideas, athletes' perception of the relevance of the feedback information and the athletes' motivation level.

[Hospital governance and the structure of German hospital supervisory boards].

PubMed

Kuntz, L; Pulm, J; Wittland, M

2014-06-01

When thinking about corporate governance frequently the supervisory board comes to mind. The aim of this study is to investigate the relationship between the participation of single professions in the supervisory board and hospital financial performance. Based on governance codes, relevant professions that should be part of the supervisory board are identified. With the help of a multiple regression, the relationship between the fractions of these professions in the supervisory board and the return on assets in the year 2009 is examined. The sample consists of 182 hospitals. The study shows that participation of physicians in the supervisory board is related to a higher return on assets. Furthermore, the association between the fractions of nurses and politicians and hospitals financial performance is ­negative. The composition of the supervisory board has a significant effect on hospital performance; it is an important issue for hospital owners. The present study identifies only one positive relationship between the involvement of physicians and financial performance. Other professions could be relevant in achieving other objectives. Further studies are necessary to analyse the effects on other dimensions of hospital performance, e. g., on quality. © Georg Thieme Verlag KG Stuttgart · New York.

Developing effective feedback on quality of anaesthetic care: what are its most valuable characteristics from a clinical perspective?

PubMed

D'Lima, Danielle M; Moore, Joanna; Bottle, Alex; Brett, Stephen J; Arnold, Glenn M; Benn, Jonathan

2015-01-01

Research suggests that better feedback from quality and safety indicators leads to enhanced capability of clinicians and departments to improve care and change behaviour. The aim of the current study was to investigate the characteristics of feedback perceived by clinicians to be of most value. Data were collected using a survey designed as part of a wider evaluation of a data feedback initiative in anaesthesia. Eighty-nine consultant anaesthetists from two English NHS acute Trusts completed the survey. Multiple linear regression with hierarchical variable entry was used to investigate which characteristics of feedback predict its perceived usefulness for monitoring variation and improving care. The final model demonstrated that the relevance of the quality indicators to the specific service area (β=0.64, p=0.01) and the credibility of the data as coming from a trustworthy, unbiased source (β=0.55, p=0.01) were the significant predictors, having controlled for all other covariates. For clinicians to engage with effective quality monitoring and feedback, the perceived local relevance of indicators and trust in the credibility of the resulting data are paramount. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Emotion and attention interactions in social cognition: brain regions involved in processing anger prosody.

PubMed

Sander, David; Grandjean, Didier; Pourtois, Gilles; Schwartz, Sophie; Seghier, Mohamed L; Scherer, Klaus R; Vuilleumier, Patrik

2005-12-01

Multiple levels of processing are thought to be involved in the appraisal of emotionally relevant events, with some processes being engaged relatively independently of attention, whereas other processes may depend on attention and current task goals or context. We conducted an event-related fMRI experiment to examine how processing angry voice prosody, an affectively and socially salient signal, is modulated by voluntary attention. To manipulate attention orthogonally to emotional prosody, we used a dichotic listening paradigm in which meaningless utterances, pronounced with either angry or neutral prosody, were presented simultaneously to both ears on each trial. In two successive blocks, participants selectively attended to either the left or right ear and performed a gender-decision on the voice heard on the target side. Our results revealed a functional dissociation between different brain areas. Whereas the right amygdala and bilateral superior temporal sulcus responded to anger prosody irrespective of whether it was heard from a to-be-attended or to-be-ignored voice, the orbitofrontal cortex and the cuneus in medial occipital cortex showed greater activation to the same emotional stimuli when the angry voice was to-be-attended rather than to-be-ignored. Furthermore, regression analyses revealed a strong correlation between orbitofrontal regions and sensitivity on a behavioral inhibition scale measuring proneness to anxiety reactions. Our results underscore the importance of emotion and attention interactions in social cognition by demonstrating that multiple levels of processing are involved in the appraisal of emotionally relevant cues in voices, and by showing a modulation of some emotional responses by both the current task-demands and individual differences.

Mortality and nursing care dependency one year after first ischemic stroke: an analysis of German statutory health insurance data.

PubMed

Kemper, Claudia; Koller, Daniela; Glaeske, Gerd; van den Bussche, Hendrik

2011-01-01

Aphasia, dementia, and depression are important and common neurological and neuropsychological disorders after ischemic stroke. We estimated the frequency of these comorbidities and their impact on mortality and nursing care dependency. Data of a German statutory health insurance were analyzed for people aged 50 years and older with first ischemic stroke. Aphasia, dementia, and depression were defined on the basis of outpatient medical diagnoses within 1 year after stroke. Logistic regression models for mortality and nursing care dependency were calculated and were adjusted for age, sex, and other relevant comorbidity. Of 977 individuals with a first ischemic stroke, 14.8% suffered from aphasia, 12.5% became demented, and 22.4% became depressed. The regression model for mortality showed a significant influence of age, aphasia, and other relevant comorbidity. In the regression model for nursing care dependency, the factors age, aphasia, dementia, depression, and other relevant comorbidity were significant. Aphasia has a high impact on mortality and nursing care dependency after ischemic stroke, while dementia and depression are strongly associated with increasing nursing care dependency.

Combining Relevance Vector Machines and exponential regression for bearing residual life estimation

NASA Astrophysics Data System (ADS)

Di Maio, Francesco; Tsui, Kwok Leung; Zio, Enrico

2012-08-01

In this paper we present a new procedure for estimating the bearing Residual Useful Life (RUL) by combining data-driven and model-based techniques. Respectively, we resort to (i) Relevance Vector Machines (RVMs) for selecting a low number of significant basis functions, called Relevant Vectors (RVs), and (ii) exponential regression to compute and continuously update residual life estimations. The combination of these techniques is developed with reference to partially degraded thrust ball bearings and tested on real world vibration-based degradation data. On the case study considered, the proposed procedure outperforms other model-based methods, with the added value of an adequate representation of the uncertainty associated to the estimates of the quantification of the credibility of the results by the Prognostic Horizon (PH) metric.

Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis

NASA Astrophysics Data System (ADS)

Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei

2016-10-01

Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

Forecasting USAF JP-8 Fuel Needs

DTIC Science & Technology

2009-03-01

versus complex ones. When we consider long -term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified...with more simple and easy-to-implement methods, versus complex ones. When we consider long -term 5-year forecasts, our multiple regression model...effort. The insight and experience was certainly appreciated. Special thanks to my Turkish peers for their continuous support and help during this long

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

USDA-ARS?s Scientific Manuscript database

A technique of using multiple calibration sets in partial least squares regression (PLS) was proposed to improve the quantitative determination of ammonia from open-path Fourier transform infrared spectra. The spectra were measured near animal farms, and the path-integrated concentration of ammonia...

Effect of lecture instruction on student performance on qualitative questions

NASA Astrophysics Data System (ADS)

Heron, Paula R. L.

2015-06-01

The impact of lecture instruction on student conceptual understanding in physics has been the subject of research for several decades. Most studies have reported disappointingly small improvements in student performance on conceptual questions despite direct instruction on the relevant topics. These results have spurred a number of attempts to improve learning in physics courses through new curricula and instructional techniques. This paper contributes to the research base through a retrospective analysis of 20 randomly selected qualitative questions on topics in kinematics, dynamics, electrostatics, waves, and physical optics that have been given in introductory calculus-based physics at the University of Washington over a period of 15 years. In some classes, questions were administered after relevant lecture instruction had been completed; in others, it had yet to begin. Simple statistical tests indicate that the average performance of the "after lecture" classes was significantly better than that of the "before lecture" classes for 11 questions, significantly worse for two questions, and indistinguishable for the remaining seven. However, the classes had not been randomly assigned to be tested before or after lecture instruction. Multiple linear regression was therefore conducted with variables (such as class size) that could plausibly lead to systematic differences in performance and thus obscure (or artificially enhance) the effect of lecture instruction. The regression models support the results of the simple tests for all but four questions. In those cases, the effect of lecture instruction was reduced to a nonsignificant level, or increased to a significant, negative level when other variables were considered. Thus the results provide robust evidence that instruction in lecture can increase student ability to give correct answers to conceptual questions but does not necessarily do so; in some cases it can even lead to a decrease.

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.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

PubMed

Cui, Zaixu; Gong, Gaolang

2018-06-02

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations. Copyright © 2018 Elsevier Inc. All rights reserved.

Paternal mental health and socioemotional and behavioral development in their children.

PubMed

Kvalevaag, Anne Lise; Ramchandani, Paul G; Hove, Oddbjørn; Assmus, Jörg; Eberhard-Gran, Malin; Biringer, Eva

2013-02-01

To examine the association between symptoms of psychological distress in expectant fathers and socioemotional and behavioral outcomes in their children at age 36 months. The current study is based on data from the Norwegian Mother and Child Cohort Study on 31 663 children. Information about fathers' mental health was obtained by self-report (Hopkins Symptom Checklist) in week 17 or 18 of gestation. Information about mothers' pre- and postnatal mental health and children's socioemotional and behavioral development at 36 months of age was obtained from parent-report questionnaires. Linear multiple regression and logistic regression models were performed while controlling for demographics, lifestyle variables, and mothers' mental health. Three percent of the fathers had high levels of psychological distress. Using linear regression models, we found a small positive association between fathers' psychological distress and children's behavioral difficulties, B = 0.19 (95% confidence interval [CI] = 0.15-0.23); emotional difficulties, B = 0.22 (95% CI = 0.18-0.26); and social functioning, B = 0.12 (95% CI = 0.07-0.16). The associations did not change when adjusted for relevant confounders. Children whose fathers had high levels of psychological distress had higher levels of emotional and behavioral problems. This study suggests that some risk of future child emotional, behavioral, and social problems can be identified during pregnancy. The findings are of importance for clinicians and policy makers in their planning of health care in the perinatal period because this represents a significant opportunity for preventive intervention.

A cross-sectional study to estimate associations between education level and osteoporosis in a Chinese postmenopausal women sample.

PubMed

Piao, Hui-Hong; He, Jiajia; Zhang, Keqin; Tang, Zihui

2015-01-01

Our research aims to investigate the associations between education level and osteoporosis (OP) in Chinese postmenopausal women. A large-scale, community-based, cross-sectional study was conducted to examine the associations between education level and OP. A self-reported questionnaire was used to access the demographical information and medical history of the participants. A total of 1905 postmenopausal women were available for data analysis in this study. Multiple regression models controlling for confounding factors to include education level were performed to investigate the relationship with OP. The prevalence of OP was 28.29% in our study sample. Multivariate linear regression analyses adjusted for relevant potential confounding factors detected significant associations between education level and T-score (β = 0.025, P-value = 0.095, 95% CI: -0.004-0.055 for model 1; and β = 0.092, P-value = 0.032, 95% CI: 0.008-0.175 for model 2). Multivariate logistic regression analyses detected significant associations between education level and OP in model 1 (P-value = 0.070 for model 1, Table 5), while no significant associations was reported in model 2 (P value = 0.131). In participants with high education levels, the OR for OP was 0.914 (95% CI: 0.830-1.007). The findings indicated that education level was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese postmenopausal women with low educational status.

Solar cycle in current reanalyses: (non)linear attribution study

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2014-12-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11 year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (Support Vector Regression, Neural Networks) besides the traditional linear approach. The analysis was applied to several current reanalysis datasets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how this type of data resolves especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the lower and upper stratosphere were found to be sufficiently robust and in qualitative agreement with previous observational studies. The analysis also pointed to the solar signal in the ozone datasets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. Consequently the results obtained by linear regression were confirmed by the nonlinear approach through all datasets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. Furthermore, the seasonal dependence of the solar response was also discussed, mainly as a source of dynamical causalities in the wave propagation characteristics in the zonal wind and the induced meridional circulation in the winter hemispheres. The hypothetical mechanism of a weaker Brewer Dobson circulation was reviewed together with discussion of polar vortex stability.

Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

PubMed

Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

2017-08-01

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

Socioeconomic conditions across life related to multiple measures of the endocrine system in older adults: Longitudinal findings from a British birth cohort study.

PubMed

Bann, David; Hardy, Rebecca; Cooper, Rachel; Lashen, Hany; Keevil, Brian; Wu, Frederick C W; Holly, Jeff M P; Ong, Ken K; Ben-Shlomo, Yoav; Kuh, Diana

2015-12-01

Little is known about how socioeconomic position (SEP) across life impacts on different axes of the endocrine system which are thought to underlie the ageing process and its adverse consequences. We examined how indicators of SEP across life related to multiple markers of the endocrine system in late midlife, and hypothesized that lower SEP across life would be associated with an adverse hormone profile across multiple axes. Data were from a British cohort study of 875 men and 905 women followed since their birth in March 1946 with circulating free testosterone and insulin-like growth factor-I (IGF-I) measured at both 53 and 60-64 years, and evening cortisol at 60-64 years. Indicators of SEP were ascertained prospectively across life-paternal occupational class at 4, highest educational attainment at 26, household occupational class at 53, and household income at 60-64 years. Associations between SEP and hormones were investigated using multiple regression and logistic regression models. Lower SEP was associated with lower free testosterone among men, higher free testosterone among women, and lower IGF-I and higher evening cortisol in both sexes. For example, the mean standardised difference in IGF-I comparing the lowest with the highest educational attainment at 26 years (slope index of inequality) was -0.4 in men (95% CI -0.7 to -0.2) and -0.4 in women (-0.6 to -0.2). Associations with each hormone differed by SEP indicator used and sex, and were particularly pronounced when using a composite adverse hormone score. For example, the odds of having 1 additional adverse hormone concentration in the lowest compared with highest education level were 3.7 (95% CI: 2.1, 6.3) among men, and 1.6 (1.0, 2.7) among women (P (sex interaction) = 0.02). We found no evidence that SEP was related to apparent age-related declines in free testosterone or IGF-I. Lower SEP was associated with an adverse hormone profile across multiple endocrine axes. SEP differences in endocrine function may partly underlie inequalities in health and function in later life, and may reflect variations in biological rates of ageing. Further studies are required to assess the likely functional relevance of these associations. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Weather Impact on Airport Arrival Meter Fix Throughput

NASA Technical Reports Server (NTRS)

Wang, Yao

2017-01-01

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

DTIC Science & Technology

2012-03-01

Impact of global warming on monsoon variability in Pakistan. J. Anim. Pl. Sci., 21, no. 1, 107–110. Gillies, S., T. Murphree, and D. Meyer, 2012...are generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The...generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The predictands are

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Reduction of shading-derived artifacts in skin chromophore imaging without measurements or assumptions about the shape of the subject

NASA Astrophysics Data System (ADS)

Yoshida, Kenichiro; Nishidate, Izumi; Ojima, Nobutoshi; Iwata, Kayoko

2014-01-01

To quantitatively evaluate skin chromophores over a wide region of curved skin surface, we propose an approach that suppresses the effect of the shading-derived error in the reflectance on the estimation of chromophore concentrations, without sacrificing the accuracy of that estimation. In our method, we use multiple regression analysis, assuming the absorbance spectrum as the response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as the predictor variables. The concentrations of melanin and total hemoglobin are determined from the multiple regression coefficients using compensation formulae (CF) based on the diffuse reflectance spectra derived from a Monte Carlo simulation. To suppress the shading-derived error, we investigated three different combinations of multiple regression coefficients for the CF. In vivo measurements with the forearm skin demonstrated that the proposed approach can reduce the estimation errors that are due to shading-derived errors in the reflectance. With the best combination of multiple regression coefficients, we estimated that the ratio of the error to the chromophore concentrations is about 10%. The proposed method does not require any measurements or assumptions about the shape of the subjects; this is an advantage over other studies related to the reduction of shading-derived errors.

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

ERIC Educational Resources Information Center

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

Sex-specific nonlinear associations between serum lipids and different domains of cognitive function in middle to older age individuals.

PubMed

Lu, Yanhui; An, Yu; Yu, Huanling; Che, Fengyuan; Zhang, Xiaona; Rong, Hongguo; Xi, Yuandi; Xiao, Rong

2017-08-01

To examine how serum lipids relates to specific cognitive ability domains between the men and women in Chinese middle to older age individuals. A complete lipid panel was obtained from 1444 individuals, ages 50-65, who also underwent a selection of cognitive tests. Participants were 584 men and 860 women from Linyi city, Shandong province. Multiple linear regression analyses examined serum lipids level as quadratic predictors of sex-specific measure of performance in different cognitive domains, which were adjusted for sociodemographic and lifestyle characteristics. In men, a significant quadratic effect of total cholesterol (TC) was identified for Digit Symbol (B = -0.081, P = 0.044) and also quadratic effect of low density lipoprotein-cholesterol (LDL-C) was identified for Trail Making Test B (B = -0.082, P = 0.045). Differently in women, there were significant quadratic associations between high density lipoprotein-cholesterol (HDL-C) and multiple neuropsychological tests. The nonlinear lipid-cognition associations differed between men and women and were specific to certain cognitive domains and might be of potential relevance for prevention and therapy of cognitive decline.

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

PubMed Central

Lim, Liang; Nichols, Brandon; Migden, Michael R.; Rajaram, Narasimhan; Reichenberg, Jason S.; Markey, Mia K.; Ross, Merrick I.; Tunnell, James W.

2014-01-01

Abstract. The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%/100%, for SCC and BCC versus AK (57 versus 14 lesions) was 95%/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device. PMID:25375350

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

NASA Astrophysics Data System (ADS)

Lim, Liang; Nichols, Brandon; Migden, Michael R.; Rajaram, Narasimhan; Reichenberg, Jason S.; Markey, Mia K.; Ross, Merrick I.; Tunnell, James W.

2014-11-01

The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%;/100%;, for SCC and BCC versus AK (57 versus 14 lesions) was 95%;/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device.

Factors associated with higher oxytocin requirements in labor.

PubMed

Frey, Heather A; Tuuli, Methodius G; England, Sarah K; Roehl, Kimberly A; Odibo, Anthony O; Macones, George A; Cahill, Alison G

2015-09-01

To identify clinical characteristics associated with high maximum oxytocin doses in women who achieve complete cervical dilation. A retrospective nested case-control study was performed within a cohort of all term women at a single center between 2004 and 2008 who reached the second stage of labor. Cases were defined as women who had a maximum oxytocin dose during labor >20 mu/min, while women in the control group had a maximum oxytocin dose during labor of ≤20 mu/min. Exclusion criteria included no oxytocin administration during labor, multiple gestations, major fetal anomalies, nonvertex presentation, and prior cesarean delivery. Multiple maternal, fetal, and labor factors were evaluated with univariable analysis and multivariable logistic regression. Maximum oxytocin doses >20 mu/min were administered to 108 women (3.6%), while 2864 women received doses ≤20 mu/min. Factors associated with higher maximum oxytocin dose after adjusting for relevant confounders included maternal diabetes, birthweight >4000 g, intrapartum fever, administration of magnesium, and induction of labor. Few women who achieve complete cervical dilation require high doses of oxytocin. We identified maternal, fetal and labor factors that characterize this group of parturients.

Deterioration of abstract reasoning ability in mild cognitive impairment and Alzheimer's disease: correlation with regional grey matter volume loss revealed by diffeomorphic anatomical registration through exponentiated lie algebra analysis.

PubMed

Yoshiura, Takashi; Hiwatashi, Akio; Yamashita, Koji; Ohyagi, Yasumasa; Monji, Akira; Takayama, Yukihisa; Kamano, Norihiro; Kawashima, Toshiro; Kira, Jun-Ichi; Honda, Hiroshi

2011-02-01

To determine which brain regions are relevant to deterioration in abstract reasoning as measured by Raven's Colored Progressive Matrices (CPM) in the context of dementia. MR images of 37 consecutive patients including 19 with Alzheimer's disease (AD) and 18 with amnestic mild cognitive impairment (aMCI) were retrospectively analyzed. All patients were administered the CPM. Regional grey matter (GM) volume was evaluated according to the regimens of voxel-based morphometry, during which a non-linear registration algorithm called Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra was employed. Multiple regression analyses were used to map the regions where GM volumes were correlated with CPM scores. The strongest correlation with CPM scores was seen in the left middle frontal gyrus while a region with the largest volume was identified in the left superior temporal gyrus. Significant correlations were seen in 14 additional regions in the bilateral cerebral hemispheres and right cerebellum. Deterioration of abstract reasoning ability in AD and aMCI measured by CPM is related to GM loss in multiple regions, which is in close agreement with the results of previous activation studies.

The stress-buffering effects of hope on adjustment to multiple sclerosis.

PubMed

Madan, Sindia; Pakenham, Kenneth I

2014-12-01

Hope is an important resource for coping with chronic illness; however, the role of hope in adjusting to multiple sclerosis (MS) has been neglected, and the mechanisms by which hope exerts beneficial impacts are not well understood. This study aims to examine the direct and stress-moderating effects of dispositional hope and its components (agency and pathways) on adjustment to MS. A total of 296 people with MS completed questionnaires at time 1 at 12 months later and time 2. Focal predictors were stress, hope, agency and pathways, and the adjustment outcomes were anxiety, depression, positive affect, positive states of mind and life satisfaction. Results of regression analyses showed that as predicted, greater hope was associated with better adjustment after controlling for the effects of time 1 adjustment and relevant demographics and illness variables. However, these direct effects of hope were subsumed by stress-buffering effects. Regarding the hope components, the beneficial impacts of agency emerged via a direct effects mechanism, whereas the effects of pathways were evidenced via a moderating mechanism. Findings highlight hope as an important protective coping resource for coping with MS and accentuate the roles of both agency and pathways thinking and their different modes of influence in this process.

Regularized quantile regression for SNP marker estimation of pig growth curves.

PubMed

Barroso, L M A; Nascimento, M; Nascimento, A C C; Silva, F F; Serão, N V L; Cruz, C D; Resende, M D V; Silva, F L; Azevedo, C F; Lopes, P S; Guimarães, S E F

2017-01-01

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Reported parental characteristics in relation to trait depression and anxiety levels in a non-clinical group.

PubMed

Parker, G

1979-09-01

Care and overprotection appear to reflect the principal dimensions underlying parental behaviours and attitudes. In previous studies of neurotically depressed patients and of a non-clinical group, subjects who scored their parents as lacking in care and/or overprotective had the greater depressive experience. The present study of another non-clinical group (289 psychology students) replicated those findings in regard to trait depression levels. In addition, associations between those parental dimensions and trait anxiety scores were demonstrated. Multiple regression analyses established that 9-10% of the variance in mood scores was accounted for by scores on those parental dimensions. Low maternal care scores predicted higher levels of both anxiety and depression, while high maternal overprotection scores predicted higher levels of anxiety but not levels of depression. Maternal influences were clearly of greater relevance than paternal influences.

A new biodegradation prediction model specific to petroleum hydrocarbons.

PubMed

Howard, Philip; Meylan, William; Aronson, Dallas; Stiteler, William; Tunkel, Jay; Comber, Michael; Parkerton, Thomas F

2005-08-01

A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.

Effects of value strains on psychopathology of Chinese rural youths.

PubMed

Zhang, Jie; Zhao, Sibo

2013-12-01

The Strain Theory of Suicide postulates that psychological strains usually precede mental disorders including suicidal behavior. This paper focuses on the effect of conflicting social value strains on the individual's psychopathology. We analyzed the data of 2031 respondents who were proxy informants for suicides and community living controls in a large scale psychological autopsy study in rural China, with the CES-D depression measure for the psychopathology. Individuals having experienced value conflicts between Confucian gender role and gender equalitarianism in modern society scored on depression significantly higher than the individuals who do not experience the value conflict, and it is also true when several other relevant variables were held constant in the multiple regression model. This study supports the hypotheses that people who confront value conflicts are likely to experience psychopathological strain, and the higher the level of strain, the stronger the depression. Copyright © 2013 Elsevier B.V. All rights reserved.

Companions and competitors: Joint metal-supply relationships in gold, silver, copper, lead and zinc mines

DOE PAGES

Jordan, Brett Watson

2017-06-03

Firms that extract and produce multiple metals are an important component of mineral supply. The reaction of such firms to changes in their relevant output prices is tested econometrically for five metals using a panel representing more than 100 mines across the time period 1991-2005. Here, the estimation strategy is drawn from joint production theory, namely a flexible form, dual revenue approach with seemingly unrelated regressions (SUR) estimation. The results indicate that multi-product mines respond (in the short run) to higher prices of a particular metal by reducing output of that metal (indicative of low-grading behavior) and increasing and/or decreasingmore » output of joint metal products (indicative of substitutes and complements in supply). As a result, the price responses are not readily explained by a metal's classification as a by-product or main product based on revenue.« less

Health risk behavior of rural secondary school students in Zimbabwe.

PubMed

Gwede, C K; McDermott, R J; Westhoff, W W; Mushore, M; Mushore, T; Chitsika, E; Majange, C S; Chauke, P

2001-10-01

A socioculturally appropriate health risk behavior instrument, modeled after the U.S. Centers for Disease Control and Prevention's Youth Risk Behavior Survey (YRBS), was administered to 717 secondary school students in a rural area of Zimbabwe. Comparisons of risk behaviors by gender and school grade were made using univariate procedures and multiple logistic regression. Males were significantly more likely than females to have had sexual intercourse (odds ratio = 5.02, p < .0001) and to report drug use behaviors. Males also were significantly more likely to report early initiation (by age 13 years) of alcohol use, cigarette smoking, and marijuana use. School site violence and drug use behaviors also were prevalent in this sample. An interaction between gender and grade was evident for some behaviors. Additional research may further the understanding of these risk behaviors and facilitate development of effective, culturally relevant risk reduction programs.

Lay beliefs about the efficacy of self-reliance, seeking help and external control as strategies for overcoming obesity, drug addiction, marital problems, stuttering and insomnia.

PubMed

Furnham, Adrian; McDermott, Mark R

1994-07-01

This study was concerned with peoples' beliefs about the importance of twenty-four different contributors towards overcoming five relatively common personal health problems, namely: obesity, drug addiction, marital difficulties, stuttering and insomnia. One hundred and twenty-two subjects completed a five-page questionnaire indicating how effective each of these contributors were to overcoming the problems as specified. Factor analysis revealed an interpretable structure similar to previous studies (Luk and Bond, 1992): the emerging three factors were labelled ' self-reliance", "seeking help" and "external control". Multiple regression showed that few individual difference variables as measured were related to perceived relevance of the different contributors. The results were discussed in terms of subjects' beliefs concerning the value of self-reliance as opposed to seeking help, and in relation to the importance of understanding lay beliefs about the efficacy of different forms of intervention.

Development of a pharmacogenetic-guided warfarin dosing algorithm for Puerto Rican patients.

PubMed

Ramos, Alga S; Seip, Richard L; Rivera-Miranda, Giselle; Felici-Giovanini, Marcos E; Garcia-Berdecia, Rafael; Alejandro-Cowan, Yirelia; Kocherla, Mohan; Cruz, Iadelisse; Feliu, Juan F; Cadilla, Carmen L; Renta, Jessica Y; Gorowski, Krystyna; Vergara, Cunegundo; Ruaño, Gualberto; Duconge, Jorge

2012-12-01

This study was aimed at developing a pharmacogenetic-driven warfarin-dosing algorithm in 163 admixed Puerto Rican patients on stable warfarin therapy. A multiple linear-regression analysis was performed using log-transformed effective warfarin dose as the dependent variable, and combining CYP2C9 and VKORC1 genotyping with other relevant nongenetic clinical and demographic factors as independent predictors. The model explained more than two-thirds of the observed variance in the warfarin dose among Puerto Ricans, and also produced significantly better 'ideal dose' estimates than two pharmacogenetic models and clinical algorithms published previously, with the greatest benefit seen in patients ultimately requiring <7 mg/day. We also assessed the clinical validity of the model using an independent validation cohort of 55 Puerto Rican patients from Hartford, CT, USA (R(2) = 51%). Our findings provide the basis for planning prospective pharmacogenetic studies to demonstrate the clinical utility of genotyping warfarin-treated Puerto Rican patients.

Companions and competitors: Joint metal-supply relationships in gold, silver, copper, lead and zinc mines

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

Jordan, Brett Watson

Firms that extract and produce multiple metals are an important component of mineral supply. The reaction of such firms to changes in their relevant output prices is tested econometrically for five metals using a panel representing more than 100 mines across the time period 1991-2005. Here, the estimation strategy is drawn from joint production theory, namely a flexible form, dual revenue approach with seemingly unrelated regressions (SUR) estimation. The results indicate that multi-product mines respond (in the short run) to higher prices of a particular metal by reducing output of that metal (indicative of low-grading behavior) and increasing and/or decreasingmore » output of joint metal products (indicative of substitutes and complements in supply). As a result, the price responses are not readily explained by a metal's classification as a by-product or main product based on revenue.« less

Effects of Value Strains on Psychopathology of Chinese Rural Youths

PubMed Central

Zhang, Jie; Zhao, Sibo

2013-01-01

The Strain Theory of Suicide postulates that psychological strains usually precede mental disorders including suicidal behavior. This paper focuses on the effect of conflicting social value strains on the individual’s psychopathology. We analyzed the data of 2,031 respondents who were proxy informants for suicides and community living controls in a large scale psychological autopsy study in rural China, with the CES-D depression measure for the psychopathology. Individuals having experienced value conflicts between Confucian gender role and gender equalitarianism in modern society scored on depression significantly higher than the individuals who do not experience the value conflict, and it is also true when several other relevant variables were held constant in the multiple regression model. This study supports the hypotheses that people who confront value conflicts is likely to lead to psychopathological strain, and the higher the level of strain, the stronger the depression. PMID:24309863

Comparison between the Health Belief Model and Subjective Expected Utility Theory: predicting incontinence prevention behaviour in post-partum women.

PubMed

Dolman, M; Chase, J

1996-08-01

A small-scale study was undertaken to test the relative predictive power of the Health Belief Model and Subjective Expected Utility Theory for the uptake of a behaviour (pelvic floor exercises) to reduce post-partum urinary incontinence in primigravida females. A structured questionnaire was used to gather data relevant to both models from a sample antenatal and postnatal primigravida women. Questions examined the perceived probability of becoming incontinent, the perceived (dis)utility of incontinence, the perceived probability of pelvic floor exercises preventing future urinary incontinence, the costs and benefits of performing pelvic floor exercises and sources of information and knowledge about incontinence. Multiple regression analysis focused on whether or not respondents intended to perform pelvic floor exercises and the factors influencing their decisions. Aggregated data were analysed to compare the Health Belief Model and Subjective Expected Utility Theory directly.

[Modifications of the superoxide anion level in breast milk by the intake of flavonoids and carotenoids].

PubMed

Marchesino, Mariana A; Cortez, Mariela V; Albrecht, Claudia; Aballay, Laura R; Soria, Elio A

2017-01-01

To associate the intake of flavonoids and carotenoids with the breast milk level of superoxide anion, as an oxidative stress marker. 100 women from Cordoba (Argentina), who breastfed within the first postpartum 6 months, were studied during the 2013-2015 period, by evaluating their sanitary data, food intake and anion level in milk with multiple logistic regression. The intake of flavonoids, provitamin A carotenoids and non-provitamin carotenoids was 72 (61) mg/d, 1813 (1 657) µg/d y 5427 (3 664) µg/d, respectively. The anion was associated with the intake of flavanols (OR=1.081; CI95 1.001-1.167) y flavanones (OR=1.025; CI95 1.001-1.048). This effect was not seen with other flavonoids and carotenoids. Intake of flavanols and flavanones increases milk oxidation risk, which is relevant to develop diet recommendations.

An Assessment of Science Teachers' Perceptions of Secondary School Environments in Taiwan

NASA Astrophysics Data System (ADS)

Huang, Shwu-Yong L.

2006-01-01

This study investigates the psychosocial environments of secondary schools from science teachers’ perspectives, as well as associated variables. Using a sample of 900 secondary science teachers from 52 schools in Taiwan, the results attest to the validity and reliability of the instrument, the Science Teacher School Environment Questionnaire, and its ability to differentiate among schools. The descriptive results show that a majority of science teachers positively perceived their school environments. The teachers reported high collegiality, good teacher student relations, effective principal leadership, strong professional interest, and low work pressure—but also low staff freedom. Multiple regression results further indicate that policy-relevant variables like school level, school location, and teachers’ intentions to stay in teaching were associated with science teachers’ perceptions of their school environments. Qualitative data analysis based on interviews of 34 science teachers confirmed and enriched these findings.

Dimensions of the epilepsy foundation concerns index.

PubMed

Loring, David W; Larrabee, Glenn J; Meador, Kimford J; Lee, Gregory P

2005-05-01

We performed principal component analysis (PCA) of the Epilepsy Foundation Concerns Index scale in 189 patients undergoing evaluation for epilepsy surgery. We identified a five-factor solution in which there were no varimax-rotated factors consisting of fewer than two questions. Factor 1 reflects affective impact on enjoyment of life, Factor 2 reflects general autonomy concerns, Factor 3 reflects fear of seizure recurrence, Factor 4 reflects concern of being a burden to one's family, and Factor 5 reflects a perceived lack of understanding by others. Multiple regression using the Quality of Life in Epilepsy--89 question version; Minnesota Multiphasic Personality Inventory--2; Wechsler Adult Intelligence Scale--third edition; and verbal and visual memory tests as predictors demonstrated a different pattern of association with the factor and summary scores. We conclude that the Epilepsy Foundation Concerns Index is multidimensional, and using a global score based on all items may mask specific concerns that may be relevant when applied to individual patients.

Quantitative structure-activity relationships of selective antagonists of glucagon receptor using QuaSAR descriptors.

PubMed

Manoj Kumar, Palanivelu; Karthikeyan, Chandrabose; Hari Narayana Moorthy, Narayana Subbiah; Trivedi, Piyush

2006-11-01

In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition.

Negotiating Multicollinearity with Spike-and-Slab Priors.

PubMed

Ročková, Veronika; George, Edward I

2014-08-01

In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout.

Forgiveness and Consideration of Future Consequences in Aggressive Driving

PubMed Central

Moore, Michael; Dahlen, Eric R.

2008-01-01

Most research on aggressive driving has focused on identifying aspects of driver personality which will exacerbate it (e.g., sensation seeking, impulsiveness, driving anger, etc.). The present study was designed to examine two theoretically relevant but previously unexplored personality factors predicted to reduce the risk of aggressive driving: trait forgiveness and consideration of future consequences. The utility of these variables in predicting aggressive driving and driving anger expression was evaluated among 316 college student volunteers. Hierarchical multiple regressions permitted an analysis of the incremental validity of these constructs beyond respondent gender, age, miles driven per week, and driving anger. Both forgiveness and consideration of future consequences contributed to the prediction of aggressive driving and driving anger expression, independent of driving anger. Research on aggressive driving may be enhanced by greater attention to adaptive, potentially risk-reducing traits. Moreover, forgiveness and consideration of future consequences may have implications for accident prevention. PMID:18760093

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…

Gas-induced susceptibility artefacts on diffusion-weighted MRI of the rectum at 1.5 T - Effect of applying a micro-enema to improve image quality.

PubMed

van Griethuysen, Joost J M; Bus, Elyse M; Hauptmann, Michael; Lahaye, Max J; Maas, Monique; Ter Beek, Leon C; Beets, Geerard L; Bakers, Frans C H; Beets-Tan, Regina G H; Lambregts, Doenja M J

2018-02-01

Assess whether application of a micro-enema can reduce gas-induced susceptibility artefacts in Single-shot Echo Planar Imaging (EPI) Diffusion-weighted imaging of the rectum at 1.5 T. Retrospective analysis of n = 50 rectal cancer patients who each underwent multiple DWI-MRIs (1.5 T) from 2012 to 2016 as part of routine follow-up during a watch-and-wait approach after chemoradiotherapy. From March 2014 DWI-MRIs were routinely acquired after application of a preparatory micro-enema (Microlax ® ; 5 ml; self-administered shortly before acquisition); before March 2014 no bowel preparation was given. In total, 335 scans were scored by an experienced reader for the presence/severity of gas-artefacts (on b1000 DWI), ranging from 0 (no artefact) to 5 (severe artefact). A score ≥3 (moderate-severe) was considered a clinically relevant artefact. A random sample of 100 scans was re-assessed by a second independent reader to study inter-observer effects. Scores were compared between the scans performed without and with a preparatory micro-enema using univariable and multivariable logistic regression taking into account potential confounding factors (age/gender, acquisition parameters, MRI-hardware, rectoscopy prior to MRI). Clinically relevant gas-artefacts were seen in 24.3% (no micro-enema) vs. 3.7% (micro-enema), odds ratios were 0.118 in univariable and 0.230 in multivariable regression (P = 0.0005 and 0.0291). Mean severity score (±SD) was 1.19 ± 1.71 (no-enema) vs 0.32 ± 0.77 (micro-enema), odds ratios were 0.321 (P < 0.0001) and 0.489 (P = 0.0461) in uni- and multivariable regression, respectively. Inter-observer agreement was excellent (κ0.85). Use of a preparatory micro-enema shortly before rectal EPI-DWI examinations performed at 1.5 T MRI significantly reduces both the incidence and severity of gas-induced artefacts, compared to examinations performed without bowel preparation. Copyright © 2017 Elsevier B.V. All rights reserved.

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

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.

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

Relation Between Burnout Syndrome and Job Satisfaction Among Mental Health Workers

PubMed Central

Ogresta, Jelena; Rusac, Silvia; Zorec, Lea

2008-01-01

Aim To identify predictors of burnout syndrome, such as job satisfaction and manifestations of occupational stress, in mental health workers. Method The study included a snowball sample of 174 mental health workers in Croatia. The following measurement instruments were used: Maslach Burnout Inventory, Manifestations of Occupational Stress Survey, and Job Satisfaction Survey. We correlated dimensions of burnout syndrome with job satisfaction and manifestations of occupational stress dimensions. We also performed multiple regression analysis using three dimensions of burnout syndrome – emotional exhaustion, depersonalization, and personal accomplishment. Results Stepwise multiple regression analysis showed that pay and rewards satisfaction (β = -0.37), work climate (β = -0.18), advancement opportunities (β = 0.17), the degree of psychological (β = 0.41), and physical manifestations of occupational stress (β = 0.29) were significant predictors of emotional exhaustion (R = 0.76; F = 30.02; P<0.001). The frequency of negative emotional and behavioral reactions toward patients and colleagues (β = 0.48), psychological (β = 0.27) and physical manifestations of occupational stress (β = 0.24), and pay and rewards satisfaction (β = 0.22) were significant predictors of depersonalization (R = 0.57; F = 13.01; P<0.001). Satisfaction with the work climate (β = -0.20) was a significant predictor of lower levels of personal accomplishment (R = 0.20; F = 5.06; P<0.005). Conclusion Mental health workers exhibited a moderate degree of burnout syndrome, but there were no significant differences regarding their occupation. Generally, both dimensions of job satisfaction and manifestations of occupational stress proved to be relevant predictors of burnout syndrome. PMID:18581615

Resilience moderates the risk of depression and anxiety symptoms on suicidal ideation in patients with depression and/or anxiety disorders.

PubMed

Min, Jung-Ah; Lee, Chang-Uk; Chae, Jeong-Ho

2015-01-01

Few studies have investigated the role of protective factors for suicidal ideation, which include resilience and social support among psychiatric patients with depression and/or anxiety disorders who are at increased risk of suicide. Demographic data, history of childhood maltreatment, and levels of depression, anxiety, problematic alcohol use, resilience, perceived social support, and current suicidal ideation were collected from a total of 436 patients diagnosed with depression and/or anxiety disorders. Hierarchical multiple logistic regression analyses were used to identify the independent and interaction effects of potentially influencing factors. Moderate-severe suicidal ideation was reported in 24.5% of our sample. After controlling for relevant covariates, history of emotional neglect and sexual abuse, low resilience, and high depression and anxiety symptoms were sequentially included in the model. In the final model, high depression (adjusted odds ratio (OR)=9.33, confidence interval (CI) 3.99-21.77) and anxiety (adjusted OR=2.62, CI=1.24-5.53) were independently associated with moderate-severe suicidal ideation among risk factors whereas resilience was not. In the multiple logistic regression model that examined interaction effects between risk and protective factors, the interactions between resilience and depression (p<.001) and between resilience and anxiety were significant (p=.021). A higher level of resilience was protective against moderate-severe suicide ideation among those with higher levels of depression or anxiety symptoms. Our results indicate that resilience potentially moderates the risk of depression and anxiety symptoms on suicidal ideation in patients with depression and/or anxiety disorders. Assessment of resilience and intervention focused on resilience enhancement is suggested for suicide prevention. Copyright © 2014 Elsevier Inc. All rights reserved.

Effect of seafood mediated PCB exposure on desaturase activity and PUFA profile in Faroese septuagenarians.

PubMed

Tøttenborg, Sandra Søgaard; Choi, Anna L; Bjerve, Kristian S; Weihe, Pal; Grandjean, Philippe

2015-07-01

Polychlorinated biphenyl (PCB) exposure may affect serum concentrations of polyunsaturated fatty acids (PUFAs) by inhibiting desaturases ∆5 and ∆6 that drive their synthesis from precursor fatty acids. Such changes in the composition of fatty acids may affect cardiovascular disease risk, which is thought to increase at elevated PCB exposures. This population-based cross-sectional study examined 712 Faroese men and women aged 70-74 years. The serum phospholipid fraction of fasting blood samples was used to determine the PUFA profile, including linoleic acid, dihomo-γ-linolenic acid, arachidonic acid, eicosatrienoic acid, and other relevant fatty acids. Ratios between precursor and metabolite fatty acids were used as proxies for ∆5 and ∆6 desaturase activity. Tertiles of serum-PCB concentrations were used in multiple regression analyses to determine the association between the exposure and desaturase activity. In multiple regression models, PCB exposure was inversely related to the estimated Δ6 desaturase activity resulting in accumulation of precursor fatty acids and decrease in the corresponding product PUFAs. A positive association between PCB and Δ5 desaturation was also found. A relative increase in EA was also observed, though only in the third tertile of PCB exposure. Non-linear relationships between the exposure and the desaturase activity were not found. Consuming fish and seafood may not be translated into beneficial fatty acid profiles if the diet simultaneously causes exposure to PCBs. Although the desaturase estimates were likely influenced by dietary intakes of product PUFAs, the association between PCB exposure and ∆6 desaturase activity is plausible and may affect cardiovascular disease risk. Copyright © 2015 Elsevier Inc. All rights reserved.

Chronic low back pain and the transdiagnostic process: How do cognitive and emotional dysregulations contribute to the intensity of risk factors and pain?

PubMed

Le Borgne, Margaux; Boudoukha, Abdel Halim; Petit, Audrey; Roquelaure, Yves

2017-10-01

Based on a transdiagnostic approach, this study assesses the impact of cognitive and emotional processes (difficulties in emotional regulation, impulsiveness, rumination and somatosensory amplification) on the psychological risk factors of chronic low-back pain. The study was carried out with 256 patients with chronic low-back pain. All the variables were assessed through a booklet of 10 validated questionnaires. Multiple regression analysis and moderation analysis were performed. Predictors included in multiple regression models explain 3%-42% (adjusted R 2 ) of the variance in psychological risk factors. Moreover, analyses reveal a significant moderator effect of somatosensory amplification on the link between fear-avoidance beliefs linked to work and pain intensity (F (3;250) =12.33; p=.00), of somatosensory amplification and brooding on the link between depression and functional repercussions (FR) on everyday life (F (3;252) =13.36; p=.000; F (1;252) =12.42; p=.00), of the reflection dimension of rumination on the link between the helplessness dimension of catastrophizing and FRs on sociability (F (3;252) =37.02; p=.00). There is also a moderation analysis with a significant trend concerning the lack of emotional awareness and the difficulties in controlling impulsive behaviours. Our results indicate an important role of some dimensions of difficulties in emotional regulation, somatosensory amplification and rumination in the increase in negative affects and dysfunctional beliefs, and in the links between those psychological risk factors and pain/disability. This study identifies some cognitive and emotional dysregulations substantially involved in work-related chronic pain. This contribute to put in place psychotherapeutic protocols to tackle these deficits and dysregulations in a relevant way. Copyright © 2017 Scandinavian Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

The prediction of intelligence in preschool children using alternative models to regression.

PubMed

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Optimization of fixture layouts of glass laser optics using multiple kernel regression.

PubMed

Su, Jianhua; Cao, Enhua; Qiao, Hong

2014-05-10

We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Changes in regional cerebral blood flow in the right cortex homologous to left language areas are directly affected by left hemispheric damage in aphasic stroke patients: evaluation by Tc-ECD SPECT and novel analytic software.

PubMed

Uruma, G; Kakuda, W; Abo, M

2010-03-01

The objective of this study was to clarify the influence of regional cerebral blood flow (rCBF) changes in language-relevant areas of the dominant hemisphere on rCBF in each region in the non-dominant hemisphere in post-stroke aphasic patients. The study subjects were 27 aphasic patients who suffered their first symptomatic stroke in the left hemisphere. In each subject, we measured rCBF by means of 99mTc-ethylcysteinate dimmer single photon emission computed tomography (SPECT). The SPECT images were analyzed by the statistical imaging analysis programs easy Z-score Imaging System (eZIS) and voxel-based stereotactic extraction estimation (vbSEE). Segmented into Brodmann Area (BA) levels, Regions of Interest (ROIs) were set in language-relevant areas bilaterally, and changes in the relative rCBF as average negative and positive Z-values were computed fully automatically. To assess the relationship between rCBF changes of each ROIs in the left and right hemispheres, the Spearman ranked correlation analysis and stepwise multiple regression analysis were applied. Globally, a negative and asymmetric influence of rCBF changes in the language-relevant areas of the dominant hemisphere on the right hemisphere was found. The rCBF decrease in left BA22 significantly influenced the rCBF increase in right BA39, BA40, BA44 and BA45. The results suggested that the chronic increase in rCBF in the right language-relevant areas is due at least in part to reduction in the trancallosal inhibitory activity of the language-dominant left hemisphere caused by the stroke lesion itself and that these relationships are not always symmetric.

Adherence of older women with strength training and aerobic exercise

PubMed Central

Picorelli, Alexandra Miranda Assumpção; Pereira, Daniele Sirineu; Felício, Diogo Carvalho; Dos Anjos, Daniela Maria; Pereira, Danielle Aparecida Gomes; Dias, Rosângela Corrêa; Assis, Marcella Guimarães; Pereira, Leani Souza Máximo

2014-01-01

Background Participation of older people in a program of regular exercise is an effective strategy to minimize the physical decline associated with age. The purpose of this study was to assess adherence rates in older women enrolled in two different exercise programs (one aerobic exercise and one strength training) and identify any associated clinical or functional factors. Methods This was an exploratory observational study in a sample of 231 elderly women of mean age 70.5 years. We used a structured questionnaire with standardized tests to evaluate the relevant clinical and functional measures. A specific adherence questionnaire was developed by the researchers to determine motivators and barriers to exercise adherence. Results The adherence rate was 49.70% in the aerobic exercise group and 56.20% in the strength training group. Multiple logistic regression models for motivation were significant (P=0.003) for the muscle strengthening group (R2=0.310) and also significant (P=0.008) for the aerobic exercise group (R2=0.154). A third regression model for barriers to exercise was significant (P=0.003) only for the muscle strengthening group (R2=0.236). The present study shows no direct relationship between worsening health status and poor adherence. Conclusion Factors related to adherence with exercise in the elderly are multifactorial. PMID:24600212

Comparison of Subjective Refraction under Binocular and Monocular Conditions in Myopic Subjects.

PubMed

Kobashi, Hidenaga; Kamiya, Kazutaka; Handa, Tomoya; Ando, Wakako; Kawamorita, Takushi; Igarashi, Akihito; Shimizu, Kimiya

2015-07-28

To compare subjective refraction under binocular and monocular conditions, and to investigate the clinical factors affecting the difference in spherical refraction between the two conditions. We examined thirty eyes of 30 healthy subjects. Binocular and monocular refraction without cycloplegia was measured through circular polarizing lenses in both eyes, using the Landolt-C chart of the 3D visual function trainer-ORTe. Stepwise multiple regression analysis was used to assess the relations among several pairs of variables and the difference in spherical refraction in binocular and monocular conditions. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition (p < 0.001), whereas no significant differences were seen in subjective cylindrical refraction (p = 0.99). The explanatory variable relevant to the difference in spherical refraction between binocular and monocular conditions was the binocular spherical refraction (p = 0.032, partial regression coefficient B = 0.029) (adjusted R(2) = 0.230). No significant correlation was seen with other clinical factors. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition. Eyes with higher degrees of myopia are more predisposed to show the large difference in spherical refraction between these two conditions.

Comparison of Subjective Refraction under Binocular and Monocular Conditions in Myopic Subjects

PubMed Central

Kobashi, Hidenaga; Kamiya, Kazutaka; Handa, Tomoya; Ando, Wakako; Kawamorita, Takushi; Igarashi, Akihito; Shimizu, Kimiya

2015-01-01

To compare subjective refraction under binocular and monocular conditions, and to investigate the clinical factors affecting the difference in spherical refraction between the two conditions. We examined thirty eyes of 30 healthy subjects. Binocular and monocular refraction without cycloplegia was measured through circular polarizing lenses in both eyes, using the Landolt-C chart of the 3D visual function trainer-ORTe. Stepwise multiple regression analysis was used to assess the relations among several pairs of variables and the difference in spherical refraction in binocular and monocular conditions. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition (p < 0.001), whereas no significant differences were seen in subjective cylindrical refraction (p = 0.99). The explanatory variable relevant to the difference in spherical refraction between binocular and monocular conditions was the binocular spherical refraction (p = 0.032, partial regression coefficient B = 0.029) (adjusted R2 = 0.230). No significant correlation was seen with other clinical factors. Subjective spherical refraction in the monocular condition was significantly more myopic than that in the binocular condition. Eyes with higher degrees of myopia are more predisposed to show the large difference in spherical refraction between these two conditions. PMID:26218972

Seagrass meadows (Posidonia oceanica) distribution and trajectories of change

PubMed Central

Telesca, Luca; Belluscio, Andrea; Criscoli, Alessandro; Ardizzone, Giandomenico; Apostolaki, Eugenia T.; Fraschetti, Simonetta; Gristina, Michele; Knittweis, Leyla; Martin, Corinne S.; Pergent, Gérard; Alagna, Adriana; Badalamenti, Fabio; Garofalo, Germana; Gerakaris, Vasilis; Louise Pace, Marie; Pergent-Martini, Christine; Salomidi, Maria

2015-01-01

Posidonia oceanica meadows are declining at alarming rates due to climate change and human activities. Although P. oceanica is considered the most important and well-studied seagrass species of the Mediterranean Sea, to date there has been a limited effort to combine all the spatial information available and provide a complete distribution of meadows across the basin. The aim of this work is to provide a fine-scale assessment of (i) the current and historical known distribution of P. oceanica, (ii) the total area of meadows and (iii) the magnitude of regressive phenomena in the last decades. The outcomes showed the current spatial distribution of P. oceanica, covering a known area of 1,224,707 ha, and highlighted the lack of relevant data in part of the basin (21,471 linear km of coastline). The estimated regression of meadows amounted to 34% in the last 50 years, showing that this generalised phenomenon had to be mainly ascribed to cumulative effects of multiple local stressors. Our results highlighted the importance of enforcing surveys to assess the status and prioritize areas where cost-effective schemes for threats reduction, capable of reversing present patterns of change and ensuring P. oceanica persistence at Mediterranean scale, could be implemented. PMID:26216526

How well are journal and clinical article characteristics associated with the journal impact factor? a retrospective cohort study

PubMed Central

Lokker, Cynthia; Haynes, R. Brian; Chu, Rong; McKibbon, K. Ann; Wilczynski, Nancy L; Walter, Stephen D

2012-01-01

Objective: Journal impact factor (JIF) is often used as a measure of journal quality. A retrospective cohort study determined the ability of clinical article and journal characteristics, including appraisal measures collected at the time of publication, to predict subsequent JIFs. Methods: Clinical research articles that passed methods quality criteria were included. Each article was rated for relevance and newsworthiness by 3 to 24 physicians from a panel of more than 4,000 practicing clinicians. The 1,267 articles (from 103 journals) were divided 60∶40 into derivation (760 articles) and validation sets (507 articles), representing 99 and 88 journals, respectively. A multiple regression model was produced determining the association of 10 journal and article measures with the 2007 JIF. Results: Four of the 10 measures were significant in the regression model: number of authors, number of databases indexing the journal, proportion of articles passing methods criteria, and mean clinical newsworthiness scores. With the number of disciplines rating the article, the 5 variables accounted for 61% of the variation in JIF (R2 = 0.607, 95% CI 0.444 to 0.706, P<0.001). Conclusion: For the clinical literature, measures of scientific quality and clinical newsworthiness available at the time of publication can predict JIFs with 60% accuracy. PMID:22272156

Socioeconomic determinants of childhood overweight and obesity in China: the long arm of institutional power.

PubMed

Fu, Qiang; George, Linda K

2015-07-01

Previous studies have widely reported that the association between socioeconomic status (SES) and childhood overweight and obesity in China is significant and positive, which lends little support to the fundamental-cause perspective. Using multiple waves (1997, 2000, 2004 and 2006) of the China Health and Nutrition Survey (CHNS) (N = 2,556, 2,063, 1,431 and 1,242, respectively) and continuous BMI cut-points obtained from a polynomial method, (mixed-effect) logistic regression analyses show that parental state-sector employment, an important, yet overlooked, indicator of political power during the market transformation has changed from a risk factor for childhood overweight/obesity in 1997 to a protective factor for childhood overweight/obesity in 2006. Results from quantile regression analyses generate the same conclusions and demonstrate that the protective effect of parental state sector employment at high percentiles of BMI is robust under different estimation strategies. By bridging the fundamental causes perspective and theories of market transformation, this research not only documents the effect of political power on childhood overweight/obesity but also calls for the use of multifaceted, culturally-relevant stratification measures in testing the fundamental cause perspective across time and space. © 2015 Foundation for the Sociology of Health & Illness.

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

Estimation of premorbid general fluid intelligence using traditional Chinese reading performance in Taiwanese samples.

PubMed

Chen, Ying-Jen; Ho, Meng-Yang; Chen, Kwan-Ju; Hsu, Chia-Fen; Ryu, Shan-Jin

2009-08-01

The aims of the present study were to (i) investigate if traditional Chinese word reading ability can be used for estimating premorbid general intelligence; and (ii) to provide multiple regression equations for estimating premorbid performance on Raven's Standard Progressive Matrices (RSPM), using age, years of education and Chinese Graded Word Reading Test (CGWRT) scores as predictor variables. Four hundred and twenty-six healthy volunteers (201 male, 225 female), aged 16-93 years (mean +/- SD, 41.92 +/- 18.19 years) undertook the tests individually under supervised conditions. Seventy percent of subjects were randomly allocated to the derivation group (n = 296), and the rest to the validation group (n = 130). RSPM score was positively correlated with CGWRT score and years of education. RSPM and CGWRT scores and years of education were also inversely correlated with age, but the declining trend for RSPM performance against age was steeper than that for CGWRT performance. Separate multiple regression equations were derived for estimating RSPM scores using different combinations of age, years of education, and CGWRT score for both groups. The multiple regression coefficient of each equation ranged from 0.71 to 0.80 with the standard error of estimate between 7 and 8 RSPM points. When fitting the data of one group to the equations derived from its counterpart group, the cross-validation multiple regression coefficients ranged from 0.71 to 0.79. There were no significant differences in the 'predicted-obtained' RSPM discrepancies between any equations. The regression equations derived in the present study may provide a basis for estimating premorbid RSPM performance.

Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

PubMed

Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

2017-05-01

This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

A population-based study on the association between rheumatoid arthritis and voice problems.

PubMed

Hah, J Hun; An, Soo-Youn; Sim, Songyong; Kim, So Young; Oh, Dong Jun; Park, Bumjung; Kim, Sung-Gyun; Choi, Hyo Geun

2016-07-01

The objective of this study was to investigate whether rheumatoid arthritis increases the frequency of organic laryngeal lesions and the subjective voice complaint rate in those with no organic laryngeal lesion. We performed a cross-sectional study using the data from 19,368 participants (418 rheumatoid arthritis patients and 18,950 controls) of the 2008-2011 Korea National Health and Nutrition Examination Survey. The associations between rheumatoid arthritis and organic laryngeal lesions/subjective voice complaints were analyzed using simple/multiple logistic regression analysis with complex sample adjusting for confounding factors, including age, sex, smoking status, stress level, and body mass index, which could provoke voice problems. Vocal nodules, vocal polyp, and vocal palsy were not associated with rheumatoid arthritis in a multiple regression analysis, and only laryngitis showed a positive association (adjusted odds ratio, 1.59; 95 % confidence interval, 1.01-2.52; P = 0.047). Rheumatoid arthritis was associated with subjective voice discomfort in a simple regression analysis, but not in a multiple regression analysis. Participants with rheumatoid arthritis were older, more often female, and had higher stress levels than those without rheumatoid arthritis. These factors were associated with subjective voice complaints in both simple and multiple regression analyses. Rheumatoid arthritis was not associated with organic laryngeal diseases except laryngitis. Rheumatoid arthritis did not increase the odds ratio for subjective voice complaints. Voice problems in participants with rheumatoid arthritis originated from the characteristics of the rheumatoid arthritis group (higher mean age, female sex, and stress level) rather than rheumatoid arthritis itself.

Predicting MHC-II binding affinity using multiple instance regression

PubMed Central

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2011-01-01

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

A dimensional approach to understanding severity estimates and risk correlates of marijuana abuse and dependence in adults

PubMed Central

WU, LI-TZY; WOODY, GEORGE E.; YANG, CHONGMING; PAN, JENG-JONG; REEVE, BRYCE B.; BLAZER, DAN G.

2012-01-01

While item response theory (IRT) research shows a latent severity trait underlying response patterns of substance abuse and dependence symptoms, little is known about IRT-based severity estimates in relation to clinically relevant measures. In response to increased prevalences of marijuana-related treatment admissions, an elevated level of marijuana potency, and the debate on medical marijuana use, we applied dimensional approaches to understand IRT-based severity estimates for marijuana use disorders (MUDs) and their correlates while simultaneously considering gender- and race/ethnicity-related differential item functioning (DIF). Using adult data from the 2008 National Survey on Drug Use and Health (N=37,897), DSM-IV criteria for MUDs among past-year marijuana users were examined by IRT, logistic regression, and multiple indicators–multiple causes (MIMIC) approaches. Among 6,917 marijuana users, 15% met criteria for a MUD; another 24% exhibited subthreshold dependence. Abuse criteria were highly correlated with dependence criteria (correlation=0.90), indicating unidimensionality; item information curves revealed redundancy in multiple criteria. MIMIC analyses showed that MUD criteria were positively associated with weekly marijuana use, early marijuana use, other substance use disorders, substance abuse treatment, and serious psychological distress. African Americans and Hispanics showed higher levels of MUDs than whites, even after adjusting for race/ethnicity-related DIF. The redundancy in multiple criteria suggests an opportunity to improve efficiency in measuring symptom-level manifestations by removing low-informative criteria. Elevated rates of MUDs among African Americans and Hispanics require research to elucidate risk factors and improve assessments of MUDs for different racial/ethnic groups. PMID:22351489

Quantifying the statistical importance of utilizing regression over classic energy intensity calculations for tracking efficiency improvements in industry

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

Nimbalkar, Sachin U.; Wenning, Thomas J.; Guo, Wei

In the United States, manufacturing facilities account for about 32% of total domestic energy consumption in 2014. Robust energy tracking methodologies are critical to understanding energy performance in manufacturing facilities. Due to its simplicity and intuitiveness, the classic energy intensity method (i.e. the ratio of total energy use over total production) is the most widely adopted. However, the classic energy intensity method does not take into account the variation of other relevant parameters (i.e. product type, feed stock type, weather, etc.). Furthermore, the energy intensity method assumes that the facilities’ base energy consumption (energy use at zero production) is zero,more » which rarely holds true. Therefore, it is commonly recommended to utilize regression models rather than the energy intensity approach for tracking improvements at the facility level. Unfortunately, many energy managers have difficulties understanding why regression models are statistically better than utilizing the classic energy intensity method. While anecdotes and qualitative information may convince some, many have major reservations about the accuracy of regression models and whether it is worth the time and effort to gather data and build quality regression models. This paper will explain why regression models are theoretically and quantitatively more accurate for tracking energy performance improvements. Based on the analysis of data from 114 manufacturing plants over 12 years, this paper will present quantitative results on the importance of utilizing regression models over the energy intensity methodology. This paper will also document scenarios where regression models do not have significant relevance over the energy intensity method.« less

Early Home Activities and Oral Language Skills in Middle Childhood: A Quantile Analysis

ERIC Educational Resources Information Center

Law, James; Rush, Robert; King, Tom; Westrupp, Elizabeth; Reilly, Sheena

2018-01-01

Oral language development is a key outcome of elementary school, and it is important to identify factors that predict it most effectively. Commonly researchers use ordinary least squares regression with conclusions restricted to average performance conditional on relevant covariates. Quantile regression offers a more sophisticated alternative.…

Understanding the role of sleep quality and sleep duration in commercial driving safety.

PubMed

Lemke, Michael K; Apostolopoulos, Yorghos; Hege, Adam; Sönmez, Sevil; Wideman, Laurie

2016-12-01

Long-haul truck drivers in the United States suffer disproportionately high injury rates. Sleep is a critical factor in these outcomes, contributing to fatigue and degrading multiple aspects of safety-relevant performance. Both sleep duration and sleep quality are often compromised among truck drivers; however, much of the efforts to combat fatigue focus on sleep duration rather than sleep quality. Thus, the current study has two objectives: (1) to determine the degree to which sleep impacts safety-relevant performance among long-haul truck drivers; and (2) to evaluate workday and non-workday sleep quality and duration as predictors of drivers' safety-relevant performance. A non-experimental, descriptive, cross-sectional design was employed to collect survey and biometric data from 260 long-haul truck drivers. The Trucker Sleep Disorders Survey was developed to assess sleep duration and quality, the impact of sleep on job performance and accident risk, and other relevant work organization characteristics. Descriptive statistics assessed work organization variables, sleep duration and quality, and frequency of engaging in safety-relevant performance while sleepy. Linear regression analyses were conducted to evaluate relationships between sleep duration, sleep quality, and work organization variables with safety composite variables. Drivers reported long work hours, with over 70% of drivers working more than 11h daily. Drivers also reported a large number of miles driven per week, with an average of 2,812.61 miles per week, and frequent violations of hours-of-service rules, with 43.8% of drivers "sometimes to always" violating the "14-h rule." Sleep duration was longer, and sleep quality was better, on non-workdays compared on workdays. Drivers frequently operated motor vehicles while sleepy, and sleepiness impacted several aspects of safety-relevant performance. Sleep quality was better associated with driving while sleepy and with job performance and concentration than sleep duration. Sleep duration was better associated with accidents and accident risk than sleep quality. Sleep quality appears to be better associated with safety-relevant performance among long-haul truck drivers than sleep duration. Comprehensive and multilevel efforts are needed to meaningfully address sleep quality among drivers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

PubMed

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-02-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism for the effects on RBC and WBC while no interactions were proved for the joint effect on PLT count. These results confirm that the assessment of interactions between chemicals in the mixture greatly depends on the concept or method used for this evaluation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Estimation of 1RM for knee extension based on the maximal isometric muscle strength and body composition.

PubMed

Kanada, Yoshikiyo; Sakurai, Hiroaki; Sugiura, Yoshito; Arai, Tomoaki; Koyama, Soichiro; Tanabe, Shigeo

2017-11-01

[Purpose] To create a regression formula in order to estimate 1RM for knee extensors, based on the maximal isometric muscle strength measured using a hand-held dynamometer and data regarding the body composition. [Subjects and Methods] Measurement was performed in 21 healthy males in their twenties to thirties. Single regression analysis was performed, with measurement values representing 1RM and the maximal isometric muscle strength as dependent and independent variables, respectively. Furthermore, multiple regression analysis was performed, with data regarding the body composition incorporated as another independent variable, in addition to the maximal isometric muscle strength. [Results] Through single regression analysis with the maximal isometric muscle strength as an independent variable, the following regression formula was created: 1RM (kg)=0.714 + 0.783 × maximal isometric muscle strength (kgf). On multiple regression analysis, only the total muscle mass was extracted. [Conclusion] A highly accurate regression formula to estimate 1RM was created based on both the maximal isometric muscle strength and body composition. Using a hand-held dynamometer and body composition analyzer, it was possible to measure these items in a short time, and obtain clinically useful results.

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

NASA Technical Reports Server (NTRS)

Stolzer, Alan J.; Halford, Carl

2007-01-01

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

Neuron’s eye view: Inferring features of complex stimuli from neural responses

PubMed Central

Chen, Xin; Beck, Jeffrey M.

2017-01-01

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world—contrast and luminance for vision, pitch and intensity for sound—and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set. PMID:28827790

Atrophied Brain Lesion Volume: A New Imaging Biomarker in Multiple Sclerosis.

PubMed

Dwyer, Michael G; Bergsland, Niels; Ramasamy, Deepa P; Jakimovski, Dejan; Weinstock-Guttman, Bianca; Zivadinov, Robert

2018-06-01

Lesion accrual in multiple sclerosis (MS) is an important and clinically relevant measure, used extensively as an imaging trial endpoint. However, lesions may also shrink or disappear entirely due to atrophy. Although generally ignored or treated as a nuisance, this phenomenon may actually be an important stand-alone imaging biomarker. Therefore, we investigated the rate of brain lesion loss due to atrophy (atrophied lesion volume) in MS subtypes compared to baseline lesion volume and to new and enlarging lesion volumes, and evaluated the independent predictive value of this phenomenon for clinical disability. A total of 192 patients (18 clinically isolated syndrome, 126 relapsing-remitting MS, and 48 progressive) received 3T magnetic resonance imaging at baseline and 5 years. Lesions were quantified at baseline, and new/enlarging lesion volumes were calculated over the study interval. Atrophied lesion volume was calculated by combining baseline lesion masks with follow-up SIENAX-derived cerebrospinal fluid partial volume maps. Measures were compared between disease subgroups, and correlations with disability change (Expanded Disability Status Scale [EDSS]) were evaluated. Hierarchical regression was employed to determine the unique additive value of atrophied lesion volume. Atrophied lesion volume was different between MS subtypes (P = .02), and exceeded new lesion volume accumulation in progressive MS (298.1 vs. 75.5 mm 3 ). Atrophied lesion volume was the only significant correlate of EDSS change (r = .192 relapsing, r = .317 progressive, P < .05), and explained significant additional variance when controlling for brain atrophy and new/enlarging lesion volume (R 2 .092 vs. .045, P = .003). Atrophied lesion volume is a unique and clinically relevant imaging marker in MS, with particular promise in progressive MS. Copyright © 2018 by the American Society of Neuroimaging.

Occurrence and potential human-health relevance of volatile organic compounds in drinking water from domestic wells in the United States

USGS Publications Warehouse

Rowe, B.L.; Toccalino, P.L.; Moran, M.J.; Zogorski, J.S.; Price, C.V.

2011-01-01

BACKGROUND: As the population and demand for safe drinking water from domestic wells increase, it is important to examine water quality and contaminant occurrence. A national assessment in 2006 by the U.S. Geological Survey reported findings for 55 volatile organic compounds (VOCs) based on 2,401 domestic wells sampled during 1985-2002. OBJECTIVES: We examined the occurrence of individual and multiple VOCs and assessed the potential human-health relevance of VOC concentrations. We also identified hydrogeologic and anthropogenic variables that influence the probability of VOC occurrence. METHODS: The domestic well samples were collected at the wellhead before treatment of water and analyzed for 55 VOCs. Results were used to examine VOC occurrence and identify associations of multiple explanatory variables using logistic regression analyses. We used a screening-level assessment to compare VOC concentrations to U.S. Environmental Protection Agency maximum contaminant levels (MCLs) and health-based screening levels. RESULTS: We detected VOCs in 65% of the samples; about one-half of these samples contained VOC mixtures. Frequently detected VOCs included chloroform, toluene, 1,2,4-trimethylbenzene, and perchloroethene. VOC concentrations generally were < 1 ??g/L. One or more VOC concentrations were greater than MCLs in 1.2% of samples, including dibromochloropropane, 1,2-dichloropropane, and ethylene dibromide (fumigants); perchloroethene and trichloroethene (solvents); and 1,1-dichloroethene (organic synthesis compound). CONCLUSIONS: Drinking water supplied by domestic wells is vulnerable to low-level VOC contamination. About 1% of samples had concentrations of potential human-health concern. Identifying factors associated with VOC occurrence may aid in understanding the sources, transport, and fate of VOCs in groundwater.

Occurrence and Potential Human-Health Relevance of Volatile Organic Compounds in Drinking Water from Domestic Wells in the United States

PubMed Central

Rowe, Barbara L.; Toccalino, Patricia L.; Moran, Michael J.; Zogorski, John S.; Price, Curtis V.

2007-01-01

Background As the population and demand for safe drinking water from domestic wells increase, it is important to examine water quality and contaminant occurrence. A national assessment in 2006 by the U.S. Geological Survey reported findings for 55 volatile organic compounds (VOCs) based on 2,401 domestic wells sampled during 1985–2002. Objectives We examined the occurrence of individual and multiple VOCs and assessed the potential human-health relevance of VOC concentrations. We also identified hydrogeologic and anthropogenic variables that influence the probability of VOC occurrence. Methods The domestic well samples were collected at the wellhead before treatment of water and analyzed for 55 VOCs. Results were used to examine VOC occurrence and identify associations of multiple explanatory variables using logistic regression analyses. We used a screening-level assessment to compare VOC concentrations to U.S. Environmental Protection Agency maximum contaminant levels (MCLs) and health-based screening levels. Results We detected VOCs in 65% of the samples; about one-half of these samples contained VOC mixtures. Frequently detected VOCs included chloroform, toluene, 1,2,4-trimethylbenzene, and perchloroethene. VOC concentrations generally were < 1 μg/L. One or more VOC concentrations were greater than MCLs in 1.2% of samples, including dibromochloropropane, 1,2-dichloropropane, and ethylene dibromide (fumigants); perchloroethene and trichloroethene (solvents); and 1,1-dichloroethene (organic synthesis compound). Conclusions Drinking water supplied by domestic wells is vulnerable to low-level VOC contamination. About 1% of samples had concentrations of potential human-health concern. Identifying factors associated with VOC occurrence may aid in understanding the sources, transport, and fate of VOCs in groundwater. PMID:18007981

Occurrence and potential human-health relevance of volatile organic compounds in drinking water from domestic wells in the United States.

USGS Publications Warehouse

Rowe, B.L.; Toccalino, P.L.; Moran, M.J.; Zogorski, J.S.; Price, C.V.

2007-01-01

BACKGROUND: As the population and demand for safe drinking water from domestic wells increase, it is important to examine water quality and contaminant occurrence. A national assessment in 2006 by the U.S. Geological Survey reported findings for 55 volatile organic compounds (VOCs) based on 2,401 domestic wells sampled during 1985-2002. OBJECTIVES: We examined the occurrence of individual and multiple VOCs and assessed the potential human-health relevance of VOC concentrations. We also identified hydrogeologic and anthropogenic variables that influence the probability of VOC occurrence. METHODS: The domestic well samples were collected at the wellhead before treatment of water and analyzed for 55 VOCs. Results were used to examine VOC occurrence and identify associations of multiple explanatory variables using logistic regression analyses. We used a screening-level assessment to compare VOC concentrations to U.S. Environmental Protection Agency maximum contaminant levels (MCLs) and health-based screening levels. RESULTS: We detected VOCs in 65% of the samples; about one-half of these samples contained VOC mixtures. Frequently detected VOCs included chloroform, toluene, 1,2,4-trimethylbenzene, and perchloroethene. VOC concentrations generally were < 1 microg/L. One or more VOC concentrations were greater than MCLs in 1.2% of samples, including dibromochloropropane, 1,2-dichloropropane, and ethylene dibromide (fumigants); perchloroethene and trichloroethene (solvents); and 1,1-dichloroethene (organic synthesis compound). CONCLUSIONS: Drinking water supplied by domestic wells is vulnerable to low-level VOC contamination. About 1% of samples had concentrations of potential human-health concern. Identifying factors associated with VOC occurrence may aid in understanding the sources, transport, and fate of VOCs in groundwater.

[Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].

PubMed

Vanegas, Jairo; Vásquez, Fabián

Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Cross Validation of Selection of Variables in Multiple Regression.

DTIC Science & Technology

1979-12-01

55 vii CROSS VALIDATION OF SELECTION OF VARIABLES IN MULTIPLE REGRESSION I Introduction Background Long term DoD planning gcals...028545024 .31109000 BF * SS - .008700618 .0471961 Constant - .70977903 85.146786 55 had adequate predictive capabilities; the other two models (the...71ZCO F111D Control 54 73EGO FlIID Computer, General Purpose 55 73EPO FII1D Converter-Multiplexer 56 73HAO flllD Stabilizer Platform 57 73HCO F1ID

Quantitative Assessment of Cervical Vertebral Maturation Using Cone Beam Computed Tomography in Korean Girls

PubMed Central

Byun, Bo-Ram; Kim, Yong-Il; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R 2 had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. PMID:25878721

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

PubMed

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Adjusted variable plots for Cox's proportional hazards regression model.

PubMed

Hall, C B; Zeger, S L; Bandeen-Roche, K J

1996-01-01

Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

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

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

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.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

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.

Development and Validation of MMPI-2-RF Scales for Indexing Triarchic Psychopathy Constructs.

PubMed

Sellbom, Martin; Drislane, Laura E; Johnson, Alexandria K; Goodwin, Brandee E; Phillips, Tasha R; Patrick, Christopher J

2016-10-01

The triarchic model characterizes psychopathy in terms of three distinct dispositional constructs of boldness, meanness, and disinhibition. The model can be operationalized through scales designed specifically to index these domains or by using items from other inventories that provide coverage of related constructs. The present study sought to develop and validate scales for assessing the triarchic model domains using items from the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). A consensus rating approach was used to identify items relevant to each triarchic domain, and following psychometric refinement, the resulting MMPI-2-RF-based triarchic scales were evaluated for convergent and discriminant validity in relation to multiple psychopathy-relevant criterion variables in offender and nonoffender samples. Expected convergent and discriminant associations were evident very clearly for the Boldness and Disinhibition scales and somewhat less clearly for the Meanness scale. Moreover, hierarchical regression analyses indicated that all MMPI-2-RF triarchic scales incremented standard MMPI-2-RF scale scores in predicting extant triarchic model scale scores. The widespread use of MMPI-2-RF in clinical and forensic settings provides avenues for both clinical and research applications in contexts where traditional psychopathy measures are less likely to be administered. © The Author(s) 2015.

Motivational and social cognitive predictors of doping intentions in elite sports: an integrated approach.

PubMed

Barkoukis, V; Lazuras, L; Tsorbatzoudis, H; Rodafinos, A

2013-10-01

Doping use is an important issue in both competitive and non-competitive sports, and poses potentially irreversible health consequences to users. Scholars increasingly call for theory-driven studies on the psychosocial processes underlying doping use that will inform subsequent policy-making and prevention interventions. The aim of the study was to implement an integrative theoretical model to assess the direct and indirect effects of motivational variables, moral orientations, and social cognitions on doping intentions. A randomly selected and representative sample of 750 elite athletes anonymously completed a battery of questionnaires on motivational and moral constructs, and social cognitions related to doping. Hierarchical linear regression analysis and multiple mediation modeling were used. The effects of achievement goals and moral orientations were significantly mediated by attitudinal, normative, and self-efficacy beliefs, in both lifetime ever and never doping users. Moral orientations indirectly predicted the doping intentions of never users, but did not predict ever users' doping intentions. Achievement goals and sportspersonship orientations influence doping intentions indirectly, through the effects of attitudes and self-efficacy beliefs. Sportspersonship (moral) orientations were relevant to doping intentions among athletes with no prior experiences with doping, while achievement goals and situational temptation were relevant to both lifetime never and ever dopers. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

[Work related predictors for "satisfaction with dementia care" among nurses working in nursing homes].

PubMed

Schmidt, Sascha G; Palm, Rebecca; Dichter, Martin; Hasselhorn, Hans Martin

2011-04-01

In German nursing homes dementia care is gaining increasing relevance. Dementia care is known to bear the high risk of a substantial occupational burden among nursing staff. Within this context, the "nurses' satisfaction with the care for residents with dementia" is investigated. Secondary data of the German 3q-study is used to assess degrees of nurses' satisfaction with the care for residents with dementia and potential work related predictors. Data from 813 nurses and nursing aides working in 53 nursing homes were included. 42% of all nursing staff was dissatisfied with the care for residents with dementia in their institution, however, pronounced differences were found between the institutions. Registered nurses and nurses in leading positions were more dissatisfied. A multiple regression analysis indicates that high "quantitative demands", low "leadership quality" and "social interaction with other professions" are strong predictors for nurses' satisfaction with the care for residents with dementia. No association was found for "emotional demands" and "possibilities for development". The results indicate that the "nurses" satisfaction with the care for residents with dementia" may be a highly relevant work factor for nursing staff in nursing homes which deserves additional attention in practice and research. The high predictive power of several work organisational factors implies that preventive action should also include work organisational factors.

An exploration of metacognitive beliefs and thought control strategies in bipolar disorder.

PubMed

Østefjells, Tiril; Melle, Ingrid; Aminoff, Sofie R; Hellvin, Tone; Hagen, Roger; Lagerberg, Trine Vik; Lystad, June Ullevoldsæter; Røssberg, Jan Ivar

2017-02-01

Metacognitive factors influence depression, but are largely unexplored in bipolar disorders. We examined i) differences in metacognitive beliefs and thought control strategies between individuals with bipolar disorder and controls, and ii) to what extent clinical characteristics were related to levels of metacognitive beliefs and thought control strategies in bipolar disorder. Eighty patients with bipolar disorder were assessed for age at onset of affective disorder, number of affective episodes, symptoms of mania and depression, metacognitive beliefs (MCQ-30) and thought control strategies (TCQ). Control subjects (N=166) completed MCQ-30 and TCQ. Factors impacting on metacognitive beliefs and thought control strategies were explored with multiple linear regressions. Patients with bipolar disorder reported higher levels of unhelpful metacognitive beliefs and thought control strategies than controls. Metacognitive beliefs were mainly influenced by depressive symptoms, and age at onset of affective illness. Thought control strategies were mainly influenced by metacognitive beliefs and age at onset of affective illness. Our findings suggest that metacognitive beliefs and control strategies are relevant in bipolar disorder. Depression and age at onset of affective disorder could contribute to metacognitive beliefs in bipolar disorder, and influence the use of thought control strategies. This indicates potential relationships that warrant further investigation for clinical relevance. Copyright © 2016 Elsevier Inc. All rights reserved.

Premorbid IQ Predicts Postconcussive Symptoms in OEF/OIF/OND Veterans with mTBI.

PubMed

Stewart-Willis, Jada J; Heyanka, Daniel; Proctor-Weber, Zoe; England, Heather; Bruhns, Maya

2018-03-01

Extant literature has demonstrated that symptoms of postconcussive syndrome (PCS) persist well beyond the expected 3-month post-injury recovery period in a minority of individuals with mild traumatic brain injury (mTBI). Suboptimal performance on validity measures and pre- and post-injury psychosocial stressors - rather than actual mTBI or current cognitive functioning - have been identified as predictors of chronic PCS. Whether premorbid IQ has any influence on chronic PCS has been understudied, in the context of established psychogenic etiologies. The sample included 31 veterans, who underwent mTBI neuropsychological evaluations six or more months post-injury in a VA outpatient neuropsychology clinic. A two-step multiple linear regression was conducted to examine the effects on the outcome variable, PCS (Neurobehavioral Symptom Inventory), of the following predictors: cognitive functioning (Repeatable Battery for the Assessment of Neuropsychological Status; Attention, Immediate Memory, and Delayed Memory Indices), performance validity, depression (Beck Depression Inventory-Second Edition), posttraumatic stress disorder (PTSD Checklist, Civilian Version), quality of sleep (Pittsburgh Sleep Quality Index), pain (Brief Pain Inventory), education, and Premorbid IQ (Wechsler Test of Adult Reading). The overall regression model containing all nine predictor variables was statistically significant. Depression (p < .05) and premorbid IQ (p < .05) were the most salient predictors of chronic PCS; in that lower premorbid IQ and greater endorsed symptoms of depression were associated with higher PCS scores. In Step 2 of the multiple linear regression, the WTAR explained an additional 6.7% of the variance in PCS after controlling for psychosocial stressors and current cognitive ability. The findings support premorbid IQ as a unique and relevant predictor of chronic PCS, with significance variance accounted for beyond education, cognitive functioning, and psychosocial variables. Given the predictive relationship between premorbid IQ and PCS, adapting postconcussive interventions to meet the specific needs of individuals with varying levels of intellect may be important in minimizing ongoing symptomatology. Published by Oxford University Press 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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

Understanding the Personality and Behavioral Mechanisms Defining Hypersexuality in Men Who Have Sex with Men

PubMed Central

Miner, Michael H.; Romine, Rebecca Swinburne; Raymond, Nancy; Janssen, Erick; MacDonald, Angus; Coleman, Eli

2016-01-01

Objective The purpose of this study was to investigate personality factors and behavioral mechanisms that are relevant to hypersexuality in men who have sex with men. Method A sample of 242 men who have sex with men were recruited from various sites in a moderate size mid-western city. Participants were assigned to hypersexuality or control group using a SCID-type interview. Self-report inventories were administered that measured the broad band personality constructs of positive emotionality, negative emotionality and constraint, and more narrow constructs related to sexual behavioral control, behavioral activation, behavioral inhibition, sexual excitation, sexual inhibition, impulsivity, ADHD, and sexual behavior. Hierarchical logistic regression was used to determine the relationship between these personality and behavioral variables and group membership. Results A hierarchical logistic regression, controlling for age, revealed a significant positive relationship between hypersexuality and negative emotionality and a negative relationship with constraint. None of the behavioral mechanism variables entered this equation. However, a hierarchical multiple regression predicting sexual behavioral control indicated that lack of such control was positively related to sexual excitation and sexual inhibition due to the threat of performance failure and negatively related to sexual inhibition due to the threat of performance consequences and general behavioral inhibition Conclusions Hypersexuality was found to be related to two broad personality factors that are characterized by emotional reactivity, risk-taking, and impulsivity. The associated lack of sexual behavior control is influenced by both sexual excitatory and inhibitory mechanisms, but not general behavioral activation and inhibitory mechanisms. PMID:27486137

A "candidate-interactome" aggregate analysis of genome-wide association data in multiple sclerosis.

PubMed

Mechelli, Rosella; Umeton, Renato; Policano, Claudia; Annibali, Viviana; Coarelli, Giulia; Ricigliano, Vito A G; Vittori, Danila; Fornasiero, Arianna; Buscarinu, Maria Chiara; Romano, Silvia; Salvetti, Marco; Ristori, Giovanni

2013-01-01

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a "candidate interactome" (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms.

A “Candidate-Interactome” Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

PubMed Central

Policano, Claudia; Annibali, Viviana; Coarelli, Giulia; Ricigliano, Vito A. G.; Vittori, Danila; Fornasiero, Arianna; Buscarinu, Maria Chiara; Romano, Silvia; Salvetti, Marco; Ristori, Giovanni

2013-01-01

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a “candidate interactome” (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms. PMID:23696811

Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

PubMed

Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

2017-01-01

Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

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

PubMed

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

2015-01-01

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

Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

PubMed

Wang, Liqin; Haug, Peter J; Del Fiol, Guilherme

2017-05-01

Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies. We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s). Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset. It is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales.

PubMed

Pratt, Bethany; Chang, Heejun

2012-03-30

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R(2) values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants. Copyright © 2012 Elsevier B.V. All rights reserved.

Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

DTIC Science & Technology

1981-09-01

corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2014 CFR

2014-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2011 CFR

2011-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2013 CFR

2013-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2012 CFR

2012-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

Toward customer-centric organizational science: A common language effect size indicator for multiple linear regressions and regressions with higher-order terms.

PubMed

Krasikova, Dina V; Le, Huy; Bachura, Eric

2018-06-01

To address a long-standing concern regarding a gap between organizational science and practice, scholars called for more intuitive and meaningful ways of communicating research results to users of academic research. In this article, we develop a common language effect size index (CLβ) that can help translate research results to practice. We demonstrate how CLβ can be computed and used to interpret the effects of continuous and categorical predictors in multiple linear regression models. We also elaborate on how the proposed CLβ index is computed and used to interpret interactions and nonlinear effects in regression models. In addition, we test the robustness of the proposed index to violations of normality and provide means for computing standard errors and constructing confidence intervals around its estimates. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Schistosomiasis Breeding Environment Situation Analysis in Dongting Lake Area

NASA Astrophysics Data System (ADS)

Li, Chuanrong; Jia, Yuanyuan; Ma, Lingling; Liu, Zhaoyan; Qian, Yonggang

2013-01-01

Monitoring environmental characteristics, such as vegetation, soil moisture et al., of Oncomelania hupensis (O. hupensis)’ spatial/temporal distribution is of vital importance to the schistosomiasis prevention and control. In this study, the relationship between environmental factors derived from remotely sensed data and the density of O. hupensis was analyzed by a multiple linear regression model. Secondly, spatial analysis of the regression residual was investigated by the semi-variogram method. Thirdly, spatial analysis of the regression residual and the multiple linear regression model were both employed to estimate the spatial variation of O. hupensis density. Finally, the approach was used to monitor and predict the spatial and temporal variations of oncomelania of Dongting Lake region, China. And the areas of potential O. hupensis habitats were predicted and the influence of Three Gorges Dam (TGB)project on the density of O. hupensis was analyzed.

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

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.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Do evoked potentials contribute to the functional follow-up and clinical prognosis of multiple sclerosis?

PubMed

Giffroy, Xavier; Maes, Nathalie; Albert, Adelin; Maquet, Pierre; Crielaard, Jean-Michel; Dive, Dominique

2017-03-01

The clinical variability and complexity of multiple sclerosis (MS) challenges the individual clinical course prognostication. This study aimed to find out whether multimodal evoked potentials (EP) correlate with the motor components of multiple sclerosis functional composite (MSFCm) and predict clinically relevant motor functional deterioration. One hundred MS patients were assessed at baseline (T 0 ) and about 7.5 years later (T 1 ), with visual, somatosensory and motor EP and rated on the Expanded Disability Status Scale (EDSS) and the MSFCm, including the 9 Hole Peg Test and the Timed 25 Foot Walk (T25FW). The Spearman correlation coefficient (r S ) was used to evaluate the cross-sectional and longitudinal relationship between EP Z scores and clinical findings. The predictive value of baseline electrophysiological data for clinical worsening (EDSS, 9-HPT, T25FW, MSFCm) during follow-up was assessed by logistic regression analysis. Unlike longitudinal correlations, cross-sectional correlations between EP Z scores and clinical outcomes were all significant and ranged between 0.22 and 0.67 (p < 0.05). The global EP Z score was systematically predictive of EDSS and MSFCm worsening over time (all p < 0.05). EP latency was a better predictor than amplitude, although weaker than latency and amplitude aggregation in the global EP Z score. The study demonstrates that EP numerical scores can be used for motor function monitoring and outcome prediction in patients with MS.

Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

PubMed

Agha, Salah R; Alnahhal, Mohammed J

2012-11-01

The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

A Modified Double Multiple Nonlinear Regression Constitutive Equation for Modeling and Prediction of High Temperature Flow Behavior of BFe10-1-2 Alloy

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying

2018-01-01

Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.

Multiple-input multiple-output causal strategies for gene selection.

PubMed

Bontempi, Gianluca; Haibe-Kains, Benjamin; Desmedt, Christine; Sotiriou, Christos; Quackenbush, John

2011-11-25

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting. We show in synthetic case study that a better prioritization of causal variables can be obtained by considering a relevance score which incorporates a causal term. In addition we show, in a meta-analysis study of six publicly available breast cancer microarray datasets, that the improvement occurs also in terms of accuracy. The biological interpretation of the results confirms the potential of a causal approach to gene selection. Integrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

Socioeconomic conditions across life related to multiple measures of the endocrine system in older adults: Longitudinal findings from a British birth cohort study

PubMed Central

Bann, David; Hardy, Rebecca; Cooper, Rachel; Lashen, Hany; Keevil, Brian; Wu, Frederick C.W.; Holly, Jeff M.P.; Ong, Ken K.; Ben-Shlomo, Yoav; Kuh, Diana

2015-01-01

Background Little is known about how socioeconomic position (SEP) across life impacts on different axes of the endocrine system which are thought to underlie the ageing process and its adverse consequences. We examined how indicators of SEP across life related to multiple markers of the endocrine system in late midlife, and hypothesized that lower SEP across life would be associated with an adverse hormone profile across multiple axes. Methods Data were from a British cohort study of 875 men and 905 women followed since their birth in March 1946 with circulating free testosterone and insulin-like growth factor-I (IGF-I) measured at both 53 and 60–64 years, and evening cortisol at 60–64 years. Indicators of SEP were ascertained prospectively across life—paternal occupational class at 4, highest educational attainment at 26, household occupational class at 53, and household income at 60–64 years. Associations between SEP and hormones were investigated using multiple regression and logistic regression models. Results Lower SEP was associated with lower free testosterone among men, higher free testosterone among women, and lower IGF-I and higher evening cortisol in both sexes. For example, the mean standardised difference in IGF-I comparing the lowest with the highest educational attainment at 26 years (slope index of inequality) was −0.4 in men (95% CI -0.7 to −0.2) and −0.4 in women (−0.6 to −0.2). Associations with each hormone differed by SEP indicator used and sex, and were particularly pronounced when using a composite adverse hormone score. For example, the odds of having 1 additional adverse hormone concentration in the lowest compared with highest education level were 3.7 (95% CI: 2.1, 6.3) among men, and 1.6 (1.0, 2.7) among women (P (sex interaction) = 0.02). We found no evidence that SEP was related to apparent age-related declines in free testosterone or IGF-I. Conclusions Lower SEP was associated with an adverse hormone profile across multiple endocrine axes. SEP differences in endocrine function may partly underlie inequalities in health and function in later life, and may reflect variations in biological rates of ageing. Further studies are required to assess the likely functional relevance of these associations. PMID:26588434

What's new in multiple sclerosis spasticity research? Poster session highlights.

PubMed

Linker, Ralf

2017-11-01

Each year at the Multiple Sclerosis Experts Summit, relevant research in the field of multiple sclerosis spasticity is featured in poster sessions. The main studies presented at this year's meeting are summarized herein.

Modeling Culex tarsalis abundance on the northern Colorado front range using a landscape-level approach.

PubMed

Schurich, Jessica A; Kumar, Sunil; Eisen, Lars; Moore, Chester G

2014-03-01

Remote sensing and Geographic Information System (GIS) data can be used to identify larval mosquito habitats and predict species distribution and abundance across a landscape. An understanding of the landscape features that impact abundance and dispersal can then be applied operationally in mosquito control efforts to reduce the transmission of mosquito-borne pathogens. In an effort to better understand the effects of landscape heterogeneity on the abundance of the West Nile virus (WNV) vector Culex tarsalis, we determined associations between GIS-based environmental data at multiple spatial extents and monthly abundance of adult Cx. tarsalis in Larimer County and Weld County, CO. Mosquito data were collected from Centers for Disease Control and Prevention miniature light traps operated as part of local WNV surveillance efforts. Multiple regression models were developed for prediction of monthly Cx. tarsalis abundance for June, July, and August using 4 years of data collected over 2007-10. The models explained monthly adult mosquito abundance with accuracies ranging from 51-61% in Fort Collins and 57-88% in Loveland-Johnstown. Models derived using landscape-level predictors indicated that adult Cx. tarsalis abundance is negatively correlated with elevation. In this case, low-elevation areas likely more abundantly include habitats for Cx. tarsalis. Model output indicated that the perimeter of larval sites is a significant predictor of Cx. tarsalis abundance at a spatial extent of 500 m in Loveland-Johnstown in all months examined. The contribution of irrigated crops at a spatial extent of 500 m improved model fit in August in both Fort Collins and Loveland-Johnstown. These results emphasize the significance of irrigation and the manual control of water across the landscape to provide viable larval habitats for Cx. tarsalis in the study area. Results from multiple regression models can be applied operationally to identify areas of larval Cx. tarsalis production (irrigated crops lands and standing water) and assign priority in larval treatments to areas with a high density of larval sites at relevant spatial extents around urban locations.

Associations of Health-Risk Behaviors and Health Cognition With Sexual Orientation Among Adolescents in School: Analysis of Pooled Data From Korean Nationwide Survey From 2008 to 2012.

PubMed

Lee, Dong-Yun; Kim, Seo-Hee; Woo, Sook Young; Yoon, Byung-Koo; Choi, DooSeok

2016-05-01

Homosexual adolescents may face significant health disparities. We examined health-risk behaviors and health cognition related to homosexual behavior in a representative sample of adolescents.Data were obtained from 129,900 adolescents between 2008 and 2012 over 5 cycles of the Korean Youth Risk Behavior Survey, a national survey of students in grades 7 to 12. Various health-risk behaviors and aspects of health cognition were compared between homosexual and heterosexual adolescents and analyzed with multiple logistic regression models.Compared with heterosexual adolescents (n = 127,594), homosexual adolescents (n = 2306) were more likely to engage in various health-risk behaviors and to have poor health cognition. In multiple logistic regression analysis, not living with parents, alcohol experience (adjusted odds ratio, 1.50; 95% confidence interval, 1.26-1.78 for males and 1.66; 1.33-2.07 for females), smoking experience (1.80; 1.54-2.10 for males and 3.15; 2.61-3.79 for females), and drug experience (3.65; 2.81-4.80 for males and 3.23; 2.35-4.46 for females) were associated with homosexual behavior. Homosexual adolescents were more likely to use adult internet content (2.82; 2.27-3.50 for males and 7.42; 4.19-13.15 for females), and to be depressed (1.21; 1.03-1.43 for males and 1.32; 1.06-1.64 for females). In addition, suicide ideation (1.51; 1.26-1.81 for males and 1.47; 1.16-1.86 for females) and attempts (1.67; 1.37-2.05 for males and 1.65; 1.34-2.03 for females) were significantly more prevalent among homosexual adolescents.Homosexual adolescents report disparities in various aspects of health-risk behavior and health cognition, including use of multiple substances, adult internet content and inappropriate weight loss methods, suicide ideation and attempts, and depressive mood. These factors should be addressed relevantly to develop specific interventions regarding sexual minorities.

Determinant of factors associated with child health outcomes and service utilization in Ghana: multiple indicator cluster survey conducted in 2011.

PubMed

Dwumoh, Duah; Essuman, Edward Eyipe; Afagbedzi, Seth Kwaku

2014-01-01

The effects of National Health Insurance Scheme in Ghana and its impact on child health outcome and service utilization cannot be underestimated. Despite the tremendous improvement in child health care in Ghana, there are still some challenges in relation to how National health insurance membership, socioeconomic status and other demographic factors impacts on child health outcomes. The study seeks to determine the association between NHIS membership, socio-economic status, geographic location and other relevant background factors, on child health service utilization and outcomes. Secondary data from the Multiple Indicator Cluster Survey conducted in 2011 was used. Multivariate analysis based on Binary Logistic Regression Models and Multiple linear regression techniques was applied to determine factors associated with child health outcomes and service utilization. Collection of best models was based on Hosmer-Lemeshow Goodness-Of-Fit as one criterion of fit and the Akaike Information Criterion. Controlling for confounding effect of socioeconomic status, age of the child, mothers education level and geographic location, the odds of a child developing anemia for children with National Health Insurance Scheme Membership is 65.2% [95% CI: 52.9-80.2] times less than children without National Health Insurance Scheme Membership. The odds of being fully immunized against common childhood illnesses for children with NHIS membership is 2.3[95% CI: 1.4-3.7] times higher than children without National Health Insurance Scheme Membership. There was no association between National Health Insurance Scheme Membership and stunted growth in children. National Health Insurance Scheme Membership was found to be related to child health service utilization (full immunization) of children under five a child's anemia status. Children with NHIS are more likely to be fully immunized against common childhood diseases and are less likely to develop anemia. Stunted growth of children was not associated with National Health Insurance Scheme Membership. Health Education on the registration and the use of the National Health Insurance should be made a national priority to enable the Ministry of Health achieve routine Immunization targets and to reduce to the bearers minimum prevalence of anemia.

Associations of Health-Risk Behaviors and Health Cognition With Sexual Orientation Among Adolescents in School

PubMed Central

Lee, Dong-Yun; Kim, Seo-Hee; Woo, Sook Young; Yoon, Byung-Koo; Choi, DooSeok

2016-01-01

Abstract Homosexual adolescents may face significant health disparities. We examined health-risk behaviors and health cognition related to homosexual behavior in a representative sample of adolescents. Data were obtained from 129,900 adolescents between 2008 and 2012 over 5 cycles of the Korean Youth Risk Behavior Survey, a national survey of students in grades 7 to 12. Various health-risk behaviors and aspects of health cognition were compared between homosexual and heterosexual adolescents and analyzed with multiple logistic regression models. Compared with heterosexual adolescents (n = 127,594), homosexual adolescents (n = 2306) were more likely to engage in various health-risk behaviors and to have poor health cognition. In multiple logistic regression analysis, not living with parents, alcohol experience (adjusted odds ratio, 1.50; 95% confidence interval, 1.26–1.78 for males and 1.66; 1.33–2.07 for females), smoking experience (1.80; 1.54–2.10 for males and 3.15; 2.61–3.79 for females), and drug experience (3.65; 2.81–4.80 for males and 3.23; 2.35–4.46 for females) were associated with homosexual behavior. Homosexual adolescents were more likely to use adult internet content (2.82; 2.27–3.50 for males and 7.42; 4.19–13.15 for females), and to be depressed (1.21; 1.03–1.43 for males and 1.32; 1.06–1.64 for females). In addition, suicide ideation (1.51; 1.26–1.81 for males and 1.47; 1.16–1.86 for females) and attempts (1.67; 1.37–2.05 for males and 1.65; 1.34–2.03 for females) were significantly more prevalent among homosexual adolescents. Homosexual adolescents report disparities in various aspects of health-risk behavior and health cognition, including use of multiple substances, adult internet content and inappropriate weight loss methods, suicide ideation and attempts, and depressive mood. These factors should be addressed relevantly to develop specific interventions regarding sexual minorities. PMID:27227939

Admixture mapping of pelvic organ prolapse in African Americans from the Women’s Health Initiative Hormone Therapy trial

PubMed Central

Hartmann, Katherine E.; Aldrich, Melinda C.; Ward, Renee M.; Wu, Jennifer M.; Park, Amy J.; Graff, Mariaelisa; Qi, Lihong; Nassir, Rami; Wallace, Robert B.; O'Sullivan, Mary J.; North, Kari E.; Velez Edwards, Digna R.; Edwards, Todd L.

2017-01-01

Evidence suggests European American (EA) women have two- to five-fold increased odds of having pelvic organ prolapse (POP) when compared with African American (AA) women. However, the role of genetic ancestry in relation to POP risk is not clear. Here we evaluate the association between genetic ancestry and POP in AA women from the Women’s Health Initiative Hormone Therapy trial. Women with grade 1 or higher classification, and grade 2 or higher classification for uterine prolapse, cystocele or rectocele at baseline or during follow-up were considered to have any POP (N = 805) and moderate/severe POP (N = 156), respectively. Women with at least two pelvic exams with no indication for POP served as controls (N = 344). We performed case-only, and case-control admixture-mapping analyses using multiple logistic regression while adjusting for age, BMI, parity and global ancestry. We evaluated the association between global ancestry and POP using multiple logistic regression. European ancestry at the individual level was not associated with POP risk. Case-only and case-control local ancestry analyses identified two ancestry-specific loci that may be associated with POP. One locus (Chromosome 15q26.2) achieved empirically-estimated statistical significance and was associated with decreased POP odds (considering grade ≥2 POP) with each unit increase in European ancestry (OR: 0.35; 95% CI: 0.30, 0.57; p-value = 1.48x10-5). This region includes RGMA, a potent regulator of the BMP family of genes. The second locus (Chromosome 1q42.1-q42.3) was associated with increased POP odds with each unit increase in European ancestry (Odds ratio [OR]: 1.69; 95% confidence interval [CI]: 1.28, 2.22; p-value = 1.93x10-4). Although this region did not reach statistical significance after considering multiple comparisons, it includes potentially relevant genes including TBCE, and ACTA1. Unique non-overlapping European and African ancestry-specific susceptibility loci may be associated with increased POP risk. PMID:28582460

Gender differences in preferences for psychological treatment, coping strategies, and triggers to help-seeking.

PubMed

Liddon, Louise; Kingerlee, Roger; Barry, John A

2018-03-01

There is some evidence that men and women deal with stress in different ways; for example, a meta-analysis found that women prefer to focus on emotions as a coping strategy more than men do. However, sex differences in preferences for therapy is a subject little explored. A cross-sectional online survey. Participants (115 men and 232 women) were recruited via relevant websites and social media. The survey described therapies and asked participants how much they liked each. Their coping strategies and help-seeking behaviour were assessed too. Survey data were analysed using multiple linear regression. After familywise adjustment of the alpha for multiple testing to p < .0125, and controlling for other relevant variables, men liked support groups more than women did (β = -.163, p < .010), used sex or pornography to cope with stress more than women did (Exp[B] = .280, p < .0002), and thought that there is a lack of male-friendly options more than women did (Exp[B] = .264, p < .002). The majority of participants expressed no preference for the sex of their therapist, but of those who did, men were only slightly more likely to prefer a female therapist whereas women were much more likely to prefer females (p < .0004). Even after familywise adjustment, there were still more significant findings than would be expected by chance (p < .001, two-tailed). Although there are many similarities in the preferences of men and women regarding therapy, our findings support the hypothesis that men and women show statistically significant differences of relevance to clinical psychologists. Men are less inclined than women to seek help for psychological issues This study demonstrates that men and women show significant differences in some aspects of therapy, coping behaviour, and help-seeking It is possible that men would be more inclined to seek help if therapies catered more for men's preferences Practitioners can learn to improve the success of their practice by taking the gender of clients into account. © 2017 The British Psychological Society.

Parental depressive symptoms and childhood cancer: the importance of financial difficulties.

PubMed

Creswell, Paul D; Wisk, Lauren E; Litzelman, Kristin; Allchin, Adelyn; Witt, Whitney P

2014-02-01

Research suggests a relationship between caring for a child with cancer and psychological distress in caregivers. Less evident is the role which financial difficulties might play in this relationship. We sought to determine if caring for a child with cancer was related to clinically relevant depressive symptoms among parents, whether or not financial difficulties mediated this relationship, and if financial difficulties were independently associated with symptoms of depression among parents of children with cancer. Data are from 215 parents of children diagnosed with cancer or brain tumors (n = 75) and a comparison group of parents of healthy children (n = 140). Multiple logistic regression analyses were used to assess the factors associated with reporting clinically relevant depressive symptoms. Caring for a child with cancer was associated with increased odds of clinically relevant depressive symptoms in parents (OR = 4.93; 95 % CI 1.97-12.30), controlling for covariates. The mediating effect of financial burden on this relationship was not statistically significant. However, among parents of children with cancer, negative financial life events increased the likelihood of reporting symptoms of depression (OR = 4.89; 95 % CI 1.26-18.96). Caring for a child with cancer was associated with depressive symptoms for parents. Financial difficulties were the strongest correlate of these symptoms among parents of children with cancer. Our results suggest that it may not only be the burden of caring for the child with cancer but also the associated financial difficulties that contribute to a higher likelihood of depressive symptoms in parents.

Teacher beliefs about the aetiology of individual differences in cognitive ability, and the relevance of behavioural genetics to education.

PubMed

Crosswaite, Madeline; Asbury, Kathryn

2018-04-26

Despite a large body of research that has explored the influence of genetic and environmental factors on educationally relevant traits, few studies have explored teachers' beliefs about, or knowledge of, developments in behavioural genetics related to education. This study aimed to describe the beliefs and knowledge of UK teachers about behavioural genetics and its relevance to education, and to test for differences between groups of teachers based on factors including years of experience and age of children taught. Data were gathered from n = 402 teachers from a representative sample of UK schools. Teachers from primary and secondary schools, and from across the state and independent sectors, were recruited. An online questionnaire was used to gather demographic data (gender, age, years of experience, age of children taught, and state vs. independent) and also data on beliefs about the relative influence of nature and nurture on cognitive ability; knowledge of behavioural genetics; openness to genetic research in education; and mindset. Data were analysed using descriptive statistics, ANOVA, correlations, and multiple regression. Teachers perceived genetic and environmental factors as equally important influences on cognitive ability and tended towards a growth mindset. Knowledge about behavioural genetics was low, but openness to learning more about genetics was high. Statistically significant differences were observed between groups based on age of children taught (openness higher among primary teachers) and state versus independent (more growth-minded in state sector). Although teachers have a limited knowledge of behavioural genetics, they are keen to learn more. © 2018 The British Psychological Society.

Influence of efforts of employer and employee on return-to-work process and outcomes.

PubMed

Muijzer, A; Groothoff, J W; Geertzen, J H B; Brouwer, S

2011-12-01

Research on disability and RTW outcome has led to significant advances in understanding these outcomes, however, limited studies focus on measuring the RTW process. After a prolonged period of sickness absence, the assessment of the RTW process by investigating RTW Effort Sufficiency (RTW-ES) is essential. However, little is known about factors influencing RTW-ES. Also, the correspondence in factors determining RTW-ES and RTW is unknown. The purpose of this study was to investigate 1) the strength and relevance of factors related to RTW-ES and RTW (no/partial RTW), and 2) the comparability of factors associated with RTW-ES and with RTW. During 4 months, all assessments of RTW-ES and RTW (no/partial RTW) among employees applying for disability benefits after 2 years of sickness absence, performed by labor experts at 3 Dutch Social Insurance Institute locations, were investigated by means of a questionnaire. Questionnaires concerning 415 cases were available. Using multiple logistic regression analysis, the only factor related to RTW-ES is a good employer-employee relationship. Factors related to RTW (no/partial RTW) were found to be high education, no previous periods of complete disability and a good employer-employee relationship. Different factors are relevant to RTW-ES and RTW, but the employer-employee relationship is relevant for both. Considering the importance of the assessment of RTW-ES after a prolonged period of sickness absence among employees who are not fully disabled, this knowledge is essential for the assessment of RTW-ES and the RTW process itself.

Vismodegib hedgehog-signaling inhibition and treatment of basal cell carcinomas as well as keratocystic odontogenic tumors in Gorlin syndrome.

PubMed

Booms, Patrick; Harth, Marc; Sader, Robert; Ghanaati, Shahram

2015-01-01

Vismodegib hedgehog signaling inhibition treatment has potential for reducing the burden of multiple skin basal cell carcinomas and jaw keratocystic odontogenic tumors. They are major criteria for the diagnosis of Gorlin syndrome, also called nevoid basal cell carcinoma syndrome. Clinical features of Gorlin syndrome are reported, and the relevance of hedgehog signaling pathway inhibition by oral vismodegib for maxillofacial surgeons is highlighted. In summary, progressed basal cell carcinoma lesions are virtually inoperable. Keratocystic odontogenic tumors have an aggressive behavior including rapid growth and extension into adjacent tissues. Interestingly, nearly complete regression of multiple Gorlin syndrome-associated keratocystic odontogenic tumors following treatment with vismodegib. Due to radio-hypersensitivity in Gorlin syndrome, avoidance of treatment by radiotherapy is strongly recommended for all affected individuals. Vismodegib can help in those instances where radiation is contra-indicated, or the lesions are inoperable. The effect of vismodegib on basal cell carcinomas was associated with a significant decrease in hedgehog-signaling and tumor proliferation. Vismodegib, a new and approved drug for the treatment of advanced basal cell carcinoma, is a specific oncogene inhibitor. It also seems to be effective for treatment of keratocystic odontogenic tumors and basal cell carcinomas in Gorlin syndrome, rendering the surgical resections less challenging.

Psychiatric disorders and cardiac anxiety in exercising and sedentary coronary artery disease patients: a case-control study.

PubMed

Sardinha, A; Araújo, C G S; Nardi, A E

2012-12-01

Regular physical exercise has been shown to favorably influence mood and anxiety; however, there are few studies regarding psychiatric aspects of physically active patients with coronary artery disease (CAD). The objective of the present study was to compare the prevalence of psychiatric disorders and cardiac anxiety in sedentary and exercising CAD patients. A total sample of 119 CAD patients (74 men) were enrolled in a case-control study. The subjects were interviewed to identify psychiatric disorders and responded to the Cardiac Anxiety Questionnaire. In the exercise group (N = 60), there was a lower prevalence (45 vs 81%; P < 0.001) of at least one psychiatric diagnosis, as well as multiple comorbidities, when compared to the sedentary group (N = 59). Considering the Cardiac Anxiety Questionnaire, sedentary patients presented higher scores compared to exercisers (mean ± SEM = 55.8 ± 1.9 vs 37.3 ± 1.6; P < 0.001). In a regression model, to be attending a medically supervised exercise program presented a relevant potential for a 35% reduction in cardiac anxiety. CAD patients regularly attending an exercise program presented less current psychiatric diagnoses and multiple mental-related comorbidities and lower scores of cardiac anxiety. These salutary mental effects add to the already known health benefits of exercise for CAD patients.

Psychiatric disorders and cardiac anxiety in exercising and sedentary coronary artery disease patients: a case-control study

PubMed Central

Sardinha, A.; Araújo, C.G.S.; Nardi, A.E.

2012-01-01

Regular physical exercise has been shown to favorably influence mood and anxiety; however, there are few studies regarding psychiatric aspects of physically active patients with coronary artery disease (CAD). The objective of the present study was to compare the prevalence of psychiatric disorders and cardiac anxiety in sedentary and exercising CAD patients. A total sample of 119 CAD patients (74 men) were enrolled in a case-control study. The subjects were interviewed to identify psychiatric disorders and responded to the Cardiac Anxiety Questionnaire. In the exercise group (N = 60), there was a lower prevalence (45 vs 81%; P < 0.001) of at least one psychiatric diagnosis, as well as multiple comorbidities, when compared to the sedentary group (N = 59). Considering the Cardiac Anxiety Questionnaire, sedentary patients presented higher scores compared to exercisers (mean ± SEM = 55.8 ± 1.9 vs 37.3 ± 1.6; P < 0.001). In a regression model, to be attending a medically supervised exercise program presented a relevant potential for a 35% reduction in cardiac anxiety. CAD patients regularly attending an exercise program presented less current psychiatric diagnoses and multiple mental-related comorbidities and lower scores of cardiac anxiety. These salutary mental effects add to the already known health benefits of exercise for CAD patients. PMID:23011407

Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors

PubMed Central

Basra, Komal; Fabian, M. Patricia; Holberger, Raymond R.; French, Robert

2017-01-01

Many health risk factors are intervention targets within communities, but information regarding high-risk subpopulations is rarely available at a geographic resolution that is relevant for community-scale interventions. Researchers and community partners in New Bedford, Massachusetts (USA) collaboratively identified high-priority behaviors and health outcomes of interest available in the Behavioral Risk Factor Surveillance System (BRFSS). We developed multivariable regression models from the BRFSS explaining variability in exercise, fruit and vegetable consumption, body mass index, and diabetes prevalence as a function of demographic and behavioral characteristics, and linked these models with population microdata developed using spatial microsimulation to characterize high-risk populations and locations. Individuals with lower income and educational attainment had lower rates of multiple health-promoting behaviors (e.g., fruit and vegetable consumption and exercise) and higher rates of self-reported diabetes. Our models in combination with the simulated population microdata identified census tracts with an elevated percentage of high-risk subpopulations, information community partners can use to prioritize funding and intervention programs. Multi-stressor modeling using data from public databases and microsimulation methods for characterizing high-resolution spatial patterns of population attributes, coupled with strong community partner engagement, can provide significant insight for intervention. Our methodology is transferrable to other communities. PMID:28684710

Watershed Planning within a Quantitative Scenario Analysis Framework.

PubMed

Merriam, Eric R; Petty, J Todd; Strager, Michael P

2016-07-24

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall short as a result of an inability to quantify and predict complex cumulative effects of current and future land use scenarios at relevant spatial scales. The goal of this manuscript is to provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework. Sites are first selected for inclusion within the watershed assessment by identifying sites that fall along independent gradients and combinations of known stressors. Field and laboratory techniques are then used to obtain data on the physical, chemical, and biological effects of multiple land use activities. Multiple linear regression analysis is then used to produce landscape-based cumulative effects models for predicting aquatic conditions. Lastly, methods for incorporating cumulative effects models within a scenario analysis framework for guiding management and regulatory decisions (e.g., permitting and mitigation) within actively developing watersheds are discussed and demonstrated for 2 sub-watersheds within the mountaintop mining region of central Appalachia. The watershed assessment and management approach provided herein enables resource managers to facilitate economic and development activity while protecting aquatic resources and producing opportunity for net ecological benefits through targeted remediation.

An operational ensemble prediction system for catchment rainfall over eastern Africa spanning multiple temporal and spatial scales

NASA Astrophysics Data System (ADS)

Riddle, E. E.; Hopson, T. M.; Gebremichael, M.; Boehnert, J.; Broman, D.; Sampson, K. M.; Rostkier-Edelstein, D.; Collins, D. C.; Harshadeep, N. R.; Burke, E.; Havens, K.

2017-12-01

While it is not yet certain how precipitation patterns will change over Africa in the future, it is clear that effectively managing the available water resources is going to be crucial in order to mitigate the effects of water shortages and floods that are likely to occur in a changing climate. One component of effective water management is the availability of state-of-the-art and easy to use rainfall forecasts across multiple spatial and temporal scales. We present a web-based system for displaying and disseminating ensemble forecast and observed precipitation data over central and eastern Africa. The system provides multi-model rainfall forecasts integrated to relevant hydrological catchments for timescales ranging from one day to three months. A zoom-in features is available to access high resolution forecasts for small-scale catchments. Time series plots and data downloads with forecasts, recent rainfall observations and climatological data are available by clicking on individual catchments. The forecasts are calibrated using a quantile regression technique and an optimal multi-model forecast is provided at each timescale. The forecast skill at the various spatial and temporal scales will discussed, as will current applications of this tool for managing water resources in Sudan and optimizing hydropower operations in Ethiopia and Tanzania.

The importance of mean time in therapeutic range for complication rates in warfarin therapy of patients with atrial fibrillation: A systematic review and meta-regression analysis.

PubMed

Vestergaard, Anne Sig; Skjøth, Flemming; Larsen, Torben Bjerregaard; Ehlers, Lars Holger

2017-01-01

\\Anticoagulation is used for stroke prophylaxis in non-valvular atrial fibrillation, amongst other by use of the vitamin K antagonist, warfarin. Quality in warfarin therapy is often summarized by the time patients spend within the therapeutic range (percent time in therapeutic range, TTR). The correlation between TTR and the occurrence of complications during warfarin therapy has been established, but the influence of patient characteristics in that respect remains undetermined. The objective of the present papers was to examine the association between mean TTR and complication rates with adjustment for differences in relevant patient cohort characteristics. A systematic literature search was conducted in MEDLINE and Embase (2005-2015) to identify eligible studies reporting on use of warfarin therapy by patients with non-valvular atrial fibrillation and the occurrence of hemorrhage and thromboembolism. Both randomized controlled trials and observational cohort studies were included. The association between the reported mean TTR and major bleeding and stroke/systemic embolism was analyzed by random-effects meta-regression with and without adjustment for relevant clinical cohort characteristics. In the adjusted meta-regressions, the impact of mean TTR on the occurrence of hemorrhage was adjusted for the mean age and the proportion of populations with prior stroke or transient ischemic attack. In the adjusted analyses on thromboembolism, the proportion of females was, furthermore, included. Of 2169 papers, 35 papers met pre-specified inclusion criteria, holding relevant information on 31 patient cohorts. In univariable meta-regression, increasing mean TTR was significantly associated with a decreased rate of both major bleeding and stroke/systemic embolism. However, after adjustment mean TTR was no longer significantly associated with stroke/systemic embolism. The proportion of residual variance composed by between-study heterogeneity was substantial for all analyses. Although higher mean TTR in warfarin therapy was associated with lower complication rates in atrial fibrillation, the strength of the association was decreased when adjusting for differences in relevant clinical characteristics of the patient cohorts. This study suggests that mainly the safety of warfarin therapy increases with higher mean TTR, whereas effectiveness appears not to be substantially improved. Due to the limitations immanent in the meta-regression methods, the results of the present study should be interpreted with caution. Further research on the association between the quality of warfarin therapy and risk of complications is warranted with adjustment for clinically relevant characteristics.

Buying a Better Air Force

DTIC Science & Technology

2006-03-01

identify if an explanatory variable may have been omitted due to model misspecification ( Ramsey , 1979). The RESET test resulted in failure to...Prob > F 0.0094 This model was also regressed using Huber-White estimators. Again, the Ramsey RESET test was done to ensure relevant...Aircraft. Annapolis, MD: Naval Institute Press, 2004. Ramsey , J. B. “ Tests for Specification Errors in Classical Least-Squares Regression Analysis

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Detection of epistatic effects with logic regression and a classical linear regression model.

PubMed

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Regression in autistic spectrum disorders.

PubMed

Stefanatos, Gerry A

2008-12-01

A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

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.

Interpret with caution: multicollinearity in multiple regression of cognitive data.

PubMed

Morrison, Catriona M

2003-08-01

Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

STATLIB: NSWC Library of Statistical Programs and Subroutines

DTIC Science & Technology

1989-08-01

Uncorrelated Weighted Polynomial Regression 41 .WEPORC Correlated Weighted Polynomial Regression 45 MROP Multiple Regression Using Orthogonal Polynomials ...could not and should not be con- NSWC TR 89-97 verted to the new general purpose computer (the current CDC 995). Some were designed tu compute...personal computers. They are referred to as SPSSPC+, BMDPC, and SASPC and in general are less comprehensive than their mainframe counterparts. The basic

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

PubMed

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

The role of exercise in modifying outcomes for people with multiple sclerosis: a randomized trial

PubMed Central

2013-01-01

Background Despite the commonly known benefits of exercise and physical activity evidence shows that persons Multiple Sclerosis (MS) are relatively inactive yet physical activity may be even more important in a population facing functional deterioration. No exercise is effective if it is not done and people with MS face unique barriers to exercise engagement which need to be overcome. We have developed and pilot tested a Multiple Sclerosis Tailored Exercise Program (MSTEP) and it is ready to be tested against general guidelines for superiority and ultimately for its impact on MS relevant outcomes. The primary research question is to what extent does an MS Tailored Exercise Program (MSTEP) result in greater improvements in exercise capacity and related outcomes over a one year period in comparison to a program based on general guidelines for exercise among people with MS who are sedentary and wish to engage in exercise as part of MS self-management. Methods/Design The proposed study is an assessor-blind, parallel-group, randomized controlled trial (RCT). The duration of the intervention will be one year with follow-up to year two. The targeted outcomes are exercise capacity, functional ambulation, strength, and components of quality of life including frequency and intensity of fatigue symptoms, mood, global physical function, health perception, and objective measures of activity level. Logistic regression will be used to test the main hypothesis related to the superiority of the MSTEP program based on a greater proportion of people making a clinically relevant gain in exercise capacity at 1 year and at 2 years, using an intention-to-treat approach. Sample size will be 240 (120 per group). Discussion The MS community is clearly looking for interventions to help alleviate the disabling sequelae of MS and promote health. Exercise is a well-known intervention which has known benefits to all, yet few exercise regularly. For people with MS, the role of exercise in MS management needs to be rigorously assessed to inform people as to how best to use exercise to reduce disability and promote health. Trial registration Clinical Trials.gov: NCT01611987 PMID:23809312

Effects of soil properties on copper toxicity to earthworm Eisenia fetida in 15 Chinese soils.

PubMed

Duan, Xiongwei; Xu, Meng; Zhou, Youya; Yan, Zengguang; Du, Yanli; Zhang, Lu; Zhang, Chaoyan; Bai, Liping; Nie, Jing; Chen, Guikui; Li, Fasheng

2016-02-01

The bioavailability and toxicity of metals in soil are influenced by a variety of soil properties, and this principle should be recognized in establishing soil environmental quality criteria. In the present study, the uptake and toxicity of Cu to the earthworm Eisenia fetida in 15 Chinese soils with various soil properties were investigated, and regression models for predicting Cu toxicity across soils were developed. The results showed that earthworm survival and body weight change were less sensitive to Cu than earthworm cocoon production. The soil Cu-based median effective concentrations (EC50s) for earthworm cocoon production varied from 27.7 to 383.7 mg kg(-1) among 15 Chinese soils, representing approximately 14-fold variation. Soil cation exchange capacity and organic carbon content were identified as key factors controlling Cu toxicity to earthworm cocoon production, and simple and multiple regression models were developed for predicting Cu toxicity across soils. Tissue Cu-based EC50s for earthworm cocoon production were also calculated and varied from 15.5 to 62.5 mg kg(-1) (4-fold variation). Compared to the soil Cu-based EC50s for cocoon production, the tissue Cu-based EC50s had less variation among soils, indicating that metals in tissue were more relevant to toxicity than metals in soil and hence represented better measurements of bioavailability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Factors influencing the hospitalization costs of patients with type 2 diabetes.

PubMed

Cao, Ping; Wang, Kaixiu; Zhang, Hua; Zhao, Rongzhi; Li, Chenglong

2015-03-01

This study aims to research the factors influencing the hospitalization costs of patients with type 2 diabetes, so as to provide some references for reducing their economic burden. Based on the Hospital Information System of a 3A grade hospital in China, we analyzed 2970 cases with type 2 diabetes during 2005-2012. Both the number of inpatients and the hospitalization costs had increased in the study period. Using multiple linear regression analysis, we found that patients in Urban Employee Basic Medical Insurance had higher costs than those in New Rural Cooperative Medical Scheme. We also found hospitalization costs to be higher in male patients and older patients, patients who stayed more days at hospital and who had surgeries, patients who had at least 1 complication, and patients whose admission status was emergency. After standardizing the regression coefficients, we found that the hospital stay, the forms of payment, and presence of complications were the first 3 factors influencing hospitalization costs in our study. In conclusion, the hospitalization costs of patients with type 2 diabetes could be influenced by age, gender, forms of payment, hospital stay, admission status, complications, and surgery. Medical workers in the studied region should take actions to reduce the duration of hospital stay for diabetic patients and prevent relevant complications. What is more, medical insurance needs further improvement. © 2015 APJPH.

Developing a stroke severity index based on administrative data was feasible using data mining techniques.

PubMed

Sung, Sheng-Feng; Hsieh, Cheng-Yang; Kao Yang, Yea-Huei; Lin, Huey-Juan; Chen, Chih-Hung; Chen, Yu-Wei; Hu, Ya-Han

2015-11-01

Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data. We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database. We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models. The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

On the method of Ermakov and Zolotukhin for multiple integration

NASA Technical Reports Server (NTRS)

Cranley, R.; Patterson, T. N. L.

1971-01-01

By introducing the idea of pseudo-implementation, a practical assessment of the method for multiple integration is made. The performance of the method is found to be unimpressive in comparison with a recent regression method.

Constructing Culturally Relevant Pedagogy in Chinese Heritage Language Classrooms: A Multiple-Case Study

ERIC Educational Resources Information Center

Wu, Hsu-Pai

2011-01-01

Culturally relevant pedagogy uses cultural references to develop students' knowledge and identities thereby empowering them academically, socially and politically. This article examined how four Chinese heritage languages teachers constructed culturally relevant pedagogy in their language instructions. Qualitative cross-case analysis indicated…

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.

1982-01-01

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Influence of anthropometric parameters on ultrasound measurements of Os calcis.

PubMed

Hans, D; Schott, A M; Arlot, M E; Sornay, E; Delmas, P D; Meunier, P J

1995-01-01

Few data have been published concerning the influence of height, weight and body mass index (BMI) on broadband ultrasound attenuation (BUA), speed of sound (SOS) and Lunar "stiffness" index, and always in small population samples. The first ain of the present cross-sectional study was to determine whether anthropometric factors have a significant influence on ultrasound measurements. The second objective was to establish whether these parameters have real effect on whether their influence is due only to measurement errors. We measured, in 271 healthy French women (mean age 77 +/- 11 years; range 31-97 years), the following parameters: age, height, weight, lean and fat body mass, heel width, foot length, knee height and external malleolus (HEM). Simple linear regression analyses between ultrasound and anthropometric parameters were performed. Age, height, and heel width were significant predictors of SOS; age, height, weight, foot length, heel width, HEM, fat mass and lean mass were significant predictors of BUA; age, height, weight, heel width, HEM, fat mass and lean mass were significant predictors of stiffness. In the multiple regression analysis, once the analysis had been adjusted for age, only heel width was a significant predictor for SOS (p = 0.0007), weight for BUA (p = 0.0001), and weight (p = 0.0001) and heel width (p = 0.004) for the stiffness index. Besides their statistical meaning, the regression coefficients have a more clinically relevant interpretation which is developed in the text. These results confirm the influence of anthropometric factors on the ultrasonic parameter values, because BUA and SOS were in part dependent on heel width and weight. The influence of the position of the transducer on the calcaneus should be taken into account to optimize the methods of measurement using ultrasound.

Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

NASA Astrophysics Data System (ADS)

Walawender, Ewelina; Walawender, Jakub P.; Ustrnul, Zbigniew

2017-02-01

The main purpose of the study is to introduce methods for mapping the spatial distribution of the occurrence of selected atmospheric phenomena (thunderstorms, fog, glaze and rime) over Poland from 1966 to 2010 (45 years). Limited in situ observations as well the discontinuous and location-dependent nature of these phenomena make traditional interpolation inappropriate. Spatially continuous maps were created with the use of geospatial predictive modelling techniques. For each given phenomenon, an algorithm identifying its favourable meteorological and environmental conditions was created on the basis of observations recorded at 61 weather stations in Poland. Annual frequency maps presenting the probability of a day with a thunderstorm, fog, glaze or rime were created with the use of a modelled, gridded dataset by implementing predefined algorithms. Relevant explanatory variables were derived from NCEP/NCAR reanalysis and downscaled with the use of a Regional Climate Model. The resulting maps of favourable meteorological conditions were found to be valuable and representative on the country scale but at different correlation ( r) strength against in situ data (from r = 0.84 for thunderstorms to r = 0.15 for fog). A weak correlation between gridded estimates of fog occurrence and observations data indicated the very local nature of this phenomenon. For this reason, additional environmental predictors of fog occurrence were also examined. Topographic parameters derived from the SRTM elevation model and reclassified CORINE Land Cover data were used as the external, explanatory variables for the multiple linear regression kriging used to obtain the final map. The regression model explained 89 % of annual frequency of fog variability in the study area. Regression residuals were interpolated via simple kriging.

Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement.

PubMed

Teoh, Shao Thing; Kitamura, Miki; Nakayama, Yasumune; Putri, Sastia; Mukai, Yukio; Fukusaki, Eiichiro

2016-08-01

In recent years, the advent of high-throughput omics technology has made possible a new class of strain engineering approaches, based on identification of possible gene targets for phenotype improvement from omic-level comparison of different strains or growth conditions. Metabolomics, with its focus on the omic level closest to the phenotype, lends itself naturally to this semi-rational methodology. When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype. However, linear modeling techniques such as PLS require a consistent metabolite-phenotype trend across the samples, which may not be the case when outliers or multiple conflicting trends are present in the data. To address this, we proposed a data-mining strategy that utilizes random sample consensus (RANSAC) to select subsets of samples with consistent trends for construction of better regression models. By applying a combination of RANSAC and PLS (RANSAC-PLS) to a dataset from a previous study (gas chromatography/mass spectrometry metabolomics data and 1-butanol tolerance of 19 yeast mutant strains), new metabolites were indicated to be correlated with tolerance within certain subsets of the samples. The relevance of these metabolites to 1-butanol tolerance were then validated from single-deletion strains of corresponding metabolic genes. The results showed that RANSAC-PLS is a promising strategy to identify unique metabolites that provide additional hints for phenotype improvement, which could not be detected by traditional PLS modeling using the entire dataset. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Social cognition and functional capacity in bipolar disorder and schizophrenia.

PubMed

Thaler, Nicholas S; Sutton, Griffin P; Allen, Daniel N

2014-12-15

Social cognition is a functionally relevant predictor of capacity in schizophrenia (SZ), though research concerning its value for bipolar disorder (BD) is limited. The current investigation examined the relationship between two social cognitive factors and functional capacity in bipolar disorder. This study included 48 individuals with bipolar disorder (24 with psychotic features) and 30 patients with schizophrenia. Multiple regression controlling for estimated IQ scores was used to assess the predictive value of social cognitive factors on the UCSD Performance-Based Functional Skills Assessment (UPSA). Results found that for the bipolar with psychosis and schizophrenia groups, the social/emotion processing factor predicted the UPSA. The theory of mind factor only predicted the UPSA for the schizophrenia group.. Findings support the clinical utility of evaluating emotion processing in individuals with a history of psychosis. For BD, theory of mind may be better explained by a generalized cognitive deficit. In contrast, social/emotion processing may be linked to distinct neurobiological processes associated with psychosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Functional Additive Mixed Models

PubMed Central

Scheipl, Fabian; Staicu, Ana-Maria; Greven, Sonja

2014-01-01

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach. PMID:26347592

Methodological quality and reporting of systematic reviews in hand and wrist pathology.

PubMed

Wasiak, J; Shen, A Y; Ware, R; O'Donohoe, T J; Faggion, C M

2017-10-01

The objective of this study was to assess methodological and reporting quality of systematic reviews in hand and wrist pathology. MEDLINE, EMBASE and Cochrane Library were searched from inception to November 2016 for relevant studies. Reporting quality was evaluated using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and methodological quality using a measurement tool to assess systematic reviews, the Assessment of Multiple Systematic Reviews (AMSTAR). Descriptive statistics and linear regression were used to identify features associated with improved methodological quality. A total of 91 studies were included in the analysis. Most reviews inadequately reported PRISMA items regarding study protocol, search strategy and bias and AMSTAR items regarding protocol, publication bias and funding. Systematic reviews published in a plastics journal, or which included more authors, were associated with higher AMSTAR scores. A large proportion of systematic reviews within hand and wrist pathology literature score poorly with validated methodological assessment tools, which may affect the reliability of their conclusions. I.

Environmental Volunteering and Health Outcomes over a 20-Year Period

PubMed Central

Pillemer, Karl; Fuller-Rowell, Thomas E.; Reid, M. C.; Wells, Nancy M.

2010-01-01

Purpose: This study tested the hypothesis that volunteering in environmental organizations in midlife is associated with greater physical activity and improved mental and physical health over a 20-year period.  Design and Methods: The study used data from two waves (1974 and 1994) of the Alameda County Study, a longitudinal study of health and mortality that has followed a cohort of 6,928 adults since 1965. Using logistic and multiple regression models, we examined the prospective association between environmental and other volunteerism and three outcomes (physical activity, self-reported health, and depression), with 1974 volunteerism predicting 1994 outcomes, controlling for a number of relevant covariates.  Results: Midlife environmental volunteering was significantly associated with physical activity, self-reported health, and depressive symptoms.  Implications: This population-based study offers the first epidemiological evidence for a significant positive relationship between environmental volunteering and health and well-being outcomes. Further research, including intervention studies, is needed to confirm and shed additional light on these initial findings. PMID:20172902

Sexual risk-taking and subcortical brain volume in adolescence.

PubMed

Feldstein Ewing, Sarah W; Hudson, Karen A; Caouette, Justin; Mayer, Andrew R; Thayer, Rachel E; Ryman, Sephira G; Bryan, Angela D

2018-04-19

The developmental period of adolescence marks the initiation of new socioemotional and physical behaviors, including sexual intercourse. However, little is known about neurodevelopmental influences on adolescent sexual decision-making. We sought to determine how subcortical brain volume correlated with condom use, and whether those associations differed by gender and pubertal development. We used FreeSurfer to extract subcortical volume among N = 169 sexually experienced youth (mean age 16.07 years; 31.95% female). We conducted multiple linear regressions to examine the relationship between frequency of condom use and subcortical volume, and whether these associations would be moderated by gender and pubertal development. We found that the relationship between brain volume and condom use was better accounted for by pubertal development than by gender, and moderated the association between limbic brain volume and condom use. No significant relationships were observed in reward areas (e.g., nucleus accumbens) or prefrontal cortical control areas. These data highlight the potential relevance of subcortical socioemotional processing structures in adolescents' sexual decision-making.

Aging and Work Ability: The Moderating Role of Job and Personal Resources

PubMed Central

Converso, Daniela; Sottimano, Ilaria; Guidetti, Gloria; Loera, Barbara; Cortini, Michela; Viotti, Sara

2018-01-01

Objective: Demographic changes involving western countries and later retirements due to the recent pension reforms induce a gradual aging of the workforce. This imply an increasing number of workers with health problems and a decreasing of ability to work. In this direction, the present study aims at examining the role of job and personal resources between age and work ability within nurses. Method: The study was cross-sectional and not randomized; data were collected by a self-report questionnaire during a multi-center survey conducted in two Italian hospitals in 2016. In this way, 333 nurses were reached. Results: Multiple linear regression showed that age is significantly and negatively associated to work ability, and that job resources (e.g., decision authority and meaning of work) and personal resources (e.g., hope and resilience) moderate the relationship between age and work ability. Discussion: These results highlight that investing in work and personal resources to support WA is even more relevant for those professions where high physical effort is required. PMID:29367848

What Makes Super‐Aged Nations Happier? Exploring Critical Factors of Happiness Among Middle‐Aged Men and Women in Japan

PubMed Central

Tamiya, Nanako; Kawachi, Nobuyuki; Miyairi, Maya

2018-01-01

This study aimed to examine multiple factors associated with happiness from the perspective of gender difference among a middle‐aged Japanese population. A total of 865 participants (male = 344, female = 521) aged 40–64 years were divided into two groups (high and low) by their self‐reported level of happiness. Logistic regression analysis by gender was carried out. In men, high levels of happiness were significantly correlated with living with spouse, occupation, enough sleep, leading a normal life, and regular checkups; while low levels of happiness were significantly correlated with smoking and having two or more diseases. In women, low levels of happiness were significantly correlated with caring for a family member. Our data suggested that the factors relevant to happiness levels might vary between men and women among middle‐aged people in Japan. To increase the nation's level of happiness, the Japanese government must implement extended social services and policymaking, to alleviate caregivers’ burdens, especially among Japanese women. PMID:29610701

Parametric Analysis to Study the Influence of Aerogel-Based Renders' Components on Thermal and Mechanical Performance.

PubMed

Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

2016-05-04

Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study's objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect.

Ex-vivo UV autofluorescence imaging and fluorescence spectroscopy of atherosclerotic pathology in human aorta

NASA Astrophysics Data System (ADS)

Lewis, William; Williams, Maura; Franco, Walfre

2017-02-01

The aim of our study was to identify fluorescence excitation-emission pairs correlated with atherosclerotic pathology in ex-vivo human aorta. Wide-field images of atherosclerotic human aorta were captured using UV and visible excitation and emission wavelength pairs of several known fluorophores to investigate correspondence with gross pathologic features. Fluorescence spectroscopy and histology were performed on 21 aortic samples. A matrix of Pearson correlation coefficients were determined for the relationship between relevant histologic features and the intensity of emission for 427 wavelength pairs. A multiple linear regression analysis indicated that elastin (370/460 nm) and tryptophan (290/340 nm) fluorescence predicted 58% of the variance in intima thickness (R-squared = 0.588, F(2,18) = 12.8, p=.0003), and 48% of the variance in media thickness (R-squared = 0.483, F(2,18) = 8.42, p=.002), suggesting that endogenous fluorescence intensity at these wavelengths can be utilized for improved pathologic characterization of atherosclerotic plaques.

Support, Inclusion, and Special Education Teachers' Attitudes toward the Education of Students with Autism Spectrum Disorders

PubMed Central

Rodríguez, Isabel R.; Saldaña, David; Moreno, F. Javier

2012-01-01

This study is aimed at assessing special education teachers' attitudes toward teaching pupils with autism spectrum disorders (ASDs) and at determining the role of variables associated with a positive attitude towards the children and their education. Sixty-nine special education teachers were interviewed. The interview included two multiple-choice Likert-type questionnaires, one about teachers' attitude, and another about teachers' perceived needs in relation to the specific education of the pupil with ASD. The study shows a positive view of teachers' expectations regarding the education of pupils with ASD. A direct logistic regression analysis was performed testing for experience with the child, school relationship with an ASD network and type of school (mainstream or special) as potential predictors. Although all three variables are useful in predicting special education teachers' attitudes, the most relevant was the relationship with an ASD network. Need for information and social support are the relatively highest needs expressed by teachers. PMID:22934171

Development of a pharmacogenetic-guided warfarin dosing algorithm for Puerto Rican patients

PubMed Central

Ramos, Alga S; Seip, Richard L; Rivera-Miranda, Giselle; Felici-Giovanini, Marcos E; Garcia-Berdecia, Rafael; Alejandro-Cowan, Yirelia; Kocherla, Mohan; Cruz, Iadelisse; Feliu, Juan F; Cadilla, Carmen L; Renta, Jessica Y; Gorowski, Krystyna; Vergara, Cunegundo; Ruaño, Gualberto; Duconge, Jorge

2012-01-01

Aim This study was aimed at developing a pharmacogenetic-driven warfarin-dosing algorithm in 163 admixed Puerto Rican patients on stable warfarin therapy. Patients & methods A multiple linear-regression analysis was performed using log-transformed effective warfarin dose as the dependent variable, and combining CYP2C9 and VKORC1 genotyping with other relevant nongenetic clinical and demographic factors as independent predictors. Results The model explained more than two-thirds of the observed variance in the warfarin dose among Puerto Ricans, and also produced significantly better ‘ideal dose’ estimates than two pharmacogenetic models and clinical algorithms published previously, with the greatest benefit seen in patients ultimately requiring <7 mg/day. We also assessed the clinical validity of the model using an independent validation cohort of 55 Puerto Rican patients from Hartford, CT, USA (R2 = 51%). Conclusion Our findings provide the basis for planning prospective pharmacogenetic studies to demonstrate the clinical utility of genotyping warfarin-treated Puerto Rican patients. PMID:23215886

Functional Additive Mixed Models.

PubMed

Scheipl, Fabian; Staicu, Ana-Maria; Greven, Sonja

2015-04-01

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.

Positive Attitudes towards Technologies and facets of Well-being in Older Adults.

PubMed

Zambianchi, Manuela; Carelli, Maria Grazia

2018-03-01

The current study investigates the relevance of positive attitudes toward Internet technologies for psychological well-being and social well-being in old age. A sample of 245 elderly people ( Mean age = 70; SD =9.1) filled in the Psychological Well-Being Questionnaire, the Social Well-Being Questionnaire, and Attitudes Toward Technologies Questionnaire (ATTQ). Favorable attitudes toward Internet technologies showed positive correlations with overall social well-being and all its components with the exception of social acceptance. Positive correlations with overall psychological well-being and two of its components, namely, personal growth and purpose in life, were also found. Two hierarchical multiple regression models underscored that positive attitudes toward Internet technologies constitute the most important predictor of social well-being, and it appears to be a significant predictor for psychological well-being as well. Results are discussed and integrated into the Positive Technology theoretical framework that sustains the value of technological resources for improving the quality of personal experience and well-being.

Acculturative stress and depression in an elderly Arabic sample.

PubMed

Wrobel, Nancy Howells; Farrag, Mohamed F; Hymes, Robert W

2009-09-01

Acculturative stress and relevant demographic variables, including immigration status, English skills, level of education, age, gender, country of origin, and years since immigration to the U. S. are examined along with their relationship to depressive symptoms. The 200 Arab-American and recent Arab immigrant participants ranged from age 60-92 and represented eight countries of origin. Most had limited fluency in English. Arabic versions of the Multi-dimensional Acculturative Stress Inventory (MASI) and Geriatric Depression Scale were administered. MASI and GDS results indicated greater degrees of acculturative stress and depression for those with a refugee or temporary resident status. More recent entry into the U.S. also predicted greater stress, while greater levels of education and English skills predicted lower levels of stress and depression. Composite stress levels and the nature of stress varied by country of origin. Although demographic variables were predictive of depression when examined separately, multiple regression analyses revealed that perceived acculturative stress, particularly pressure to learn English, provided a notable increment in prediction of depression over the demographic variables.

Negotiating Multicollinearity with Spike-and-Slab Priors

PubMed Central

Ročková, Veronika

2014-01-01

In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout. PMID:25419004

Emotional connotations of words related to authority and community.

PubMed

Schauenburg, Gesche; Ambrasat, Jens; Schröder, Tobias; von Scheve, Christian; Conrad, Markus

2015-09-01

We present a database of 858 German words from the semantic fields of authority and community, which represent core dimensions of human sociality. The words were selected on the basis of co-occurrence profiles of representative keywords for these semantic fields. All words were rated along five dimensions, each measured by a bipolar semantic-differential scale: Besides the classic dimensions of affective meaning (valence, arousal, and potency), we collected ratings of authority and community with newly developed scales. The results from cluster, correlational, and multiple regression analyses on the rating data suggest a robust negativity bias for authority valuation among German raters recruited via university mailing lists, whereas community ratings appear to be rather unrelated to the well-established affective dimensions. Furthermore, our data involve a strong overall negative correlation-rather than the classical U-shaped distribution-between valence and arousal for socially relevant concepts. Our database provides a valuable resource for research questions at the intersection of cognitive neuroscience and social psychology. It can be downloaded as supplemental materials with this article.

Parametric Analysis to Study the Influence of Aerogel-Based Renders’ Components on Thermal and Mechanical Performance

PubMed Central

Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

2016-01-01

Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study’s objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect. PMID:28773460

The role of helplessness, fear of pain, and passive pain-coping in chronic pain patients.

PubMed

Samwel, Han J A; Evers, Andrea W M; Crul, Ben J P; Kraaimaat, Floris W

2006-01-01

The goal of this study was to examine the relative contribution of helplessness, fear of pain, and passive pain-coping to pain level, disability, and depression in chronic pain patients attending an interdisciplinary pain center. One hundred sixty-nine chronic pain patients who had entered treatment at an interdisciplinary pain center completed various questionnaires and a pain diary. Helplessness, fear of pain, and passive pain-coping strategies were all related to the pain level, disability, and depression. When comparing the contribution of the predictors in multiple regression analyses, helplessness was the only significant predictor for pain level. Helplessness and the passive behavioral pain-coping strategies of resting significantly predicted disability. The passive cognitive pain-coping strategy of worrying significantly predicted depression. These findings indicate a role for helplessness and passive pain-coping in chronic pain patients and suggest that both may be relevant in the treatment of pain level, disability, and/or depression.

A paclitaxel-loaded recombinant polypeptide nanoparticle outperforms Abraxane in multiple murine cancer models

NASA Astrophysics Data System (ADS)

Bhattacharyya, Jayanta; Bellucci, Joseph J.; Weitzhandler, Isaac; McDaniel, Jonathan R.; Spasojevic, Ivan; Li, Xinghai; Lin, Chao-Chieh; Chi, Jen-Tsan Ashley; Chilkoti, Ashutosh

2015-08-01

Packaging clinically relevant hydrophobic drugs into a self-assembled nanoparticle can improve their aqueous solubility, plasma half-life, tumour-specific uptake and therapeutic potential. To this end, here we conjugated paclitaxel (PTX) to recombinant chimeric polypeptides (CPs) that spontaneously self-assemble into ~60 nm near-monodisperse nanoparticles that increased the systemic exposure of PTX by sevenfold compared with free drug and twofold compared with the Food and Drug Administration-approved taxane nanoformulation (Abraxane). The tumour uptake of the CP-PTX nanoparticle was fivefold greater than free drug and twofold greater than Abraxane. In a murine cancer model of human triple-negative breast cancer and prostate cancer, CP-PTX induced near-complete tumour regression after a single dose in both tumour models, whereas at the same dose, no mice treated with Abraxane survived for >80 days (breast) and 60 days (prostate), respectively. These results show that a molecularly engineered nanoparticle with precisely engineered design features outperforms Abraxane, the current gold standard for PTX delivery.

Moral judgment and its relation to second-order theory of mind.

PubMed

Fu, Genyue; Xiao, Wen S; Killen, Melanie; Lee, Kang

2014-08-01

Recent research indicates that moral judgment and 1st-order theory of mind abilities are related. What is not known, however, is how 2nd-order theory of mind is related to moral judgment. In the present study, we extended previous findings by administering a morally relevant theory of mind task (an accidental transgressor) to 4- to 7-year-old Chinese children (N = 79) and analyzing connections with 2nd-order theory of mind understanding. Using hierarchical multiple regression analyses, we found that above and beyond age, children's 1st-order theory of mind and 2nd-order theory of mind each significantly and uniquely contributed to children's moral evaluations of the intention in the accidental transgression. These findings highlight the important roles that 1st- and 2nd-order theory of mind play in leading children to make appropriate moral judgments based on an actor's intention in a social situation. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

PubMed

Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

2012-12-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

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

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

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

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

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

Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF).

PubMed

Gissi, Andrea; Lombardo, Anna; Roncaglioni, Alessandra; Gadaleta, Domenico; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio; Benfenati, Emilio

2015-02-01

The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R(2)) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100L/kg for Chemical Safety Assessment; 500L/kg for Classification and Labeling; 2000 and 5000L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R(2)>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot-Gobas models. As classifiers, ACD and logP-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R(2)ext=0.59), followed by the EPISuite Meylan model (R(2)ext=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R(2)=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R(2)=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach. Copyright © 2014 Elsevier Inc. All rights reserved.

Multiple regression analysis in nomogram development for myopic wavefront laser in situ keratomileusis: Improving astigmatic outcomes.

PubMed

Allan, Bruce D; Hassan, Hala; Ieong, Alvin

2015-05-01

To describe and evaluate a new multiple regression-derived nomogram for myopic wavefront laser in situ keratomileusis (LASIK). Moorfields Eye Hospital, London, United Kingdom. Prospective comparative case series. Multiple regression modeling was used to derive a simplified formula for adjusting attempted spherical correction in myopic LASIK. An adaptation of Thibos' power vector method was then applied to derive adjustments to attempted cylindrical correction in eyes with 1.0 diopter (D) or more of preoperative cylinder. These elements were combined in a new nomogram (nomogram II). The 3-month refractive results for myopic wavefront LASIK (spherical equivalent ≤11.0 D; cylinder ≤4.5 D) were compared between 299 consecutive eyes treated using the earlier nomogram (nomogram I) in 2009 and 2010 and 414 eyes treated using nomogram II in 2011 and 2012. There was no significant difference in treatment accuracy (variance in the postoperative manifest refraction spherical equivalent error) between nomogram I and nomogram II (P = .73, Bartlett test). Fewer patients treated with nomogram II had more than 0.5 D of residual postoperative astigmatism (P = .0001, Fisher exact test). There was no significant coupling between adjustments to the attempted cylinder and the achieved sphere (P = .18, t test). Discarding marginal influences from a multiple regression-derived nomogram for myopic wavefront LASIK had no clinically significant effect on treatment accuracy. Thibos' power vector method can be used to guide adjustments to the treatment cylinder alongside nomograms designed to optimize postoperative spherical equivalent results in myopic LASIK. mentioned. Copyright © 2015 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia.

PubMed

Almalki, Mohammed J; FitzGerald, Gerry; Clark, Michele

2012-09-12

Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. A cross-sectional survey was used in this study. Data were collected using Brooks' survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia

PubMed Central

2012-01-01

Background Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. Methods A cross-sectional survey was used in this study. Data were collected using Brooks’ survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Results Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Conclusions Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes. PMID:22970764

Risk factors for autistic regression: results of an ambispective cohort study.

PubMed

Zhang, Ying; Xu, Qiong; Liu, Jing; Li, She-chang; Xu, Xiu

2012-08-01

A subgroup of children diagnosed with autism experience developmental regression featured by a loss of previously acquired abilities. The pathogeny of autistic regression is unknown, although many risk factors likely exist. To better characterize autistic regression and investigate the association between autistic regression and potential influencing factors in Chinese autistic children, we conducted an ambispective study with a cohort of 170 autistic subjects. Analyses by multiple logistic regression showed significant correlations between autistic regression and febrile seizures (OR = 3.53, 95% CI = 1.17-10.65, P = .025), as well as with a family history of neuropsychiatric disorders (OR = 3.62, 95% CI = 1.35-9.71, P = .011). This study suggests that febrile seizures and family history of neuropsychiatric disorders are correlated with autistic regression.

Culture, Relevance, and Schooling: Exploring Uncommon Ground

ERIC Educational Resources Information Center

Scherff, Lisa, Ed.; Spector, Karen, Ed.

2011-01-01

In "Culture, Relevance, and Schooling: Exploring Uncommon Ground," Lisa Scherff, Karen Spector, and the contributing authors conceive of culturally relevant and critically minded pedagogies in terms of opening up new spatial, discursive, and/or embodied learning terrains. Readers will traverse multiple landscapes and look into a variety of spaces…

Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study

PubMed Central

Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

2015-01-01

The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200–400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. PMID:25005037

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…

Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.

ERIC Educational Resources Information Center

Algina, James; Olejnik, Stephen

2000-01-01

Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)

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.

The Attitudes of First Year Senior Secondary School Students toward Their Science Classes in the Sudan

NASA Astrophysics Data System (ADS)

Lado, Longun Moses

This study examined the influence of a set of relevant independent variables on students' decision to major in math or science disciplines, on the one hand, or arts or humanities disciplines, on the other. The independent variables of interest in the study were students' attitudes toward science, their gender, their socioeconomic status, their age, and the strength and direction of parents' and peers' influences on their academic decisions. The study answered five research questions that concerned students' intention in math or science, the association between students' attitudes and their choice to major in math or science, the extent to which parents' and peers' perspectives influence students' choice of major, and the influence of a combination of relevant variables on students' choice of major. The scholarly context for the study was literature relating to students' attitudes toward science and math, their likelihood of taking courses or majoring in science or math and various conditions influencing their attitudes and actions with respect to enrollment in science or math disciplines. This literature suggested that students' experiences, their gender, parents' and peers' influence, their socio-economic status, teachers' treatment of them, school curricula, school culture, and other variables may influence students' attitudes toward science and math and their decision regarding the study of these subjects. The study used a questionnaire comprised of 28 items to elicit information from students. Based upon cluster sampling of secondary schools, the researcher surveyed 1000 students from 10 secondary schools and received 987 responses. The researcher used SPSS to analyze students' responses. Descriptive statistics, logistic regression, and multiple regression analyses to provide findings that address the study's research questions. The following are the major findings from the study: (1) The instrument used to measure students' attitudes toward science and mathematics was not highly reliable, perhaps contributing to an attenuation of the relationship between attitude toward science and mathematics and choice of a science or mathematics major (rather than an arts or humanities major). (2) Far more students than the researcher had anticipated provided responses indicating that they planned to major in a science or mathematics discipline rather than an arts or humanities discipline. (3) Students' attitudes towards math and science were more favorable than the researcher anticipated based on findings from previous related studies. This result suggests the possibility of social desirability bias in students' responses. (4) Three significant predicator variables contributed to a significant logistic regression equation in which choice of science or mathematics major was the dependent variable: gender (negative association), attitude toward science and math (positive association), and peer influence 1 (positive association). Gender was the strongest predictor. (5) Five significant predictor variables contributed to a significant multiple linear regression equation in which attitude toward science and mathematics was the dependent variable: peer influence 1 (positive association), parent influence 1 (positive association), parent influence 2 (positive association), books in home (positive association), and peer influence 2 (positive association). The results reveal that among the targeted variables (gender, attitude, peer influence 1, peer influence 2, parent influence 1, parent influence 2, books in home, and age) only gender, peer influence 1, and attitude were significant predictors of students' major in math or science.

Flood characteristics of Alaskan streams

USGS Publications Warehouse

Lamke, R.D.

1979-01-01

Peak discharge data for Alaskan streams are summarized and analyzed. Multiple-regression equations relating peak discharge magnitude and frequency to climatic and physical characteristics of 260 gaged basins were determined in order to estimate average recurrence interval of floods at ungaged sites. These equations are for 1.25-, 2-, 5-, 10-, 25-, and 50-year average recurrence intervals. In this report, Alaska was divided into two regions, one having a maritime climate with fall and winter rains and floods, the other having spring and summer floods of a variety or combinations of causes. Average standard errors of the six multiple-regression equations for these two regions were 48 and 74 percent, respectively. Maximum recorded floods at more than 400 sites throughout Alaska are tabulated. Maps showing lines of equal intensity of the principal climatic variables found to be significant (mean annual precipitation and mean minimum January temperature), and location of the 260 sites used in the multiple-regression analyses are included. Little flood data have been collected in western and arctic Alaska, and the predictive equations are therefore less reliable for those areas. (Woodard-USGS)

Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

PubMed

Suresh, Arumuganainar; Choi, Hong Lim

2011-10-01

Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (P<0.001) multiple property correlations (R²) were obtained between nutrients with specific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Mapping diffuse photosynthetically active radiation from satellite data in Thailand

NASA Astrophysics Data System (ADS)

Choosri, P.; Janjai, S.; Nunez, M.; Buntoung, S.; Charuchittipan, D.

2017-12-01

In this paper, calculation of monthly average hourly diffuse photosynthetically active radiation (PAR) using satellite data is proposed. Diffuse PAR was analyzed at four stations in Thailand. A radiative transfer model was used for calculating the diffuse PAR for cloudless sky conditions. Differences between the diffuse PAR under all sky conditions obtained from the ground-based measurements and those from the model are representative of cloud effects. Two models are developed, one describing diffuse PAR only as a function of solar zenith angle, and the second one as a multiple linear regression with solar zenith angle and satellite reflectivity acting linearly and aerosol optical depth acting in logarithmic functions. When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of -2.20%. Results from the multiple regression model are used to map diffuse PAR throughout the country as monthly averages of hourly data.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

PubMed

Bersabé, Rosa; Rivas, Teresa

2010-05-01

The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

Specificity of the Relationships Between Dysphoria and Related Constructs in an Outpatient Sample.

PubMed

Starcevic, Vladan; Berle, David; Viswasam, Kirupamani; Hannan, Anthony; Milicevic, Denise; Brakoulias, Vlasios; Dale, Erin

2015-12-01

Dysphoria has recently been conceptualized as a complex emotional state that consists of discontent and/or unhappiness and a predominantly externalizing mode of coping with these feelings. The Nepean Dysphoria Scale (NDS) was developed on the basis of this model of dysphoria and used in this clinical study to ascertain the specificity of the relationships between dysphoria and relevant domains of psychopathology. Ninety-six outpatients completed the NDS, Symptom Checklist 90-Revised (SCL-90R) and Depression, Anxiety, Stress Scales, 21-item version (DASS-21). The scores on the NDS subscales (Discontent, Surrender, Irritability and Interpersonal Resentment) and total NDS scores correlated significantly with scores on the DASS-21 scales and relevant SCL-90R subscales. Multiple regression analyses demonstrated the following: DASS-21 Depression and Stress each had unique relationships with NDS Discontent and Surrender; DASS-21 Anxiety had a unique relationship with NDS Discontent; SCL-90R Hostility and Paranoid Ideation and DASS-21 Stress each had unique relationships with NDS Irritability; and SCL-90R Paranoid Ideation and DASS-21 Stress, Depression and Anxiety each had unique relationships with NDS Interpersonal Resentment. These findings support the notion that dysphoria is a complex emotional state, with both non-specific and specific relationships with irritability, tension, depression, paranoid tendencies, anxiety, hostility and interpersonal sensitivity. Conceptual rigor when referring to dysphoria should be promoted in both clinical practice and further research.

Suicide and occupation: further supportive evidence for their relevance.

PubMed

Nishimura, Mariko; Terao, Takeshi; Soeda, Shuji; Nakamura, Jun; Iwata, Noboru; Sakamoto, Kaoru

2004-01-01

In recent years, the relationship between occupation and suicide has been extensively investigated, but few definite conclusions regarding the nature of the relationship have been established. In the present study, this relationship was investigated by examining Japanese governmental statistics. First, correlations of suicide rate relative to industry categories were examined individually for primary industry (farmers, fishermen, and forest workers), secondary industry (construction workers, manufacture works, and miners), and tertiary industry (indoor workers) for all of the 47 prefectures of Japan. Second, in the industries that showed a significant correlation with suicide rate, the relationship to other factors was adjusted using possibly confounding factors. As a result, suicide rate was positively correlated with primary industry percentage, but not with secondary or tertiary industry percentages. Multiple regression analysis showed that suicide rate was positively associated with primary industry percentage with significant tendency while it was significantly and negatively associated with annual total sunshine. Limitations are that individual suicide rates according to occupational types were not available and direct correlations with the above variables could not be investigated. The present findings suggest a possibility that occupational factors associated with primary industry may be relevant to suicide, and that, additionally, annual total sunshine may affect suicide independently. Since workers with primary industry are likely to be exposed to sunshine than other workers, they may tend to be more affected by the decrease of annual total sunshine.

Application of Partial Least Square (PLS) Regression to Determine Landscape-Scale Aquatic Resources Vulnerability in the Ozark Mountains

EPA Science Inventory

Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology, particularly for determining the associations among multiple constituents of surface water and landscape configuration. Common dat...

Examining the Effects of Perceived Relevance and Work-Related Subjective Well-Being on Individual Performance for Co-Op Students

ERIC Educational Resources Information Center

Drewery, Dave; Pretti, T. Judene; Barclay, Sage

2016-01-01

The purpose of this study was to examine the relationships between co-op students' perceived relevance of their work term, work-related subjective well-being (SWB), and individual performance at work. Data were collected using a survey of co-op students (n = 1,989) upon completion of a work term. Results of regression analyses testing a…

Cluster Analysis of Campylobacter jejuni Genotypes Isolated from Small and Medium-Sized Mammalian Wildlife and Bovine Livestock from Ontario Farms.

PubMed

Viswanathan, M; Pearl, D L; Taboada, E N; Parmley, E J; Mutschall, S K; Jardine, C M

2017-05-01

Using data collected from a cross-sectional study of 25 farms (eight beef, eight swine and nine dairy) in 2010, we assessed clustering of molecular subtypes of C. jejuni based on a Campylobacter-specific 40 gene comparative genomic fingerprinting assay (CGF40) subtypes, using unweighted pair-group method with arithmetic mean (UPGMA) analysis, and multiple correspondence analysis. Exact logistic regression was used to determine which genes differentiate wildlife and livestock subtypes in our study population. A total of 33 bovine livestock (17 beef and 16 dairy), 26 wildlife (20 raccoon (Procyon lotor), five skunk (Mephitis mephitis) and one mouse (Peromyscus spp.) C. jejuni isolates were subtyped using CGF40. Dendrogram analysis, based on UPGMA, showed distinct branches separating bovine livestock and mammalian wildlife isolates. Furthermore, two-dimensional multiple correspondence analysis was highly concordant with dendrogram analysis showing clear differentiation between livestock and wildlife CGF40 subtypes. Based on multilevel logistic regression models with a random intercept for farm of origin, we found that isolates in general, and raccoons more specifically, were significantly more likely to be part of the wildlife branch. Exact logistic regression conducted gene by gene revealed 15 genes that were predictive of whether an isolate was of wildlife or bovine livestock isolate origin. Both multiple correspondence analysis and exact logistic regression revealed that in most cases, the presence of a particular gene (13 of 15) was associated with an isolate being of livestock rather than wildlife origin. In conclusion, the evidence gained from dendrogram analysis, multiple correspondence analysis and exact logistic regression indicates that mammalian wildlife carry CGF40 subtypes of C. jejuni distinct from those carried by bovine livestock. Future studies focused on source attribution of C. jejuni in human infections will help determine whether wildlife transmit Campylobacter jejuni directly to humans. © 2016 Blackwell Verlag GmbH.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

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.

Comparison of Different Shrinkage Formulas in Estimating Population Multiple Correlation Coefficients.

ERIC Educational Resources Information Center

Carter, David S.

1979-01-01

There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)

On Relevance of Codon Usage to Expression of Synthetic and Natural Genes in Escherichia coli

PubMed Central

Supek, Fran; Šmuc, Tomislav

2010-01-01

A recent investigation concluded that codon bias did not affect expression of green fluorescent protein (GFP) variants in Escherichia coli, while stability of an mRNA secondary structure near the 5′ end played a dominant role. We demonstrate that combining the two variables using regression trees or support vector regression yields a biologically plausible model with better support in the GFP data set and in other experimental data: codon usage is relevant for protein levels if the 5′ mRNA structures are not strong. Natural E. coli genes had weaker 5′ mRNA structures than the examined set of GFP variants and did not exhibit a correlation between the folding free energy of 5′ mRNA structures and protein expression. PMID:20421604

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

PubMed

Zhang, Y J; Zhou, D H; Bai, Z P; Xue, F X

2018-02-10

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives . Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Estimating Optimal Transformations for Multiple Regression and Correlation.

DTIC Science & Technology

1982-07-01

S w.EECTli1Z"", , J OCT 0 11982 u! !for Public its... .. . ESTIMATING OPTIMAL TRANSFORMATIONS FOR MULTIPLE REGRESSION AND CORRELATION by Leo...in the plot lb of *(yk) versus 1 < k < 200. Figure lc is a plot of $*(xk) versus xk. These plots clearly suggest the transformati " s 6(y) = log(y) and...direct .814 .022 ACE .808 .031 -13- Figure la6L ’ ’ I . . . S " ’ ’ . . I ’ 6- - - .4...... Co o • . o ’ 0 0.2 0.4 0.5 0.8 1 Fi gure lb2 2 2 // II / / -/

Bark analysis as a guide to cassava nutrition in Sierra Leone

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

Godfrey-Sam-Aggrey, W.; Garber, M.J.

1979-01-01

Cassava main stem barks from two experiments in which similar fertilizers were applied directly in a 2/sup 5/ confounded factorial design were analyzed and the bark nutrients used as a guide to cassava nutrition. The application of multiple regression analysis to the respective root yields and bark nutrient concentrations enable nutrient levels and optimum adjusted root yields to be derived. Differences in bark nutrient concentrations reflected soil fertility levels. Bark analysis and the application of multiple regression analysis to root yields and bark nutrients appear to be useful tools for predicting fertilizer recommendations for cassava production.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

The impact of napping on memory for future-relevant stimuli: Prioritization among multiple salience cues.

PubMed

Bennion, Kelly A; Payne, Jessica D; Kensinger, Elizabeth A

2016-06-01

Prior research has demonstrated that sleep enhances memory for future-relevant information, including memory for information that is salient due to emotion, reward, or knowledge of a later memory test. Although sleep has been shown to prioritize information with any of these characteristics, the present study investigates the novel question of how sleep prioritizes information when multiple salience cues exist. Participants encoded scenes that were future-relevant based on emotion (emotional vs. neutral), reward (rewarded vs. unrewarded), and instructed learning (intentionally vs. incidentally encoded), preceding a delay consisting of a nap, an equivalent time period spent awake, or a nap followed by wakefulness (to control for effects of interference). Recognition testing revealed that when multiple dimensions of future relevance co-occur, sleep prioritizes top-down, goal-directed cues (instructed learning, and to a lesser degree, reward) over bottom-up, stimulus-driven characteristics (emotion). Further, results showed that these factors interact; the effect of a nap on intentionally encoded information was especially strong for neutral (relative to emotional) information, suggesting that once one cue for future relevance is present, there are diminishing returns with additional cues. Sleep may binarize information based on whether it is future-relevant or not, preferentially consolidating memory for the former category. Potential neural mechanisms underlying these selective effects and the implications of this research for educational and vocational domains are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Controlling the Rate of GWAS False Discoveries

PubMed Central

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

2017-01-01

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

Controlling the Rate of GWAS False Discoveries.

PubMed

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

2017-01-01

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

Clinical Correlations of Brain Lesion Location in Multiple Sclerosis: Voxel-Based Analysis of a Large Clinical Trial Dataset.

PubMed

Altermatt, Anna; Gaetano, Laura; Magon, Stefano; Häring, Dieter A; Tomic, Davorka; Wuerfel, Jens; Radue, Ernst-Wilhelm; Kappos, Ludwig; Sprenger, Till

2018-05-29

There is a limited correlation between white matter (WM) lesion load as determined by magnetic resonance imaging and disability in multiple sclerosis (MS). The reasons for this so-called clinico-radiological paradox are diverse and may, at least partly, relate to the fact that not just the overall lesion burden, but also the exact anatomical location of lesions predict the severity and type of disability. We aimed at studying the relationship between lesion distribution and disability using a voxel-based lesion probability mapping approach in a very large dataset of MS patients. T2-weighted lesion masks of 2348 relapsing-remitting MS patients were spatially normalized to standard stereotaxic space by non-linear registration. Relations between supratentorial WM lesion locations and disability measures were assessed using a non-parametric ANCOVA (Expanded Disability Status Scale [EDSS]; Multiple Sclerosis Functional Composite, and subscores; Modified Fatigue Impact Scale) or multinomial ordinal logistic regression (EDSS functional subscores). Data from 1907 (81%) patients were included in the analysis because of successful registration. The lesion mapping showed similar areas to be associated with the different disability scales: periventricular regions in temporal, frontal, and limbic lobes were predictive, mainly affecting the posterior thalamic radiation, the anterior, posterior, and superior parts of the corona radiata. In summary, significant associations between lesion location and clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers, which are relevant WM pathways supporting many different brain functions.

gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

PubMed

Larson, Nicholas B; McDonnell, Shannon; Cannon Albright, Lisa; Teerlink, Craig; Stanford, Janet; Ostrander, Elaine A; Isaacs, William B; Xu, Jianfeng; Cooney, Kathleen A; Lange, Ethan; Schleutker, Johanna; Carpten, John D; Powell, Isaac; Bailey-Wilson, Joan E; Cussenot, Olivier; Cancel-Tassin, Geraldine; Giles, Graham G; MacInnis, Robert J; Maier, Christiane; Whittemore, Alice S; Hsieh, Chih-Lin; Wiklund, Fredrik; Catalona, William J; Foulkes, William; Mandal, Diptasri; Eeles, Rosalind; Kote-Jarai, Zsofia; Ackerman, Michael J; Olson, Timothy M; Klein, Christopher J; Thibodeau, Stephen N; Schaid, Daniel J

2017-05-01

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results. © 2017 WILEY PERIODICALS, INC.

Regression Analysis with Dummy Variables: Use and Interpretation.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Oliver, J. Dale

1986-01-01

Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

REGRESSION MODELS THAT RELATE STREAMS TO WATERSHEDS: COPING WITH NUMEROUS, COLLINEAR PEDICTORS

EPA Science Inventory

GIS efforts can produce a very large number of watershed variables (climate, land use/land cover and topography, all defined for multiple areas of influence) that could serve as candidate predictors in a regression model of reach-scale stream features. Invariably, many of these ...

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…

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…

Regression of a vaginal leiomyoma after ovariohysterectomy in a dog: a case report.

PubMed

Sathya, Suresh; Linn, Kathleen

2014-01-01

An 11 yr old female mixed-breed Siberian husky was presented with a history of sanguineous vaginal discharge, swelling of the perineal area, decreased appetite, and lethargy. A single, large vaginal leiomyoma and multiple mammary tumors were diagnosed. Mastectomy and ovariohysterectomy were performed. The vaginal leiomyoma regressed completely after ovariohysterectomy. This is the first reported case of spontaneous regression of a vaginal leiomyoma after ovariohysterectomy in a dog.

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis.

PubMed

Aneja, Viney P; Pillai, Priya R; Isherwood, Aaron; Morgan, Peter; Aneja, Saurabh P

2017-04-01

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM 10 ), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM 2.5 ) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM 2.5 (r 2 = 0.62), and the two-variable (AOD-PM 2.5 ) model predicted PM 2.5 (r 2 = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM 2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM 2.5 . For the relevant period in 2008, in Roda, VA, the predicted PM 2.5 mass concentration is 9.11 ± 5.16 μg m -3 (mean ± 1SD). This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or "hollows," where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.

Uterine Fibroids: Correlation of T2 Signal Intensity with Semiquantitative Perfusion MR Parameters in Patients Screened for MR-guided High-Intensity Focused Ultrasound Ablation.

PubMed

Kim, Young-Sun; Lee, Jeong-Won; Choi, Chel Hun; Kim, Byoung-Gie; Bae, Duk-Soo; Rhim, Hyunchul; Lim, Hyo Keun

2016-03-01

To evaluate the relationships between T2 signal intensity and semiquantitative perfusion magnetic resonance (MR) parameters of uterine fibroids in patients who were screened for MR-guided high-intensity focused ultrasound (HIFU) ablation. Institutional review board approval was granted, and informed consents were waived. One hundred seventy most symptom-relevant, nondegenerated uterine fibroids (mean diameter, 7.3 cm; range, 3.0-17.2 cm) in 170 women (mean age, 43.5 years; range, 24-56 years) undergoing screening MR examinations for MR-guided HIFU ablation from October 2009 to April 2014 were retrospectively analyzed. Fibroid signal intensity was assessed as the ratio of the fibroid T2 signal intensity to that of skeletal muscle. Parameters of semiquantitative perfusion MR imaging obtained during screening MR examination (peak enhancement, percentage of relative peak enhancement, time to peak [in seconds], wash-in rate [per seconds], and washout rate [per seconds]) were investigated to assess their relationships with T2 signal ratio by using multiple linear regression analysis. Correlations between T2 signal intensity and independently significant perfusion parameters were then evaluated according to fibroid type by using Spearman correlation test. Multiple linear regression analysis revealed that relative peak enhancement showed an independently significant correlation with T2 signal ratio (Β = 0.004, P < .001). Submucosal intracavitary (n = 20, ρ = 0.275, P = .240) and type III (n = 18, ρ = 0.082, P = .748) fibroids failed to show significant correlations between perfusion and T2 signal intensity, while significant correlations were found for all other fibroid types (ρ = 0.411-0.629, P < .05). In possible candidates for MR-guided HIFU ablation, the T2 signal intensity of nondegenerated uterine fibroids showed an independently significant positive correlation with relative peak enhancement in most cases, except those of submucosal intracavitary or type III fibroids.

Intimate partner violence: associations with low infant birthweight in a South African birth cohort

PubMed Central

Wyatt, Gail E.; Williams, John K.; Zhang, Muyu; Myer, Landon; Zar, Heather J.; Stein, Dan J.

2015-01-01

Violence against women is a global public health problem. Exposure to intimate partner violence (IPV) during pregnancy has been associated with a number of adverse maternal and fetal outcomes, including delivery of a low birthweight (LBW) infant. However, there is a paucity of data from low-middle income countries (LMIC). We examined the association between antenatal IPV and subsequent LBW in a South African birth cohort. This study reports data from the Drakenstein Child Lung Health Study (DCLHS), a multidisciplinary birth cohort investigation of the influence of a number of antecedent risk factors on maternal and infant health outcomes over time. Pregnant women seeking antenatal care were recruited at two different primary care clinics in a low income, semi-rural area outside Cape Town, South Africa. Antenatal trauma exposure was assessed using the Childhood Trauma Questionnaire (CTQ) and an IPV assessment tool specifically designed for the purposes of this study. Potential confounding variables including maternal sociodemographics, pregnancy intention, partner support, biomedical and mental illness, substance use and psychosocial risk were also assessed. Bivariate and multiple regression analyses were performed to determine the association between IPV during pregnancy and delivery of an infant with LBW and/or low weight-for-age z (WAZ) scores. The final study sample comprised 263 mother-infant dyads. In multiple regression analyses, the model run was significant [r2=0.14 (adjusted r2=0.11, F(8, 212) = 4.16, p=0.0001]. Exposure to physical IPV occurring during the past year was found to be significantly associated with LBW [t=−2.04, p=0.0429] when controlling for study site (clinic), maternal height, ethnicity, socioeconomic status, substance use and childhood trauma. A significant association with decreased WAZ scores was not demonstrated. Exposure of pregnant women to IPV may impact newborn health. Further research is needed in this field to assess the relevant underlying mechanisms, to inform public health policies and to develop appropriate trauma IPV interventions for LMIC settings. PMID:24729207

HLA and killer cell immunoglobulin-like receptor (KIRs) genotyping in patients with acute viral encephalitis

PubMed Central

Tuttolomondo, Antonino; Colomba, Claudia; Di Bona, Danilo; Casuccio, Alessandra; Di Raimondo, Domenico; Clemente, Giuseppe; Arnao, Valentina; Pecoraro, Rosaria; Ragonese, Paolo; Aiello, Anna; Accardi, Giulia; Maugeri, Rosario; Maida, Carlo; Simonetta, Irene; Della Corte, Vittoriano; Iacopino, Domenico Gerardo; Caruso, Calogero; Cascio, Antonio; Pinto, Antonio

2018-01-01

Introduction The HLA genes, as well as the innate immune KIR genes, are considered relevant determinants of viral outcomes but no study, to our knowledge, has evaluated their role in the clinical setting of acute viral encephalitis. Results Subjects with acute viral encephalitis in comparison to subjects without acute viral encephalitis showed a significantly higher frequency of 2DL1 KIR gene and AA KIR haplotypes and of HLA-C2 and HLA-A-Bw4 alleles. Subjects without acute viral encephalitis showed a higher frequency of interaction between KIR2DL2 and HLAC1. Multiple logistic regression analysis showed the detrimental effect of HLA-A haplotype and HLA-C1, HLA-A-BW4 HLA-B-BW4T alleles, whereas multiple logistic regression showed a protective effect of AB+BB KIR haplotype and a detrimental effect of interaction between KIR3DL1 and HLA-A-Bw4. Discussion Our findings of a lower frequency of activating receptors in patients with acute encephalitis compared to controls could result in a less efficient response of NK cells. This finding could represent a possible pathogenetic explanation of susceptibility to acute symptomatic encephalitis in patients with viral infection from potentially responsible viruses such as Herpes virus. Materials and Methods 30 Consecutive patients with symptomatic acute viral encephalitis and as controls, 36 consecutive subjects without acute encephalitis were analyzed. The following KIR genes were analyzed, KIR2DL1, 2DL2, 2DL3, 2DL5, 3DL1, 3DL2, 3DL3, 2DL4, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5, 3DS1, 2 pseudogenes (2DP1 and 3DP1) and the common variants of KIR2DL5 (KIR2DL5A, KIR2DL5B). PMID:29707126

Neighbourhood Factors and Depression among Adolescents in Four Caribbean Countries

PubMed Central

Gibson, Roger C.; Halliday, Sharon; Morris, Amrie; Clarke, Nelson; Wilson, Rosemarie N.

2014-01-01

Background Past research suggests that perceived neighbourhood conditions may influence adolescents' emotional health. Relatively little research has been conducted examining the association of perceived neighbourhood conditions with depressive symptoms among Caribbean adolescents. This project examines the association of perceived neighbourhood conditions with levels of depressive symptoms among adolescents in Jamaica, the Bahamas, St. Kitts and Nevis, and St. Vincent. Methods Adolescents attending grade ten of the academic year 2006/2007 in Jamaica, the Bahamas, St. Vincent, and St. Kitts and Nevis were administered the Neighbourhood Characteristics Questionnaire along with the BDI-II. Social cohesion, attachment to the neighbourhood, neighbourhood quality, neighbourhood crime, and neighbourhood disorder scales were created by summing the relevant subscales of the Neighbourhood Characteristics Questionnaire. Multiple regression analyses were used to examine the relationships of perceived neighbourhood conditions to depressive symptoms. Results A wide cross-section of tenth grade students in each nation was sampled (n = 1955; 278 from Jamaica, 217 from the Bahamas, 737 St. Kitts and Nevis, 716 from St. Vincent; 52.1% females, 45.6% males and 2.3% no gender reported; 12 to 19 years, mean = 15.3 yrs, sd = .95 yr). Nearly half (52.1%) of all adolescents reported mild to severe symptoms of depression with 29.1% reporting moderate to severe symptoms of depression. Overall, Jamaican adolescents perceived their neighbourhoods in a more positive manner than those in the Bahamas, St. Vincent and St. Kitts and Nevis. Results of a series of hierarchical multiple regression analyses suggested that a different pattern of neighbourhood factors for each island were associated with depressive symptoms. However, neighbourhood factors were more highly associated with depressive symptoms for Jamaican students than for students in the other three islands. Conclusions Neighbourhood factors appear to be partially associated with adolescents' self-reports of depressive symptoms. However, other factors may mitigate this relationship. PMID:24760035

Somatization among persons with Turkish origin: Results of the pretest of the German National Cohort Study.

PubMed

Morawa, Eva; Dragano, Nico; Jöckel, Karl-Heinz; Moebus, Susanne; Brand, Tilman; Erim, Yesim

2017-05-01

Despite the emerging need to examine mental health of immigrants, there are no investigations designed to analyze representative samples in Germany. The aim of the present study was to explore the severity of somatic symptoms/somatization among a sample of considerable size consisting of persons with Turkish origin. We studied whether somatization was associated with sociodemographic and migration-related characteristics. This examination was part of a pretest for a large national epidemiological cohort study in Germany. We applied the somatization (PHQ-15) and the depression module (PHQ-9) from the Patient Health Questionnaire in a subsample of 335 Turkish immigrants. We analyzed the distribution of the sum score. Differences in degree of somatization in relation to relevant socio-demographic (gender) and migrant-related characteristics (generation of immigration) were tested with analysis of covariance (ANCOVA), controlling for age. A multiple linear regression analysis was also conducted. Women had significantly higher age-adjusted mean scores than men (M=10.4, SD=6.3 vs. M=8.1, SD=6.3; F=10.467, p=0.001), a significant effect of age was also found (F=4.853, p=0.028). First generation immigrants had a higher age-adjusted mean number of symptoms in relation to the second generation immigrants (M=10.0, SD=6.5 vs. M=7.4, SD=7.0; F=6.042, p=0.014), the effect of age was not significant (F=0.466, p=0.495). Multiple regression analysis revealed that lower severity of somatization was associated with lower numbers of diagnosed physical illnesses (β=0.271, p<0.001) and better language proficiency (β=0.197, p=0.003, explained variance: 15.6%). The degree of somatization among Turkish immigrants in Germany is associated with gender and generation of immigration. Copyright © 2017 Elsevier Inc. All rights reserved.

The Association between Anticholinergic Drug Use and Rehabilitation Outcome in Post-Acute Hip Fractured Patients: A Retrospective Cohort Study.

PubMed

Hershkovitz, Avital; Angel, Corina; Brill, Shai; Nissan, Ran

2018-04-01

Anticholinergic (AC) drugs are associated with significant impairment in cognitive and physical function which may affect rehabilitation in older people. We aimed to evaluate whether AC burden is associated with rehabilitation achievement in post-acute hip-fractured patients. A retrospective cohort study carried out in a post-acute geriatric rehabilitation center on 1019 hip-fractured patients admitted from January 2011 to October 2015. The Anticholinergic Cognitive Burden Scale (ACB) was used to quantify the AC burden. Main outcome measures included the Functional Independence Measure (FIM) instrument, motor FIM (mFIM), Montebello Rehabilitation Factor Score (MRFS) on the mFIM, and length of stay (LOS). The study population was divided into two groups: individuals with low admission AC burden (ACB ≤ 1) and those with high admission AC burden (ACB ≥ 2). The relationship between the admission AC burden and clinical, demographic and comorbidity variables was assessed using the Mann-Whitney and Chi square tests. A multiple linear regression model was used to estimate the association between admission AC burden and discharge FIM score after controlling for sociodemographic characteristics and chronic diseases. Patients with a high admission AC burden had a significantly higher rate of high education, a significantly lower rate reside at home, they waited a longer period of time from surgery to rehabilitation, were less independent pre-fracture, and presented with a higher rate of vascular disorders and depression compared with patients with a lower admission AC burden. These patients also exhibited a significantly lower FIM score on admission and at discharge, a lower FIM score change, and a lower achievement on the MRFS compared with patients with a lower admission AC burden. A multiple linear regression analysis showed that admission AC burden was significantly associated with the discharge FIM score after adjustment for confounding variables. High admission AC drug burden is significantly associated with less favorable discharge functional status in post-acute hip-fractured patients, independent of relevant risk factors.

Neighborhood socioeconomic circumstances and the co-occurrence of unhealthy lifestyles: evidence from 206,457 Australians in the 45 and up study.

PubMed

Feng, Xiaoqi; Astell-Burt, Thomas

2013-01-01

Research on the co-occurrence of unhealthy lifestyles has tended to focus mainly upon the demographic and socioeconomic characteristics of individuals. This study investigated the relevance of neighborhood socioeconomic circumstance for multiple unhealthy lifestyles. An unhealthy lifestyle index was constructed for 206,457 participants in the 45 and Up Study (2006-2009) by summing binary responses on smoking, alcohol, physical activity and five diet-related variables. Higher scores indicated the co-occurrence of unhealthy lifestyles. Association with self-rated health, quality of life; and risk of psychological distress was investigated using multilevel logistic regression. Association between the unhealthy lifestyle index with neighborhood characteristics (local affluence and geographic remoteness) were assessed using multilevel linear regression, adjusting for individual-level characteristics. Nearly 50% of the sample reported 3 or 4 unhealthy lifestyles. Only 1.5% reported zero unhealthy lifestyles and 0.2% had all eight. Compared to people who scored zero, those who scored 8 (the 'unhealthiest' group) were 7 times more likely to rate their health as poor (95%CI 3.6, 13.7), 5 times more likely to report poor quality of life (95%CI 2.6, 10.1), and had a 2.6 times greater risk of psychological distress (95%CI 1.8, 3.7). Higher scores among men decreased with age, whereas a parabolic distribution was observed among women. Neighborhood affluence was independently associated with lower scores on the unhealthy lifestyle index. People on high incomes scored higher on the unhealthy lifestyle index if they were in poorer neighborhoods, while those on low incomes had fewer unhealthy lifestyles if living in more affluent areas. Residents of deprived neighborhoods tend to report more unhealthy lifestyles than their peers in affluent areas, regardless of their individual demographic and socioeconomic characteristics. Future research should investigate the trade-offs of population-level versus geographically targeted multiple lifestyle interventions.

Neighborhood Socioeconomic Circumstances and the Co-Occurrence of Unhealthy Lifestyles: Evidence from 206,457 Australians in the 45 and Up Study

PubMed Central

Feng, Xiaoqi; Astell-Burt, Thomas

2013-01-01

Background Research on the co-occurrence of unhealthy lifestyles has tended to focus mainly upon the demographic and socioeconomic characteristics of individuals. This study investigated the relevance of neighborhood socioeconomic circumstance for multiple unhealthy lifestyles. Method An unhealthy lifestyle index was constructed for 206,457 participants in the 45 and Up Study (2006–2009) by summing binary responses on smoking, alcohol, physical activity and five diet-related variables. Higher scores indicated the co-occurrence of unhealthy lifestyles. Association with self-rated health, quality of life; and risk of psychological distress was investigated using multilevel logistic regression. Association between the unhealthy lifestyle index with neighborhood characteristics (local affluence and geographic remoteness) were assessed using multilevel linear regression, adjusting for individual-level characteristics. Results Nearly 50% of the sample reported 3 or 4 unhealthy lifestyles. Only 1.5% reported zero unhealthy lifestyles and 0.2% had all eight. Compared to people who scored zero, those who scored 8 (the ‘unhealthiest’ group) were 7 times more likely to rate their health as poor (95%CI 3.6, 13.7), 5 times more likely to report poor quality of life (95%CI 2.6, 10.1), and had a 2.6 times greater risk of psychological distress (95%CI 1.8, 3.7). Higher scores among men decreased with age, whereas a parabolic distribution was observed among women. Neighborhood affluence was independently associated with lower scores on the unhealthy lifestyle index. People on high incomes scored higher on the unhealthy lifestyle index if they were in poorer neighborhoods, while those on low incomes had fewer unhealthy lifestyles if living in more affluent areas. Interpretation Residents of deprived neighborhoods tend to report more unhealthy lifestyles than their peers in affluent areas, regardless of their individual demographic and socioeconomic characteristics. Future research should investigate the trade-offs of population-level versus geographically targeted multiple lifestyle interventions. PMID:23977335

Nonparametric evaluation of quantitative traits in population-based association studies when the genetic model is unknown.

PubMed

Konietschke, Frank; Libiger, Ondrej; Hothorn, Ludwig A

2012-01-01

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.

Modeling the Learner in Computer-Assisted Instruction

ERIC Educational Resources Information Center

Fletcher, J. D.

1975-01-01

This paper briefly reviews relevant work in four areas: 1) quantitative models of memory; 2) regression models of performance; 3) automation models of performance; and 4) artificial intelligence. (Author/HB)

Aberrant gene promoter methylation associated with sporadic multiple colorectal cancer.

PubMed

Gonzalo, Victoria; Lozano, Juan José; Muñoz, Jenifer; Balaguer, Francesc; Pellisé, Maria; Rodríguez de Miguel, Cristina; Andreu, Montserrat; Jover, Rodrigo; Llor, Xavier; Giráldez, M Dolores; Ocaña, Teresa; Serradesanferm, Anna; Alonso-Espinaco, Virginia; Jimeno, Mireya; Cuatrecasas, Miriam; Sendino, Oriol; Castellví-Bel, Sergi; Castells, Antoni

2010-01-19

Colorectal cancer (CRC) multiplicity has been mainly related to polyposis and non-polyposis hereditary syndromes. In sporadic CRC, aberrant gene promoter methylation has been shown to play a key role in carcinogenesis, although little is known about its involvement in multiplicity. To assess the effect of methylation in tumor multiplicity in sporadic CRC, hypermethylation of key tumor suppressor genes was evaluated in patients with both multiple and solitary tumors, as a proof-of-concept of an underlying epigenetic defect. We examined a total of 47 synchronous/metachronous primary CRC from 41 patients, and 41 gender, age (5-year intervals) and tumor location-paired patients with solitary tumors. Exclusion criteria were polyposis syndromes, Lynch syndrome and inflammatory bowel disease. DNA methylation at the promoter region of the MGMT, CDKN2A, SFRP1, TMEFF2, HS3ST2 (3OST2), RASSF1A and GATA4 genes was evaluated by quantitative methylation specific PCR in both tumor and corresponding normal appearing colorectal mucosa samples. Overall, patients with multiple lesions exhibited a higher degree of methylation in tumor samples than those with solitary tumors regarding all evaluated genes. After adjusting for age and gender, binomial logistic regression analysis identified methylation of MGMT2 (OR, 1.48; 95% CI, 1.10 to 1.97; p = 0.008) and RASSF1A (OR, 2.04; 95% CI, 1.01 to 4.13; p = 0.047) as variables independently associated with tumor multiplicity, being the risk related to methylation of any of these two genes 4.57 (95% CI, 1.53 to 13.61; p = 0.006). Moreover, in six patients in whom both tumors were available, we found a correlation in the methylation levels of MGMT2 (r = 0.64, p = 0.17), SFRP1 (r = 0.83, 0.06), HPP1 (r = 0.64, p = 0.17), 3OST2 (r = 0.83, p = 0.06) and GATA4 (r = 0.6, p = 0.24). Methylation in normal appearing colorectal mucosa from patients with multiple and solitary CRC showed no relevant difference in any evaluated gene. These results provide a proof-of-concept that gene promoter methylation is associated with tumor multiplicity. This underlying epigenetic defect may have noteworthy implications in the prevention of patients with sporadic CRC.

Association of Emotional Labor and Occupational Stressors with Depressive Symptoms among Women Sales Workers at a Clothing Shopping Mall in the Republic of Korea: A Cross-Sectional Study

PubMed Central

Chung, Yuh-Jin; Jung, Woo-Chul

2017-01-01

In the distribution service industry, sales people often experience multiple occupational stressors such as excessive emotional labor, workplace mistreatment, and job insecurity. The present study aimed to explore the associations of these stressors with depressive symptoms among women sales workers at a clothing shopping mall in Korea. A cross sectional study was conducted on 583 women who consist of clothing sales workers and manual workers using a structured questionnaire to assess demographic factors, occupational stressors, and depressive symptoms. Multiple regression analyses were performed to explore the association of these stressors with depressive symptoms. Scores for job stress subscales such as job demand, job control, and job insecurity were higher among sales workers than among manual workers (p < 0.01). The multiple regression analysis revealed the association between occupation and depressive symptoms after controlling for age, educational level, cohabiting status, and occupational stressors (sβ = 0.08, p = 0.04). A significant interaction effect between occupation and social support was also observed in this model (sβ = −0.09, p = 0.02). The multiple regression analysis stratified by occupation showed that job demand, job insecurity, and workplace mistreatment were significantly associated with depressive symptoms in both occupations (p < 0.05), although the strength of statistical associations were slightly different. We found negative associations of social support (sβ = −0.22, p < 0.01) and emotional effort (sβ = −0.17, p < 0.01) with depressive symptoms in another multiple regression model for sales workers. Emotional dissonance (sβ = 0.23, p < 0.01) showed positive association with depressive symptoms in this model. The result of this study indicated that reducing occupational stressors would be effective for women sales workers to prevent depressive symptoms. In particular, promoting social support could be the most effective way to promote women sales workers’ mental health. PMID:29168777

Association of Emotional Labor and Occupational Stressors with Depressive Symptoms among Women Sales Workers at a Clothing Shopping Mall in the Republic of Korea: A Cross-Sectional Study.

PubMed

Chung, Yuh-Jin; Jung, Woo-Chul; Kim, Hyunjoo; Cho, Seong-Sik

2017-11-23

In the distribution service industry, sales people often experience multiple occupational stressors such as excessive emotional labor, workplace mistreatment, and job insecurity. The present study aimed to explore the associations of these stressors with depressive symptoms among women sales workers at a clothing shopping mall in Korea. A cross sectional study was conducted on 583 women who consist of clothing sales workers and manual workers using a structured questionnaire to assess demographic factors, occupational stressors, and depressive symptoms. Multiple regression analyses were performed to explore the association of these stressors with depressive symptoms. Scores for job stress subscales such as job demand, job control, and job insecurity were higher among sales workers than among manual workers ( p < 0.01). The multiple regression analysis revealed the association between occupation and depressive symptoms after controlling for age, educational level, cohabiting status, and occupational stressors (sβ = 0.08, p = 0.04). A significant interaction effect between occupation and social support was also observed in this model (sβ = -0.09, p = 0.02). The multiple regression analysis stratified by occupation showed that job demand, job insecurity, and workplace mistreatment were significantly associated with depressive symptoms in both occupations ( p < 0.05), although the strength of statistical associations were slightly different. We found negative associations of social support (sβ = -0.22, p < 0.01) and emotional effort (sβ = -0.17, p < 0.01) with depressive symptoms in another multiple regression model for sales workers. Emotional dissonance (sβ = 0.23, p < 0.01) showed positive association with depressive symptoms in this model. The result of this study indicated that reducing occupational stressors would be effective for women sales workers to prevent depressive symptoms. In particular, promoting social support could be the most effective way to promote women sales workers' mental health.

Quality and relevance of master degree education for the professional development of nurses and midwives.

PubMed

Massimi, Azzurra; Marzuillo, Carolina; Di Muzio, Marco; Vacchio, Maria Rosaria; D'Andrea, Elvira; Villari, Paolo; De Vito, Corrado

2017-06-01

Advanced education in nursing is essential to provide safe, high quality and efficient health services in line with population needs. However, there is an almost complete lack of studies on how nurses view the usefulness of post-graduate education for their current employment and for professional advancement. To evaluate how nurse graduates view the quality, relevance and applicability of the knowledge and skills acquired during the Master of Science in Nursing (MSN) degree. Multicentre cross-sectional study. A multicenter cross-sectional study was carried out through an online questionnaire mailed (July 2014-June 2015) to 560 nurses who obtained the MSN degree from 23 Italian universities in the academic year 2010-2011. A total of 426 nurses completed the survey (response rate 76.1%), 80% of whom believed they had acquired knowledge and skills useful in their professional life after graduation. A multiple logistic regression model highlighted the characteristics of nurse graduates who judged the master's course relevant for their present role. In brief, they are expert nurses (OR=3.41, 95% CI=1.54-7.54) who achieved professional growth after the course (OR=5.25, 95% CI=2.67-10.33) and who judged the course very good or excellent (OR=2.16, 95% CI=1.04-4.52). Only 8% of the respondents achieved a full professional growth after the course. In Italy, MSN courses are able to provide a high level of skills and competencies. However, given the low rate of professional growth after the course, specific policies should increase the employment rates of new master's graduate nurses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Determinants of knowledge translation from health technology assessment to policy-making in China: From the perspective of researchers.

PubMed

Liu, Wenbin; Shi, Lizheng; Pong, Raymond W; Dong, Hengjin; Mao, Yiwei; Tang, Meng; Chen, Yingyao

2018-01-01

For health technology assessment (HTA) to be more policy relevant and for health technology-related decision-making to be truly evidence-based, promoting knowledge translation (KT) is of vital importance. Although some research has focused on KT of HTA, there is a dearth of literature on KT determinants and the situation in developing countries and transitional societies remains largely unknown. To investigate the determinants of HTA KT from research to health policy-making from the perspective of researchers in China. Cross-sectional study. A structured questionnaire which focused on KT was distributed to HTA researchers in China. KT activity levels in various fields of HTA research were compared, using one-way ANOVA. Principal component analysis was performed to provide a basis to combine similar variables. To investigate the determinants of KT level, multiple linear regression analysis was performed. Based on a survey of 382 HTA researchers, it was found that HTA KT wasn't widespread in China. Furthermore, results showed that no significant differences existed between the various HTA research fields. Factors, such as attitudes of researchers toward HTA and evidence utilization, academic ranks and linkages between researchers and policy-makers, had significant impact on HTA KT (p-values<0.05). Additionally, collaboration between HTA researchers and policy-makers, policy-relevance of HTA research, practicality of HTA outcomes and making HTA reports easier to understand also contributed to predicting KT level. However, academic nature of HTA research was negatively associated with KT level. KT from HTA to policy-making was influenced by many factors. Of particular importance were collaborations between researchers and policy-makers, ensuring policy relevance of HTA and making HTA evidence easier to understand by potential users.

Determinants of knowledge translation from health technology assessment to policy-making in China: From the perspective of researchers

PubMed Central

Liu, Wenbin; Shi, Lizheng; Pong, Raymond W.; Dong, Hengjin; Mao, Yiwei; Tang, Meng; Chen, Yingyao

2018-01-01

Background For health technology assessment (HTA) to be more policy relevant and for health technology-related decision-making to be truly evidence-based, promoting knowledge translation (KT) is of vital importance. Although some research has focused on KT of HTA, there is a dearth of literature on KT determinants and the situation in developing countries and transitional societies remains largely unknown. Objective To investigate the determinants of HTA KT from research to health policy-making from the perspective of researchers in China. Design Cross-sectional study. Methods A structured questionnaire which focused on KT was distributed to HTA researchers in China. KT activity levels in various fields of HTA research were compared, using one-way ANOVA. Principal component analysis was performed to provide a basis to combine similar variables. To investigate the determinants of KT level, multiple linear regression analysis was performed. Results Based on a survey of 382 HTA researchers, it was found that HTA KT wasn’t widespread in China. Furthermore, results showed that no significant differences existed between the various HTA research fields. Factors, such as attitudes of researchers toward HTA and evidence utilization, academic ranks and linkages between researchers and policy-makers, had significant impact on HTA KT (p-values<0.05). Additionally, collaboration between HTA researchers and policy-makers, policy-relevance of HTA research, practicality of HTA outcomes and making HTA reports easier to understand also contributed to predicting KT level. However, academic nature of HTA research was negatively associated with KT level. Conclusion KT from HTA to policy-making was influenced by many factors. Of particular importance were collaborations between researchers and policy-makers, ensuring policy relevance of HTA and making HTA evidence easier to understand by potential users. PMID:29300753

Effects of Minority Stress, Group-Level Coping, and Social Support on Mental Health of German Gay Men

PubMed Central

Sattler, Frank A.; Wagner, Ulrich; Christiansen, Hanna

2016-01-01

Objective According to epidemiological studies, gay men are at a higher risk of mental disorders than heterosexual men. In the current study, the minority stress theory was investigated in German gay men: 1) it was hypothesized that minority stressors would positively predict mental health problems and that 2) group-level coping and social support variables would moderate these predictions negatively. Methods Data from 1,188 German self-identified gay men were collected online. The questionnaire included items about socio-demographics, minority stress (victimization, rejection sensitivity, and internalized homonegativity), group-level coping (disclosure of sexual orientation, homopositivity, gay affirmation, gay rights support, and gay rights activism), and social support (gay social support and non-gay social support). A moderated multiple regression was conducted. Results Minority stressors positively predicted mental health problems. Group-level coping did not interact with minority stressors, with the exception of disclosure and homopositivity interacting marginally with some minority stressors. Further, only two interactions were found for social support variables and minority stress, one of them marginal. Gay and non-gay social support inversely predicted mental health problems. In addition, disclosure and homopositivity marginally predicted mental health problems. Conclusions The findings imply that the minority stress theory should be modified. Disclosure does not have a relevant effect on mental health, while social support variables directly influence mental health of gay men. Group-level coping does not interact with minority stressors relevantly, and only one relevant interaction between social support and minority stress was found. Further longitudinal or experimental replication is needed before transferring the results to mental health interventions and prevention strategies for gay men. PMID:26943785

Effects of Minority Stress, Group-Level Coping, and Social Support on Mental Health of German Gay Men.

PubMed

Sattler, Frank A; Wagner, Ulrich; Christiansen, Hanna

2016-01-01

According to epidemiological studies, gay men are at a higher risk of mental disorders than heterosexual men. In the current study, the minority stress theory was investigated in German gay men: 1) it was hypothesized that minority stressors would positively predict mental health problems and that 2) group-level coping and social support variables would moderate these predictions negatively. Data from 1,188 German self-identified gay men were collected online. The questionnaire included items about socio-demographics, minority stress (victimization, rejection sensitivity, and internalized homonegativity), group-level coping (disclosure of sexual orientation, homopositivity, gay affirmation, gay rights support, and gay rights activism), and social support (gay social support and non-gay social support). A moderated multiple regression was conducted. Minority stressors positively predicted mental health problems. Group-level coping did not interact with minority stressors, with the exception of disclosure and homopositivity interacting marginally with some minority stressors. Further, only two interactions were found for social support variables and minority stress, one of them marginal. Gay and non-gay social support inversely predicted mental health problems. In addition, disclosure and homopositivity marginally predicted mental health problems. The findings imply that the minority stress theory should be modified. Disclosure does not have a relevant effect on mental health, while social support variables directly influence mental health of gay men. Group-level coping does not interact with minority stressors relevantly, and only one relevant interaction between social support and minority stress was found. Further longitudinal or experimental replication is needed before transferring the results to mental health interventions and prevention strategies for gay men.