The role of action planning and plan enactment for smoking cessation

PubMed Central

2013-01-01

Background Several studies have reemphasized the role of action planning. Yet, little attention has been paid to the role of plan enactment. This study assesses the determinants and the effects of action planning and plan enactment on smoking cessation. Methods One thousand and five participants completed questionnaires at baseline and at follow-ups after one and six months. Factors queried were part of the I-Change model. Descriptive analyses were used to assess which plans were enacted the most. Multivariate linear regression analyses were used to assess whether the intention to quit smoking predicted action planning and plan enactment, and to assess which factors would predict quitting behavior. Subsequently, both multivariate and univariate regression analyses were used to assess which particular action plans would be most effective in predicting quitting behavior. Similar analyses were performed among a subsample of smokers prepared to quit within one month. Results Smokers who intended to quit smoking within the next month had higher levels of action planning than those intending to quit within a year. Additional predictors of action planning were being older, being female, having relatively low levels of cigarette dependence, perceiving more positive and negative consequences of quitting, and having high self-efficacy toward quitting. Plan enactment was predicted by baseline intention to quit and levels of action planning. Regression analysis revealed that smoking cessation after six months was predicted by low levels of depression, having a non-smoking partner, the intention to quit within the next month, and plan enactment. Only 29% of the smokers who executed relatively few plans had quit smoking versus 59% of the smokers who executed many plans. The most effective preparatory plans for smoking cessation were removing all tobacco products from the house and choosing a specific date to quit. Conclusion Making preparatory plans to quit smoking is important because it also predicts plan enactment, which is predictive of smoking cessation. Not all action plans were found to be predictive of smoking cessation. The effects of planning were not very much different between the total sample and smokers prepared to quit within one month. PMID:23622256

Statistical Prediction in Proprietary Rehabilitation.

ERIC Educational Resources Information Center

Johnson, Kurt L.; And Others

1987-01-01

Applied statistical methods to predict case expenditures for low back pain rehabilitation cases in proprietary rehabilitation. Extracted predictor variables from case records of 175 workers compensation claimants with some degree of permanent disability due to back injury. Performed several multiple regression analyses resulting in a formula that…

Ideal Teacher Behaviors: Student Motivation and Self-Efficacy Predict Preferences

ERIC Educational Resources Information Center

Komarraju, Meera

2013-01-01

Differences in students' academic self-efficacy and motivation were examined in predicting preferred teacher traits. Undergraduates (261) completed the Teaching Behavior Checklist, Academic Self-Concept scale, and Academic Motivation scale. Hierarchical regression analyses indicated that academic self-efficacy and extrinsic motivation explained…

Prediction of Advisability of Returning Home Using the Home Care Score

PubMed Central

Matsugi, Akiyoshi; Tani, Keisuke; Tamaru, Yoshiki; Yoshioka, Nami; Yamashita, Akira; Mori, Nobuhiko; Oku, Kosuke; Ikeda, Masashi; Nagano, Kiyoshi

2015-01-01

Purpose. The aim of this study was to assess whether the home care score (HCS), which was developed by the Ministry of Health and Welfare in Japan in 1992, is useful for the prediction of advisability of home care. Methods. Subjects living at home and in assisted-living facilities were analyzed. Binominal logistic regression analyses, using age, sex, the functional independence measure score, and the HCS, along with receiver operating characteristic curve analyses, were conducted. Findings/Conclusions. Only HCS was selected for the regression equation. Receiver operating characteristic curve analysis revealed that the area under the curve (0.9), sensitivity (0.82), specificity (0.83), and positive predictive value (0.84) for HCS were higher than those for the functional independence measure, indicating that the HCS is a powerful predictor for advisability of home care. Clinical Relevance. Comprehensive measurements of the condition of provided care and the activities of daily living of the subjects, which are included in the HCS, are required for the prediction of advisability of home care. PMID:26491568

Prediction models for Arabica coffee beverage quality based on aroma analyses and chemometrics.

PubMed

Ribeiro, J S; Augusto, F; Salva, T J G; Ferreira, M M C

2012-11-15

In this work, soft modeling based on chemometric analyses of coffee beverage sensory data and the chromatographic profiles of volatile roasted coffee compounds is proposed to predict the scores of acidity, bitterness, flavor, cleanliness, body, and overall quality of the coffee beverage. A partial least squares (PLS) regression method was used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the compounds for the regression model of each sensory attribute in order to take only significant chromatographic peaks into account. The prediction errors of these models, using 4 or 5 latent variables, were equal to 0.28, 0.33, 0.35, 0.33, 0.34 and 0.41, for each of the attributes and compatible with the errors of the mean scores of the experts. Thus, the results proved the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of Brazilian Arabica coffee. Copyright © 2012 Elsevier B.V. All rights reserved.

Exploring the links between personality traits and motivations to play online games.

PubMed

Park, Jowon; Song, Yosep; Teng, Ching-I

2011-12-01

The present study explores the links between personality traits and motivations to play online games. We identified the underlying dimensions of motivations to play online games, examined how personality traits predict motivation, and investigated how personality traits predict online gaming behavior (i.e., playing time and preference for game genres). Factor analyses identified five motivational factors: relationships, adventure, escapism, relaxation, and achievement. The regression analyses indicated that two personality traits, extraversion and agreeableness, predicted various motivations; however, personality traits did not affect the playing time and game genre preference.

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

PubMed

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE < 10.3%. The external data evaluation showed that the models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

VBM-DTI correlates of verbal intelligence: a potential link to Broca's area.

PubMed

Konrad, Andreas; Vucurevic, Goran; Musso, Francesco; Winterer, Georg

2012-04-01

Human brain lesion studies first investigated the biological roots of cognitive functions including language in the late 1800s. Neuroimaging studies have reported correlation findings with general intelligence predominantly in fronto-parietal cortical areas. However, there is still little evidence about the relationship between verbal intelligence and structural properties of the brain. We predicted that verbal performance is related to language regions of Broca's and Wernicke's areas. Verbal intelligence quotient (vIQ) was assessed in 30 healthy young subjects. T1-weighted MRI and diffusion tensor imaging data sets were acquired. Voxel-wise regression analyses were used to correlate fractional anisotropy (FA) and mean diffusivity values with vIQ. Moreover, regression analyses of regional brain volume with vIQ were performed adopting voxel-based morphometry (VBM) and ROI methodology. Our analyses revealed a significant negative correlation between vIQ and FA and a significant positive correlation between vIQ and mean diffusivity in the left-hemispheric Broca's area. VBM regression analyses did not show significant results, whereas a subsequent ROI analysis of Broca's area FA peak cluster demonstrated a positive correlation of gray matter volume and vIQ. These findings suggest that cortical thickness in Broca's area contributes to verbal intelligence. Diffusion parameters predicted gray matter ratio in Broca's area more sensitive than VBM methodology.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Predicting Patient Advocacy Engagement: A Multiple Regression Analysis Using Data From Health Professionals in Acute-Care Hospitals.

PubMed

Jansson, Bruce S; Nyamathi, Adeline; Heidemann, Gretchen; Duan, Lei; Kaplan, Charles

2015-01-01

Although literature documents the need for hospital social workers, nurses, and medical residents to engage in patient advocacy, little information exists about what predicts the extent they do so. This study aims to identify predictors of health professionals' patient advocacy engagement with respect to a broad range of patients' problems. A cross-sectional research design was employed with a sample of 94 social workers, 97 nurses, and 104 medical residents recruited from eight hospitals in Los Angeles. Bivariate correlations explored whether seven scales (Patient Advocacy Eagerness, Ethical Commitment, Skills, Tangible Support, Organizational Receptivity, Belief Other Professionals Engage, and Belief the Hospital Empowers Patients) were associated with patient advocacy engagement, measured by the validated Patient Advocacy Engagement Scale. Regression analysis examined whether these scales, when controlling for sociodemographic and setting variables, predicted patient advocacy engagement. While all seven predictor scales were significantly associated with patient advocacy engagement in correlational analyses, only Eagerness, Skills, and Belief the Hospital Empowers Patients predicted patient advocacy engagement in regression analyses. Additionally, younger professionals engaged in higher levels of patient advocacy than older professionals, and social workers engaged in greater patient advocacy than nurses. Limitations and the utility of these findings for acute-care hospitals are discussed.

Retention of community college students in online courses

NASA Astrophysics Data System (ADS)

Krajewski, Sarah

The issue of attrition in online courses at higher learning institutions remains a high priority in the United States. A recent rapid growth of online courses at community colleges has been instigated by student demand, as they meet the time constraints many nontraditional community college students have as a result of the need to work and care for dependents. Failure in an online course can cause students to become frustrated with the college experience, financially burdened, or to even give up and leave college. Attrition could be avoided by proper guidance of who is best suited for online courses. This study examined factors related to retention (i.e., course completion) and success (i.e., receiving a C or better) in an online biology course at a community college in the Midwest by operationalizing student characteristics (age, race, gender), student skills (whether or not the student met the criteria to be placed in an AFP course), and external factors (Pell recipient, full/part time status, first term) from the persistence model developed by Rovai. Internal factors from this model were not included in this study. Both univariate analyses and multivariate logistic regression were used to analyze the variables. Results suggest that race and Pell recipient were both predictive of course completion on univariate analyses. However, multivariate analyses showed that age, race, academic load and first term were predictive of completion and Pell recipient was no longer predictive. The univariate results for the C or better showed that age, race, Pell recipient, academic load, and meeting AFP criteria were predictive of success. Multivariate analyses showed that only age, race, and Pell recipient were significant predictors of success. Both regression models explained very little (<15%) of the variability within the outcome variables of retention and success. Therefore, although significant predictors were identified for course completion and retention, there are still many factors that remain unaccounted for in both regression models. Further research into the operationalization of Rovai's model, including internal factors, to predict completion and success is necessary.

Predicting South Korean University Students' Happiness through Social Support and Efficacy Beliefs

ERIC Educational Resources Information Center

Lee, Diane Sookyoung; Padilla, Amado M.

2016-01-01

This study investigated the adversity and coping experiences of 198 South Korean university students and takes a cultural lens in understanding how social and individual factors shape their happiness. Hierarchical linear regression analyses suggest that Korean students' perceptions of social support significantly predicted their happiness,…

Developmental Screening Referrals: Child and Family Factors that Predict Referral Completion

ERIC Educational Resources Information Center

Jennings, Danielle J.; Hanline, Mary Frances

2013-01-01

This study researched the predictive impact of developmental screening results and the effects of child and family characteristics on completion of referrals given for evaluation. Logistical and hierarchical logistic regression analyses were used to determine the significance of 10 independent variables on the predictor variable. The number of…

The Predictive Value of Selection Criteria in an Urban Magnet School

ERIC Educational Resources Information Center

Lohmeier, Jill Hendrickson; Raad, Jennifer

2012-01-01

The predictive value of selection criteria on outcome data from two cohorts of students (Total N = 525) accepted to an urban magnet high school were evaluated. Regression analyses of typical screening variables (suspensions, absences, metropolitan achievement tests, middle school grade point averages [GPAs], Matrix Analogies test scores, and…

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

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

PubMed

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

2016-02-01

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

Space shuttle propulsion parameter estimation using optional estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

A regression analyses on tabular aerodynamic data provided. A representative aerodynamic model for coefficient estimation. It also reduced the storage requirements for the "normal' model used to check out the estimation algorithms. The results of the regression analyses are presented. The computer routines for the filter portion of the estimation algorithm and the :"bringing-up' of the SRB predictive program on the computer was developed. For the filter program, approximately 54 routines were developed. The routines were highly subsegmented to facilitate overlaying program segments within the partitioned storage space on the computer.

Using Faculty Characteristics to Predict Attitudes toward Developmental Education

ERIC Educational Resources Information Center

Sides, Meredith Louise Carr

2017-01-01

The study adapted Astin's I-E-O model and utilized multiple regression analyses to predict faculty attitudes toward developmental education. The study utilized a cross-sectional survey design to survey faculty members at 27 different higher education institutions in the state of Alabama. The survey instrument was a self-designed questionnaire that…

Predicting postfire Douglas-fir beetle attacks and tree mortality in the northern Rocky Mountains

Treesearch

Sharon Hood; Barbara Bentz

2007-01-01

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) were monitored for 4 years following three wildfires. Logistic regression analyses were used to develop models predicting the probability of attack by Douglas-fir beetle (Dendroctonus pseudotsugae Hopkins, 1905) and the probability of Douglas-fir mortality within 4 years following...

The Role of Goal Importance in Predicting University Students' High Academic Performance

ERIC Educational Resources Information Center

Kyle, Vanessa A.; White, Katherine M.; Hyde, Melissa K.; Occhipinti, Stefano

2014-01-01

We examined goal importance, focusing on high, but not exclusive priority goals, in the theory of planned behaviour (TPB) to predict students' academic performance. At the beginning of semester, students in a psychology subject (N = 197) completed TPB and goal importance items for achieving a high grade. Regression analyses revealed partial…

Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

PubMed Central

O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

2011-01-01

Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

Resource predictability and specialization in avian malaria parasites.

PubMed

Svensson-Coelho, Maria; Loiselle, Bette A; Blake, John G; Ricklefs, Robert E

2016-09-01

We tested the hypothesis that avian haemosporidian (malaria) parasites specialize on hosts that can be characterized as predictable resources at a site in Amazonian Ecuador. We incorporated host phylogenetic relationship and relative abundance in assessing parasite specialization, and we examined associations between parasite specialization and three host characteristics - abundance, mass and longevity - using quantile regression, phylogenetic logistic regression and t-tests. Hosts of specialist malaria parasite lineages were on average more abundant than hosts of generalist parasite lineages, but the relationship between host abundance and parasite specialization was not consistent across analyses. We also found support for a positive association between parasite specialization and host longevity, but this also was not consistent across analyses. Nonetheless, our findings suggest that the predictability of a host resource may play a role in the evolution of specialization. However, we also discuss two alternative explanations to the resource predictability hypothesis for specialization: (i) that interspecific interactions among the parasites themselves might constrain some parasites to a specialist strategy, and (ii) that frequent encounters with multiple host species, mediated by blood-sucking insects, might promote generalization within this system. © 2016 John Wiley & Sons Ltd.

Regression Analyses on the Butterfly Ballot Effect: A Statistical Perspective of the US 2000 Election

ERIC Educational Resources Information Center

Wu, Dane W.

2002-01-01

The year 2000 US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction (or confidence) intervals for least squares regression lines…

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

Role of subdural electrocorticography in prediction of long-term seizure outcome in epilepsy surgery

PubMed Central

Juhász, Csaba; Shah, Aashit; Sood, Sandeep; Chugani, Harry T.

2009-01-01

Since prediction of long-term seizure outcome using preoperative diagnostic modalities remains suboptimal in epilepsy surgery, we evaluated whether interictal spike frequency measures obtained from extraoperative subdural electrocorticography (ECoG) recording could predict long-term seizure outcome. This study included 61 young patients (age 0.4–23.0 years), who underwent extraoperative ECoG recording prior to cortical resection for alleviation of uncontrolled focal seizures. Patient age, frequency of preoperative seizures, neuroimaging findings, ictal and interictal ECoG measures were preoperatively obtained. The seizure outcome was prospectively measured [follow-up period: 2.5–6.4 years (mean 4.6 years)]. Univariate and multivariate logistic regression analyses determined how well preoperative demographic and diagnostic measures predicted long-term seizure outcome. Following the initial cortical resection, Engel Class I, II, III and IV outcomes were noted in 35, 6, 12 and 7 patients, respectively. One child died due to disseminated intravascular coagulation associated with pseudomonas sepsis 2 days after surgery. Univariate regression analyses revealed that incomplete removal of seizure onset zone, higher interictal spike-frequency in the preserved cortex and incomplete removal of cortical abnormalities on neuroimaging were associated with a greater risk of failing to obtain Class I outcome. Multivariate logistic regression analysis revealed that incomplete removal of seizure onset zone was the only independent predictor of failure to obtain Class I outcome. The goodness of regression model fit and the predictive ability of regression model were greatest in the full regression model incorporating both ictal and interictal measures [R2 0.44; Area under the receiver operating characteristic (ROC) curve: 0.81], slightly smaller in the reduced model incorporating ictal but not interictal measures (R2 0.40; Area under the ROC curve: 0.79) and slightly smaller again in the reduced model incorporating interictal but not ictal measures (R2 0.27; Area under the ROC curve: 0.77). Seizure onset zone and interictal spike frequency measures on subdural ECoG recording may both be useful in predicting the long-term seizure outcome of epilepsy surgery. Yet, the additive clinical impact of interictal spike frequency measures to predict long-term surgical outcome may be modest in the presence of ictal ECoG and neuroimaging data. PMID:19286694

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

PubMed

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Prediction by regression and intrarange data scatter in surface-process studies

USGS Publications Warehouse

Toy, T.J.; Osterkamp, W.R.; Renard, K.G.

1993-01-01

Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies. ?? 1993 Springer-Verlag.

Mobility, Fertility, and Residential Crowding

ERIC Educational Resources Information Center

Morris, Earl W.

1977-01-01

Regression analyses predicting fertility and mobility in a sample of a metropolitan county in New York State indicate that residential mobility serves to release the negative pressure that residential crowding might exert on fertility behavior. (Author)

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

PubMed

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

2009-10-01

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

The six-minute walk test predicts cardiorespiratory fitness in individuals with aneurysmal subarachnoid hemorrhage.

PubMed

Harmsen, Wouter J; Ribbers, Gerard M; Slaman, Jorrit; Heijenbrok-Kal, Majanka H; Khajeh, Ladbon; van Kooten, Fop; Neggers, Sebastiaan J C M M; van den Berg-Emons, Rita J

2017-05-01

Peak oxygen uptake (VO 2peak ) established during progressive cardiopulmonary exercise testing (CPET) is the "gold-standard" for cardiorespiratory fitness. However, CPET measurements may be limited in patients with aneurysmal subarachnoid hemorrhage (a-SAH) by disease-related complaints, such as cardiovascular health-risks or anxiety. Furthermore, CPET with gas-exchange analyses require specialized knowledge and infrastructure with limited availability in most rehabilitation facilities. To determine whether an easy-to-administer six-minute walk test (6MWT) is a valid clinical alternative to progressive CPET in order to predict VO 2peak in individuals with a-SAH. Twenty-seven patients performed the 6MWT and CPET with gas-exchange analyses on a cycle ergometer. Univariate and multivariate regression models were made to investigate the predictability of VO 2peak from the six-minute walk distance (6MWD). Univariate regression showed that the 6MWD was strongly related to VO 2peak (r = 0.75, p < 0.001), with an explained variance of 56% and a prediction error of 4.12 ml/kg/min, representing 18% of mean VO 2peak . Adding age and sex to an extended multivariate regression model improved this relationship (r = 0.82, p < 0.001), with an explained variance of 67% and a prediction error of 3.67 ml/kg/min corresponding to 16% of mean VO 2peak . The 6MWT is an easy-to-administer submaximal exercise test that can be selected to estimate cardiorespiratory fitness at an aggregated level, in groups of patients with a-SAH, which may help to evaluate interventions in a clinical or research setting. However, the relatively large prediction error does not allow for an accurate prediction in individual patients.

Comparison of the Incremental Validity of the Old and New MCAT.

ERIC Educational Resources Information Center

Wolf, Fredric M.; And Others

The predictive and incremental validity of both the Old and New Medical College Admission Test (MCAT) was examined and compared with a sample of over 300 medical students. Results of zero order and incremental validity coefficients, as well as prediction models resulting from all possible subsets regression analyses using Mallow's Cp criterion,…

Person-Environment Congruence and Personality Domains in the Prediction of Job Performance and Work Quality

ERIC Educational Resources Information Center

Kieffer, Kevin M.; Schinka, John A.; Curtiss, Glenn

2004-01-01

This study examined the contributions of the 5-Factor Model (FFM; P. T. Costa & R. R. McCrae, 1992) and RIASEC (J. L. Holland, 1994) constructs of consistency, differentiation, and person-environment congruence in predicting job performance ratings in a large sample (N = 514) of employees. Hierarchical regression analyses conducted separately by…

Predicting Negative Discipline in Traditional Families: A Multi-Dimensional Stress Model.

ERIC Educational Resources Information Center

Fisher, Philip A.

An attempt is made to integrate existing theories of family violence by introducing the concept of family role stress. Role stressors may be defined as factors inhibiting the enactment of family roles. Multiple regression analyses were performed on data from 190 families to test a hypothesis involving the prediction of negative discipline at…

On the Validity of Validity Scales: The Importance of Defensive Responding in the Prediction of Institutional Misconduct

ERIC Educational Resources Information Center

Edens, John F.; Ruiz, Mark A.

2006-01-01

This study examined the effects of defensive responding on the prediction of institutional misconduct among male inmates (N = 349) who completed the Personality Assessment Inventory (L. C. Morey, 1991). Hierarchical logistic regression analyses demonstrated significant main effects for the Antisocial Features (ANT) scale as well as main effects…

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.

Escherichia coli bacteria density in relation to turbidity, streamflow characteristics, and season in the Chattahoochee River near Atlanta, Georgia, October 2000 through September 2008—Description, statistical analysis, and predictive modeling

USGS Publications Warehouse

Lawrence, Stephen J.

2012-01-01

Regression analyses show that E. coli density in samples was strongly related to turbidity, streamflow characteristics, and season at both sites. The regression equation chosen for the Norcross data showed that 78 percent of the variability in E. coli density (in log base 10 units) was explained by the variability in turbidity values (in log base 10 units), streamflow event (dry-weather flow or stormflow), season (cool or warm), and an interaction term that is the cross product of streamflow event and turbidity. The regression equation chosen for the Atlanta data showed that 76 percent of the variability in E. coli density (in log base 10 units) was explained by the variability in turbidity values (in log base 10 units), water temperature, streamflow event, and an interaction term that is the cross product of streamflow event and turbidity. Residual analysis and model confirmation using new data indicated the regression equations selected at both sites predicted E. coli density within the 90 percent prediction intervals of the equations and could be used to predict E. coli density in real time at both sites.

Coming Out and the Potential for Growth in Sexual Minorities: The Role of Social Reactions and Internalized Homonegativity.

PubMed

Solomon, David; McAbee, James; Åsberg, Kia; McGee, Ashley

2015-01-01

Coming out is a significant and sometimes difficult process in the lives of sexual minorities, but disclosure can also affect wellbeing in positive ways, including reduced distress and greater relationship satisfaction. This study investigates the possibility of stress-related growth and depreciation following coming out. To obtain a diverse sample with varying coming-out experiences, data were collected from undergraduate students as well as from online sources, including lesbian, gay, and bisexual support groups and Pride groups. Regression analyses indicated that negative social reactions to coming out predicted both growth and depreciation, although they more strongly predicted depreciation. Positive social reactions were positively related to stress-related growth, while internalized homonegativity was inversely associated with growth. Although the two sample sources (online and campus) differed in some ways, sample source was not a significant predictor in the regressions, nor was it indicated as a moderator in exploratory ANOVA analyses.

Factors That Contribute to the Completion of Programs of Study at Arkansas Institutions of Higher Education for African American Males

ERIC Educational Resources Information Center

Petty, Barrett Wade McCoy

2015-01-01

The study examined factors that predicted the completion of programs of study at Arkansas institutions of higher education for African American males. Astin's (1993a) Input-Environment-Output (I-E-O) Model was used as the theoretical foundation. Descriptive analyses and hierarchical logistic regression analyses were performed on the data. The…

Parental education predicts change in intelligence quotient after childhood epilepsy surgery.

PubMed

Meekes, Joost; van Schooneveld, Monique M J; Braams, Olga B; Jennekens-Schinkel, Aag; van Rijen, Peter C; Hendriks, Marc P H; Braun, Kees P J; van Nieuwenhuizen, Onno

2015-04-01

To know whether change in the intelligence quotient (IQ) of children who undergo epilepsy surgery is associated with the educational level of their parents. Retrospective analysis of data obtained from a cohort of children who underwent epilepsy surgery between January 1996 and September 2010. We performed simple and multiple regression analyses to identify predictors associated with IQ change after surgery. In addition to parental education, six variables previously demonstrated to be associated with IQ change after surgery were included as predictors: age at surgery, duration of epilepsy, etiology, presurgical IQ, reduction of antiepileptic drugs, and seizure freedom. We used delta IQ (IQ 2 years after surgery minus IQ shortly before surgery) as the primary outcome variable, but also performed analyses with pre- and postsurgical IQ as outcome variables to support our findings. To validate the results we performed simple regression analysis with parental education as the predictor in specific subgroups. The sample for regression analysis included 118 children (60 male; median age at surgery 9.73 years). Parental education was significantly associated with delta IQ in simple regression analysis (p = 0.004), and also contributed significantly to postsurgical IQ in multiple regression analysis (p = 0.008). Additional analyses demonstrated that parental education made a unique contribution to prediction of delta IQ, that is, it could not be replaced by the illness-related variables. Subgroup analyses confirmed the association of parental education with IQ change after surgery for most groups. Children whose parents had higher education demonstrate on average a greater increase in IQ after surgery and a higher postsurgical--but not presurgical--IQ than children whose parents completed at most lower secondary education. Parental education--and perhaps other environmental variables--should be considered in the prognosis of cognitive function after childhood epilepsy surgery. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Predictors of Global Self-Worth and Academic Performance among Regular Education, Learning Disabled, and Continuation High School Students.

ERIC Educational Resources Information Center

Wiest, Dudley J.; Wong, Eugene H.; Kreil, Dennis A.

1998-01-01

The ability of measures of perceived competence, control, and autonomy support to predict self-worth and academic performance was studied across groups of high school students. Stepwise regression analyses indicate these variables in model predict self-worth and grade point average. In addition, levels of school status and depression predict…

Disentangling the Correlates of Drug Use in a Clinic and Community Sample: A Regression Analysis of the Associations between Drug Use, Years-of-School, Impulsivity, IQ, Working Memory, and Psychiatric Symptoms.

PubMed

Heyman, Gene M; Dunn, Brian J; Mignone, Jason

2014-01-01

Years-of-school is negatively correlated with illicit drug use. However, educational attainment is positively correlated with IQ and negatively correlated with impulsivity, two traits that are also correlated with drug use. Thus, the negative correlation between education and drug use may reflect the correlates of schooling, not schooling itself. To help disentangle these relations we obtained measures of working memory, simple memory, IQ, disposition (impulsivity and psychiatric status), years-of-school and frequency of illicit and licit drug use in methadone clinic and community drug users. We found strong zero-order correlations between all measures, including IQ, impulsivity, years-of-school, psychiatric symptoms, and drug use. However, multiple regression analyses revealed a different picture. The significant predictors of illicit drug use were gender, involvement in a methadone clinic, and years-of-school. That is, psychiatric symptoms, impulsivity, cognition, and IQ no longer predicted illicit drug use in the multiple regression analyses. Moreover, high risk subjects (low IQ and/or high impulsivity) who spent 14 or more years in school used stimulants and opiates less than did low risk subjects who had spent <14 years in school. Smoking and drinking had a different correlational structure. IQ and years-of-school predicted whether someone ever became a smoker, whereas impulsivity predicted the frequency of drinking bouts, but years-of-school did not. Many subjects reported no use of one or more drugs, resulting in a large number of "zeroes" in the data sets. Cragg's Double-Hurdle regression method proved the best approach for dealing with this problem. To our knowledge, this is the first report to show that years-of-school predicts lower levels of illicit drug use after controlling for IQ and impulsivity. This paper also highlights the advantages of Double-Hurdle regression methods for analyzing the correlates of drug use in community samples.

Body-density measurement in children: the BOD POD versus Hydrodensitometry.

PubMed

Holmes, Jason C; Gibson, Ann L; Cremades, J Gualberto; Mier, Constance M

2011-06-01

To compare estimates of body density (Db) from air-displacement plethysmography (ADP) with measured and predicted thoracic-gas-volume (TGV) measurements and those from hydrodensitometry (HD) in children. Seventeen participants (13 male and 4 female; 10.1 ± 2.20 yr, 42.0 ± 15.03 kg, 145.6 ± 17.41 cm, 30.0 ± 8.66 kg/m²) were tested using ADP and HD, with ADP always preceding HD. Db estimates were compared between ADP with measured TGV, ADP with predicted TGV, and the reference measure, HD. Regression analyses were used to assess the accuracy of the ADP methods, and potential bias between the ADP procedures and HD were evaluated using Bland-Altman analyses. The cross-validation criteria described by Lohman for estimating Db relative to HD were used to interpret the results of the study. A significant difference was found between Db estimates from ADP with measured TGV (1.0453 ± 0.01934 g/cm³) and ADP with predicted TGV (1.0415 ± 0.01858 g/cm³); however, neither was significantly different from Db obtained by the reference HD procedure (1.0417 ± 0.02391 g/cm³). For both ADP procedures, regression analyses produced an r = .737-.738, r² = .543-.544, and SEE = 0.02 g/cm³, and the regression lines deviated significantly from the line of identity; however, no significant biases were indicated. Despite no significant mean differences between Db estimates from the ADP procedures and HD, more cross-validation research is needed before recommending the BOD POD for routine use with children in clinical and research settings.

Effects of perceived stress and uplifts on inflammation and coagulability.

PubMed

Jain, Shamini; Mills, Paul J; von Känel, Roland; Hong, Suzi; Dimsdale, Joel E

2007-01-01

We investigated whether depressed mood and chronic hassles and uplifts predicted levels of hemostasis markers D-Dimer and type-1 plasminogen activator inhibitor (PAI-1), as well as the proinflammatory markers interleukin-6 (IL-6) and soluble intercellular adhesion molecule-1 (sICAM-1) in 108 healthy individuals. One hundred eight African-American and Euro-American men and women were studied (58 men, 50 women; mean age = 36.5 +/- 8 years). D-Dimer, PAI-1, IL-6, and sICAM-1 plasma levels were analyzed from fasting venous blood samples. Data were analyzed via hierarchical linear regression and followed with partial correlation analysis. Regression analyses combined with partial correlation analyses suggested that increases in hassle frequency predicted elevated levels of sICAM-1 (p= .034), and increases in hassle severity predicted elevated levels of D-Dimer (p= .017). Increases in uplift intensity predicted lower levels of PAI-1 (p= .004) as well as showed a trend for decreased IL-6 (p= .069). Depressed mood did not significantly predict any dependent variable. These results were independent of sociodemographic, biological, and other related mood variables. The findings suggest that for even relatively healthy persons, increased perceptions of hassles are independently associated with greater inflammation and hypercoagulability, whereas increased perceptions of uplifts are independently associated with decreased hypercoagulability.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

PubMed

Muhlestein, Whitney E; Akagi, Dallin S; Kallos, Justiss A; Morone, Peter J; Weaver, Kyle D; Thompson, Reid C; Chambless, Lola B

2018-04-01

Objective  Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods  A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results  Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p  = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion  Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

A Head-to-Head Comparison of the Personality Inventory for DSM-5 (PID-5) With the Personality Diagnostic Questionnaire-4 (PDQ-4) in Predicting the General Level of Personality Pathology Among Community Dwelling Subjects.

PubMed

Fossati, Andrea; Somma, Antonella; Borroni, Serena; Maffei, Cesare; Markon, Kristian E; Krueger, Robert F

2016-02-01

In order to evaluate if measures of DSM-5 Alternative PD Model domains predicted interview-based scores of general personality pathology when compared to self-report measures of DSM-IV Axis II/DSM-5 Section II PD criteria, 300 Italian community adults were administered the Iowa Personality Disorder Screen (IPDS) interview, the Personality Inventory for DSM-5 (PID-5), and the Personality Diagnostic Questionnaire-4+ (PDQ-4+). Multiple regression analyses showed that the five PID-5 domain scales collectively explained an adequate rate of the variance of the IPDS interview total score. This result was slightly lower than the amount of variance in the IPDS total score explained by the 10 PDQ-4+ scales. The PID-5 traits scales performed better than the PDQ-4+, although the difference was marginal. Hierarchical regression analyses revealed that the PID-5 domain and trait scales provided a moderate, but significant increase in the prediction of the general level of personality pathology above and beyond the PDQ-4+ scales.

Can the displacement of a conservatively treated distal radius fracture be predicted at the beginning of treatment?

PubMed Central

Einsiedel, T.; Freund, W.; Sander, S.; Trnavac, S.; Gebhard, F.

2008-01-01

The aim of this study was to investigate whether the final displacement of conservatively treated distal radius fractures can be predicted after primary reduction. We analysed the radiographic documents of 311 patients with a conservatively treated distal radius fracture at the time of injury, after reduction and after bony consolidation. We measured the dorsal angulation (DA), the radial angle (RA) and the radial shortening (RS) at each time point. The parameters were analysed separately for metaphyseally “stable” (A2, C1) and “unstable” (A3, C2, C3) fractures, according to the AO classification system. Spearman’s rank correlations and regression functions were determined for the analysis. The highest correlations were found for the DA between the time points ‘reduction’ and ‘complete healing’ (r = 0.75) and for the RA between the time points ‘reduction’ and ‘complete healing’ (r = 0.80). The DA and the RA after complete healing can be predicted from the regression functions. PMID:18504577

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

PubMed Central

Alexeeff, Stacey E.; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A.

2016-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies. PMID:24896768

Internal Accountability and District Achievement: How Superintendents Affect Student Learning

ERIC Educational Resources Information Center

Hough, Kimberly L.

2014-01-01

This quantitative survey study was designed to determine whether superintendent accountability behaviors or agreement about accountability behaviors between superintendents and their subordinate central office administrators predicted district student achievement. Hierarchical multiple regression and analyses of covariance were employed,…

Women, Physical Activity, and Quality of Life: Self-concept as a Mediator.

PubMed

Gonzalo Silvestre, Tamara; Ubillos Landa, Silvia

2016-02-22

The objectives of this research are: (a) analyze the incremental validity of physical activity's (PA) influence on perceived quality of life (PQL); (b) determine if PA's predictive power is mediated by self-concept; and (c) study if results vary according to a unidimensional or multidimensional approach to self-concept measurement. The sample comprised 160 women from Burgos, Spain aged 18 to 45 years old. Non-probability sampling was used. Two three-step hierarchical regression analyses were applied to forecast PQL. The hedonic quality-of-life indicators, self-concept, self-esteem, and PA were included as independent variables. The first regression analysis included global self-concept as predictor variable, while the second included its five dimensions. Two mediation analyses were conducted to see if PA's ability to predict PQL was mediated by global and physical self-concept. Results from the first regression shows that self-concept, satisfaction with life, and PA were significant predictors. PA slightly but significantly increased explained variance in PQL (2.1%). In the second regression, substituting global self-concept with its five constituent factors, only the physical dimension and satisfaction with life predicted PQL, while PA ceased to be a significant predictor. Mediation analysis revealed that only physical self-concept mediates the relationship between PA and PQL (z = 1.97, p < .050), and not global self-concept. Physical self-concept was the strongest predictor and approximately 32.45 % of PA's effect on PQL was mediated by it. This study's findings support a multidimensional view of self-concept, and represent a more accurate image of the relationship between PQL, PA, and self-concept.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

Reporting and methodological quality of meta-analyses in urological literature.

PubMed

Xia, Leilei; Xu, Jing; Guzzo, Thomas J

2017-01-01

To assess the overall quality of published urological meta-analyses and identify predictive factors for high quality. We systematically searched PubMed to identify meta-analyses published from January 1st, 2011 to December 31st, 2015 in 10 predetermined major paper-based urology journals. The characteristics of the included meta-analyses were collected, and their reporting and methodological qualities were assessed by the PRISMA checklist (27 items) and AMSTAR tool (11 items), respectively. Descriptive statistics were used for individual items as a measure of overall compliance, and PRISMA and AMSTAR scores were calculated as the sum of adequately reported domains. Logistic regression was used to identify predictive factors for high qualities. A total of 183 meta-analyses were included. The mean PRISMA and AMSTAR scores were 22.74 ± 2.04 and 7.57 ± 1.41, respectively. PRISMA item 5, protocol and registration, items 15 and 22, risk of bias across studies, items 16 and 23, additional analysis had less than 50% adherence. AMSTAR item 1, " a priori " design, item 5, list of studies and item 10, publication bias had less than 50% adherence. Logistic regression analyses showed that funding support and " a priori " design were associated with superior reporting quality, following PRISMA guideline and " a priori " design were associated with superior methodological quality. Reporting and methodological qualities of recently published meta-analyses in major paper-based urology journals are generally good. Further improvement could potentially be achieved by strictly adhering to PRISMA guideline and having " a priori " protocol.

Estimates of Flow Duration, Mean Flow, and Peak-Discharge Frequency Values for Kansas Stream Locations

USGS Publications Warehouse

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

2004-01-01

Streamflow statistics of flow duration and peak-discharge frequency were estimated for 4,771 individual locations on streams listed on the 1999 Kansas Surface Water Register. These statistics included the flow-duration values of 90, 75, 50, 25, and 10 percent, as well as the mean flow value. Peak-discharge frequency values were estimated for the 2-, 5-, 10-, 25-, 50-, and 100-year floods. Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating flow-duration values of 90, 75, 50, 25, and 10 percent and the mean flow for uncontrolled flow stream locations. The contributing-drainage areas of 149 U.S. Geological Survey streamflow-gaging stations in Kansas and parts of surrounding States that had flow uncontrolled by Federal reservoirs and used in the regression analyses ranged from 2.06 to 12,004 square miles. Logarithmic transformations of climatic and basin data were performed to yield the best linear relation for developing equations to compute flow durations and mean flow. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were contributing-drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. The analyses yielded a model standard error of prediction range of 0.43 logarithmic units for the 90-percent duration analysis to 0.15 logarithmic units for the 10-percent duration analysis. The model standard error of prediction was 0.14 logarithmic units for the mean flow. Regression equations used to estimate peak-discharge frequency values were obtained from a previous report, and estimates for the 2-, 5-, 10-, 25-, 50-, and 100-year floods were determined for this report. The regression equations and an interpolation procedure were used to compute flow durations, mean flow, and estimates of peak-discharge frequency for locations along uncontrolled flow streams on the 1999 Kansas Surface Water Register. Flow durations, mean flow, and peak-discharge frequency values determined at available gaging stations were used to interpolate the regression-estimated flows for the stream locations where available. Streamflow statistics for locations that had uncontrolled flow were interpolated using data from gaging stations weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled reaches of Kansas streams, the streamflow statistics were interpolated between gaging stations using only gaged data weighted by drainage area.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

BRCA1/2 Test Results Impact Risk Management Attitudes, Intentions and Uptake

PubMed Central

O’Neill, Suzanne C.; Valdimarsdottir, Heiddis B.; DeMarco, Tiffani A.; Peshkin, Beth N.; Graves, Kristi D.; Brown, Karen; Hurley, Karen E.; Isaacs, Claudine; Hecker, Sharon; Schwartz, Marc D.

2011-01-01

BACKGROUND Women who receive positive or uninformative BRCA1/2 test results face a number of decisions about how to manage their cancer risk. The purpose of this study was to prospectively examine the effect of receiving a positive vs. uninformative BRCA1/2 genetic test result on the perceived pros and cons of risk-reducing mastectomy (RRM) and risk-reducing oophorectomy (RRO) and breast cancer screening. We further examined how perceived pros and cons of surgery predict intention for and uptake of surgery. METHODS 308 women (146 positive, 162 uninformative) were included in RRM and breast cancer screening analyses. 276 women were included in RRO analyses. Participants completed questionnaires at pre-disclosure baseline and 1-, 6-and 12-months post-disclosure. We used linear multiple regression to assess whether test result contributed to change in pros and cons and logistic regression to predict intentions and surgery uptake. RESULTS Receipt of a positive BRCA1/2 test result predicted stronger pros for RRM and RRO (Ps < .001), but not perceived cons of RRM and RRO. Pros of surgery predicted RRM and RRO intentions in carriers and RRO intentions in uninformatives. Cons predicted RRM intentions in carriers. Pros and cons predicted carriers’ RRO uptake in the year after testing (Ps < .001). CONCLUSIONS Receipt of BRCA1/2 mutation test results impacts how carriers see the positive aspects of RRO and RRM and their surgical intentions. Both the positive and negative aspects predict uptake of surgery. PMID:20383578

Victimization and Suicidality among Female College Students

ERIC Educational Resources Information Center

Leone, Janel M.; Carroll, James M.

2016-01-01

Objective: To investigate the predictive role of victimization in suicidality among college women. Participants: Female respondents to the American College Health Association National College Health Assessment II (N = 258). Methods: Multivariate logistic regression analyses examined the relationship between victimization and suicidality. Results:…

Logistic Regression Analyses for Predicting Clinically Important Differences in Motor Capacity, Motor Performance, and Functional Independence after Constraint-Induced Therapy in Children with Cerebral Palsy

ERIC Educational Resources Information Center

Wang, Tien-ni; Wu, Ching-yi; Chen, Chia-ling; Shieh, Jeng-yi; Lu, Lu; Lin, Keh-chung

2013-01-01

Given the growing evidence for the effects of constraint-induced therapy (CIT) in children with cerebral palsy (CP), there is a need for investigating the characteristics of potential participants who may benefit most from this intervention. This study aimed to establish predictive models for the effects of pediatric CIT on motor and functional…

Validity of Treadmill-Derived Critical Speed on Predicting 5000-Meter Track-Running Performance.

PubMed

Nimmerichter, Alfred; Novak, Nina; Triska, Christoph; Prinz, Bernhard; Breese, Brynmor C

2017-03-01

Nimmerichter, A, Novak, N, Triska, C, Prinz, B, and Breese, BC. Validity of treadmill-derived critical speed on predicting 5,000-meter track-running performance. J Strength Cond Res 31(3): 706-714, 2017-To evaluate 3 models of critical speed (CS) for the prediction of 5,000-m running performance, 16 trained athletes completed an incremental test on a treadmill to determine maximal aerobic speed (MAS) and 3 randomly ordered runs to exhaustion at the [INCREMENT]70% intensity, at 110% and 98% of MAS. Critical speed and the distance covered above CS (D') were calculated using the hyperbolic speed-time (HYP), the linear distance-time (LIN), and the linear speed inverse-time model (INV). Five thousand meter performance was determined on a 400-m running track. Individual predictions of 5,000-m running time (t = [5,000-D']/CS) and speed (s = D'/t + CS) were calculated across the 3 models in addition to multiple regression analyses. Prediction accuracy was assessed with the standard error of estimate (SEE) from linear regression analysis and the mean difference expressed in units of measurement and coefficient of variation (%). Five thousand meter running performance (speed: 4.29 ± 0.39 m·s; time: 1,176 ± 117 seconds) was significantly better than the predictions from all 3 models (p < 0.0001). The mean difference was 65-105 seconds (5.7-9.4%) for time and -0.22 to -0.34 m·s (-5.0 to -7.5%) for speed. Predictions from multiple regression analyses with CS and D' as predictor variables were not significantly different from actual running performance (-1.0 to 1.1%). The SEE across all models and predictions was approximately 65 seconds or 0.20 m·s and is therefore considered as moderate. The results of this study have shown the importance of aerobic and anaerobic energy system contribution to predict 5,000-m running performance. Using estimates of CS and D' is valuable for predicting performance over race distances of 5,000 m.

Dietary acculturation and body composition predict American Hmong children's blood pressure.

PubMed

Smith, Chery; Franzen-Castle, Lisa

2012-01-01

Determine how dietary acculturation, anthropometric measures (height, weight, circumferences, and skinfolds), body mass index (BMI), and waist hip ratios (WHRs) are associated with blood pressure (BP) measures in Hmong children living in Minnesota. Acculturation was measured using responses to questions regarding language usage, social connections, and diet. Dietary assessment was completed using the multiple-pass 24-h dietary recall method on two different days. Anthropometric and BP measurement were taken using standard procedures, and BMI and WHR were calculated. Data analyses included descriptive statistics, ANOVA, and stepwise regression analyses. Using stepwise regression analysis, hip circumference (HC) predicted boys' systolic (S)BP (R(2) = 0.55). For girls' SBP, mid-upper arm circumference, WHR, low calcium consumption, and height percentile jointly explained 41% of the total variation. Mid upper arm circumference (MAC) and carbohydrate consumption predicted 35% of the variance for boys' diastolic (D)BP, and HC, dairy consumption, and calcium intake predicted 31% of the total variance for girls' DBP. Responses to dietary acculturation questions revealed between group differences for breakfast with half of the younger Born-Thailand/Laos (Born-T/L) consuming mostly Hmong food, while at dinner Born-US consumed a mixed diet and Born-T/L were more likely to consume Hmong food. Dietary acculturation and body composition predict Hmong children's BP. Copyright © 2012 Wiley Periodicals, Inc.

Death Anxiety as a Function of Aging Anxiety

ERIC Educational Resources Information Center

Benton, Jeremy P.; Christopher, Andrew N.; Walter, Mark I.

2007-01-01

To assess how different facets of aging anxiety contributed to the prediction of tangible and existential death anxiety, 167 Americans of various Christian denominations completed a battery of questionnaires. Multiple regression analyses, controlling for demographic variables and previously demonstrated predictors of death anxiety, revealed that…

Prediction of adherent placenta in pregnancy with placenta previa using ultrasonography and magnetic resonance imaging.

PubMed

Tanimura, Kenji; Yamasaki, Yui; Ebina, Yasuhiko; Deguchi, Masashi; Ueno, Yoshiko; Kitajima, Kazuhiro; Yamada, Hideto

2015-04-01

Adherent placenta is a life-threatening condition in pregnancy, and is often complicated by placenta previa. The aim of this prospective study was to determine prenatal imaging findings that predict the presence of adherent placenta in pregnancies with placenta previa. The study included 58 consecutive pregnant women with placenta previa who underwent both ultrasonography and magnetic resonance imaging prenatally. Ultrasonographic findings of anterior placental location, grade 2 or higher placental lacunae (PL≥G2), loss of retroplacental hypoechoic clear zone (LCZ) and the presence of turbulent blood flow in the arteries were evaluated, in addition to MRI findings. Forty-three women underwent cesarean section alone; 15 women with adherent placenta underwent cesarean section followed by hysterectomy with pathological examination. To determine imaging findings that predict adherent placenta, univariate and multivariate logistic regression analyses were performed. Univariate logistic regression analyses demonstrated that anterior placental location, PL≥G2, LCZ, and MRI were associated with the presence of adherent placenta. Multivariate analyses revealed that LCZ (p<0.01, odds ratio 15.6, 95%CI 2.1-114.6) was a single significant predictor of adherent placenta in women with placenta previa. This prospective study demonstrated for the first time that US findings, especially LCZ, might be useful for identifying patients at high risk for adherent placenta among pregnant women with placenta previa. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Vesicular stomatitis forecasting based on Google Trends

PubMed Central

Lu, Yi; Zhou, GuangYa; Chen, Qin

2018-01-01

Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198

Applicability of the Tanaka-Johnston and Moyers mixed dentition analyses in Northeast Han Chinese.

PubMed

Sherpa, Jangbu; Sah, Gopal; Rong, Zeng; Wu, Lipeng

2015-06-01

To assess applicability of the Tanaka-Johnston and Moyers prediction methods in a Han ethnic group from Northeast China and to develop prediction equations for this same population. Cross-sectional study. Department of Orthodontics, School of Stomatology, Jiamusi University, Heilongjiang, China. A total of 130 subjects (65 male and 65 female) aged 16-21 years from a Han ethnic group of Northeast China were recruited from dental students and patients seeking orthodontic treatment. Ethnicity was verified by questionnaire. Mesio-distal tooth width was measured using Digital Vernier calipers. Predicted values were obtained from the Tanaka-Johnston and Moyers methods in both arches were compared with the actual measured widths. Based on regression analysis, prediction equations were developed. Tanaka-Johnston equations were not precise, except for the upper arch in males. However, the Moyers 85th percentile in the upper arch and 75th percentile in the lower arch predicted the sum precisely in males. For females, the Moyers 75th percentile predicted the sum precisely for the upper arch, but none of the Moyers percentiles predicted in the lower arch. Both the Tanaka-Johnston and Moyers method may not be applied universally without question. Hence, it may be safer to develop regression equations for specific populations. Validating studies must be conducted to confirm the precision of these newly developed regression equations.

The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

PubMed

Menditto, Anthony A; Linhorst, Donald M; Coleman, James C; Beck, Niels C

2006-04-01

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.

Enhanced fertility prediction of cryopreserved boar spermatozoa using novel sperm function assessment.

PubMed

Daigneault, B W; McNamara, K A; Purdy, P H; Krisher, R L; Knox, R V; Rodriguez-Zas, S L; Miller, D J

2015-05-01

Due to reduced fertility, cryopreserved semen is seldom used for commercial porcine artificial insemination (AI). Predicting the fertility of individual frozen ejaculates for selection of higher quality semen prior to AI would increase overall success. Our objective was to test novel and traditional laboratory analyses to identify characteristics of cryopreserved spermatozoa that are related to boar fertility. Traditional post-thaw analyses of motility, viability, and acrosome integrity were performed on each ejaculate. In vitro fertilization, cleavage, and blastocyst development were also determined. Finally, spermatozoa-oviduct binding and competitive zona-binding assays were applied to assess sperm adhesion to these two matrices. Fertility of the same ejaculates subjected to laboratory assays was determined for each boar by multi-sire AI and defined as (i) the mean percentage of the litter sired and (ii) the mean number of piglets sired in each litter. Means of each laboratory evaluation were calculated for each boar and those values were applied to multiple linear regression analyses to determine which sperm traits could collectively estimate fertility in the simplest model. The regression model to predict the percent of litter sired by each boar was highly effective (p < 0.001, r(2) = 0.87) and included five traits; acrosome-compromised spermatozoa, percent live spermatozoa (0 and 60 min post-thaw), percent total motility, and the number of zona-bound spermatozoa. A second model to predict the number of piglets sired by boar was also effective (p < 0.05, r(2) = 0.57). These models indicate that the fertility of cryopreserved boar spermatozoa can be predicted effectively by including traditional and novel laboratory assays that consider functions of spermatozoa. © 2015 American Society of Andrology and European Academy of Andrology.

Can body mass index predict percent body fat and changes in percent body fat with weight loss in bariatric surgery patients?

PubMed

Carey, Daniel G; Raymond, Robert L

2008-07-01

The primary objective of this study was to assess the validity of body mass index (BMI) in predicting percent body fat and changes in percent body fat with weight loss in bariatric surgery patients. Twenty-two bariatric patients (17 female, five male) began the study designed to include 12 months of testing, including data collection within 1 week presurgery and 1 month, 3 months, 6 months, and 1 year postsurgery. Five female subjects were lost to the study between 6 months and 12 months postsurgery, resulting in 17 subjects (12 female, five male) completing the 12 months of testing. Variables measured in the study included height, weight, percent fat (% fat) by hydrostatic weighing, lean mass, fat mass, and basal metabolic rate. Regression analyses predicting % fat from BMI yielded the following results: presurgery r = 0.173, p = 0.479, standard error of estimate (SEE) = 3.86; 1 month r = 0.468, p = 0.043, SEE = 4.70; 3 months r = 0.553, p = 0.014, SEE = 6.2; 6 months r = 0.611, p = 0.005, SEE = 5.88; 12 months r = 0.596, p = 0.007, SEE = 7.13. Regression analyses predicting change in % fat from change in BMI produced the following results: presurgery to 1 month r = -0.134, p = 0.583, SEE = 2.44%; 1-3 months r = 0.265, p = 0.272, SEE = 2.36%; 3-6 months r = 0.206, p = 0.398, SEE = 3.75%; 6-12 months r = 0.784, p = 0.000, SEE = 3.20. Although some analyses resulted in significant correlation coefficients (p < 0.05), the relatively large SEE values would preclude the use of BMI in predicting % fat or change in % fat with weight loss in bariatric surgery patients.

External validation of the diffuse intrinsic pontine glioma survival prediction model: a collaborative report from the International DIPG Registry and the SIOPE DIPG Registry.

PubMed

Veldhuijzen van Zanten, Sophie E M; Lane, Adam; Heymans, Martijn W; Baugh, Joshua; Chaney, Brooklyn; Hoffman, Lindsey M; Doughman, Renee; Jansen, Marc H A; Sanchez, Esther; Vandertop, William P; Kaspers, Gertjan J L; van Vuurden, Dannis G; Fouladi, Maryam; Jones, Blaise V; Leach, James

2017-08-01

We aimed to perform external validation of the recently developed survival prediction model for diffuse intrinsic pontine glioma (DIPG), and discuss its utility. The DIPG survival prediction model was developed in a cohort of patients from the Netherlands, United Kingdom and Germany, registered in the SIOPE DIPG Registry, and includes age <3 years, longer symptom duration and receipt of chemotherapy as favorable predictors, and presence of ring-enhancement on MRI as unfavorable predictor. Model performance was evaluated by analyzing the discrimination and calibration abilities. External validation was performed using an unselected cohort from the International DIPG Registry, including patients from United States, Canada, Australia and New Zealand. Basic comparison with the results of the original study was performed using descriptive statistics, and univariate- and multivariable regression analyses in the validation cohort. External validation was assessed following a variety of analyses described previously. Baseline patient characteristics and results from the regression analyses were largely comparable. Kaplan-Meier curves of the validation cohort reproduced separated groups of standard (n = 39), intermediate (n = 125), and high-risk (n = 78) patients. This discriminative ability was confirmed by similar values for the hazard ratios across these risk groups. The calibration curve in the validation cohort showed a symmetric underestimation of the predicted survival probabilities. In this external validation study, we demonstrate that the DIPG survival prediction model has acceptable cross-cohort calibration and is able to discriminate patients with short, average, and increased survival. We discuss how this clinico-radiological model may serve a useful role in current clinical practice.

Interrelation and independence of positive and negative psychological constructs in predicting general treatment adherence in coronary artery patients - Results from the THORESCI study.

PubMed

van Montfort, Eveline; Denollet, Johan; Widdershoven, Jos; Kupper, Nina

2016-09-01

In cardiac patients, positive psychological factors have been associated with improved medical and psychological outcomes. The current study examined the interrelation between and independence of multiple positive and negative psychological constructs. Furthermore, the potential added predictive value of positive psychological functioning regarding the prediction of patients' treatment adherence and participation in cardiac rehabilitation (CR) was investigated. 409 percutaneous coronary intervention (PCI) patients were included (mean age = 65.6 ± 9.5; 78% male). Self-report questionnaires were administered one month post-PCI. Positive psychological constructs included positive affect (GMS) and optimism (LOT-R); negative constructs were depression (PHQ-9, BDI), anxiety (GAD-7) and negative affect (GMS). Six months post-PCI self-reported general adherence (MOS) and CR participation were determined. Factor Analysis (Oblimin rotation) revealed two components (r = − 0.56), reflecting positive and negative psychological constructs. Linear regression analyses showed that in unadjusted analyses both optimism and positive affect were associated with better general treatment adherence at six months (p < 0.05). In adjusted analyses, optimism's predictive values remained, independent of sex, age, PCI indication, depression and anxiety. Univariate logistic regression analysis showed that in patients with a cardiac history, positive affect was significantly associated with CR participation. After controlling for multiple covariates, this relation was no longer significant. Positive and negative constructs should be considered as two distinct dimensions. Positive psychological constructs (i.e. optimism) may be of incremental value to negative psychological constructs in predicting patients' treatment adherence. A more complete view of a patients' psychological functioning will open new avenues for treatment. Additional research is needed to investigate the relationship between positive psychological factors and other cardiac outcomes, such as cardiac events and mortality.

Reporting and methodological quality of meta-analyses in urological literature

PubMed Central

Xu, Jing

2017-01-01

Purpose To assess the overall quality of published urological meta-analyses and identify predictive factors for high quality. Materials and Methods We systematically searched PubMed to identify meta-analyses published from January 1st, 2011 to December 31st, 2015 in 10 predetermined major paper-based urology journals. The characteristics of the included meta-analyses were collected, and their reporting and methodological qualities were assessed by the PRISMA checklist (27 items) and AMSTAR tool (11 items), respectively. Descriptive statistics were used for individual items as a measure of overall compliance, and PRISMA and AMSTAR scores were calculated as the sum of adequately reported domains. Logistic regression was used to identify predictive factors for high qualities. Results A total of 183 meta-analyses were included. The mean PRISMA and AMSTAR scores were 22.74 ± 2.04 and 7.57 ± 1.41, respectively. PRISMA item 5, protocol and registration, items 15 and 22, risk of bias across studies, items 16 and 23, additional analysis had less than 50% adherence. AMSTAR item 1, “a priori” design, item 5, list of studies and item 10, publication bias had less than 50% adherence. Logistic regression analyses showed that funding support and “a priori” design were associated with superior reporting quality, following PRISMA guideline and “a priori” design were associated with superior methodological quality. Conclusions Reporting and methodological qualities of recently published meta-analyses in major paper-based urology journals are generally good. Further improvement could potentially be achieved by strictly adhering to PRISMA guideline and having “a priori” protocol. PMID:28439452

The Effects of Home-School Dissonance on African American Male High School Students

ERIC Educational Resources Information Center

Brown-Wright, Lynda; Tyler, Kenneth Maurice

2010-01-01

The current study examined associations between home-school dissonance and several academic and psychological variables among 80 African American male high school students. Regression analyses revealed that home-school dissonance significantly predicted multiple academic and psychological variables, including amotivation, academic cheating,…

Acculturative Stress, Parental and Professor Attachment, and College Adjustment in Asian International Students

ERIC Educational Resources Information Center

Han, Suejung; Pistole, M. Carole; Caldwell, Jarred M.

2017-01-01

This study examined parental and professor attachment as buffers against acculturative stress and as predictors of college adjustment of 210 Asian international students (AISs). Moderated hierarchical regression analyses revealed that acculturative stress negatively and secure parental and professor attachment positively predicted academic…

Violence Breeds Violence: Childhood Exposure and Adolescent Conduct Problems

ERIC Educational Resources Information Center

Weaver, Chelsea M.; Borkowski, John G.; Whitman, Thomas L.

2008-01-01

The relationships between childhood exposure to violence and adolescent conduct problems were investigated in a sample of 88 primiparous adolescent mothers and their children. Regression analyses revealed that witnessing violence and victimization prior to age 10 predicted delinquency and violent behaviors, even after controlling for prenatal…

Paranormal belief, experience, and the Keirsey Temperament Sorter.

PubMed

Fox, J; Williams, C

2000-06-01

121 college students completed the Anomalous Experience Inventory and the Keirsey Temperament Sorter. Multiple regression analyses provided significant models predicting both Paranormal Experience and Belief; the main predictors were the other subscales of the Anomalous Experience Inventory with the Keirsey variables playing only a minor role.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Non-metallic coating thickness prediction using artificial neural network and support vector machine with time resolved thermography

NASA Astrophysics Data System (ADS)

Wang, Hongjin; Hsieh, Sheng-Jen; Peng, Bo; Zhou, Xunfei

2016-07-01

A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It's theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model.

Performance on the adult rheumatology in-training examination and relationship to outcomes on the rheumatology certification examination.

PubMed

Lohr, Kristine M; Clauser, Amanda; Hess, Brian J; Gelber, Allan C; Valeriano-Marcet, Joanne; Lipner, Rebecca S; Haist, Steven A; Hawley, Janine L; Zirkle, Sarah; Bolster, Marcy B

2015-11-01

The American College of Rheumatology (ACR) Adult Rheumatology In-Training Examination (ITE) is a feedback tool designed to identify strengths and weaknesses in the content knowledge of individual fellows-in-training and the training program curricula. We determined whether scores on the ACR ITE, as well as scores on other major standardized medical examinations and competency-based ratings, could be used to predict performance on the American Board of Internal Medicine (ABIM) Rheumatology Certification Examination. Between 2008 and 2012, 629 second-year fellows took the ACR ITE. Bivariate correlation analyses of assessment scores and multiple linear regression analyses were used to determine whether ABIM Rheumatology Certification Examination scores could be predicted on the basis of ACR ITE scores, United States Medical Licensing Examination scores, ABIM Internal Medicine Certification Examination scores, fellowship directors' ratings of overall clinical competency, and demographic variables. Logistic regression was used to evaluate whether these assessments were predictive of a passing outcome on the Rheumatology Certification Examination. In the initial linear model, the strongest predictors of the Rheumatology Certification Examination score were the second-year fellows' ACR ITE scores (β = 0.438) and ABIM Internal Medicine Certification Examination scores (β = 0.273). Using a stepwise model, the strongest predictors of higher scores on the Rheumatology Certification Examination were second-year fellows' ACR ITE scores (β = 0.449) and ABIM Internal Medicine Certification Examination scores (β = 0.276). Based on the findings of logistic regression analysis, ACR ITE performance was predictive of a pass/fail outcome on the Rheumatology Certification Examination (odds ratio 1.016 [95% confidence interval 1.011-1.021]). The predictive value of the ACR ITE score with regard to predicting performance on the Rheumatology Certification Examination supports use of the Adult Rheumatology ITE as a valid feedback tool during fellowship training. © 2015, American College of Rheumatology.

Estimates of Median Flows for Streams on the 1999 Kansas Surface Water Register

USGS Publications Warehouse

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

2004-01-01

The Kansas State Legislature, by enacting Kansas Statute KSA 82a?2001 et. seq., mandated the criteria for determining which Kansas stream segments would be subject to classification by the State. One criterion for the selection as a classified stream segment is based on the statistic of median flow being equal to or greater than 1 cubic foot per second. As specified by KSA 82a?2001 et. seq., median flows were determined from U.S. Geological Survey streamflow-gaging-station data by using the most-recent 10 years of gaged data (KSA) for each streamflow-gaging station. Median flows also were determined by using gaged data from the entire period of record (all-available hydrology, AAH). Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating median flows for uncontrolled stream segments. The drainage area of the gaging stations on uncontrolled stream segments used in the regression analyses ranged from 2.06 to 12,004 square miles. A logarithmic transformation of the data was needed to develop the best linear relation for computing median flows. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. Tobit analyses of KSA data yielded a model standard error of prediction of 0.285 logarithmic units, and the best equations using Tobit analyses of AAH data had a model standard error of prediction of 0.250 logarithmic units. These regression equations and an interpolation procedure were used to compute median flows for the uncontrolled stream segments on the 1999 Kansas Surface Water Register. Measured median flows from gaging stations were incorporated into the regression-estimated median flows along the stream segments where available. The segments that were uncontrolled were interpolated using gaged data weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled segments of Kansas streams, the median flow information was interpolated between gaging stations using only gaged data weighted by drainage area. Of the 2,232 total stream segments on the Kansas Surface Water Register, 34.5 percent of the segments had an estimated median streamflow of less than 1 cubic foot per second when the KSA analysis was used. When the AAH analysis was used, 36.2 percent of the segments had an estimated median streamflow of less than 1 cubic foot per second. This report supercedes U.S. Geological Survey Water-Resources Investigations Report 02?4292.

Structural vascular disease in Africans: Performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: The SABPA study.

PubMed

Botha, J; de Ridder, J H; Potgieter, J C; Steyn, H S; Malan, L

2013-10-01

A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fasting bloods (glucose, high density lipoprotein (HDL) and triglycerides) were obtained in a well-controlled setting. The RPWC male model (LR ROC AUC: 0.71, NN ROC AUC: 0.71) was practically equal to the JSC model (LR ROC AUC: 0.71, NN ROC AUC: 0.69) to predict structural vascular -disease. Similarly, the female RPWC model (LR ROC AUC: 0.84, NN ROC AUC: 0.82) and JSC model (LR ROC AUC: 0.82, NN ROC AUC: 0.81) equally predicted CIMT as surrogate marker for structural vascular disease. Odds ratios supported validity where prediction of CIMT revealed -clinical -significance, well over 1, for both the JSC and RPWC models in African males and females (OR 3.75-13.98). In conclusion, the proposed RPWC model was substantially validated utilizing linear and non-linear analyses. We therefore propose ethnic-specific WC cut points (African males, ≥90 cm; -females, ≥98 cm) to predict a surrogate marker for structural vascular disease. © J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart · New York.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

PubMed

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

Burnout does not help predict depression among French school teachers.

PubMed

Bianchi, Renzo; Schonfeld, Irvin Sam; Laurent, Eric

2015-11-01

Burnout has been viewed as a phase in the development of depression. However, supportive research is scarce. We examined whether burnout predicted depression among French school teachers. We conducted a 2-wave, 21-month study involving 627 teachers (73% female) working in French primary and secondary schools. Burnout was assessed with the Maslach Burnout Inventory and depression with the 9-item depression module of the Patient Health Questionnaire (PHQ-9). The PHQ-9 grades depressive symptom severity and provides a provisional diagnosis of major depression. Depression was treated both as a continuous and categorical variable using linear and logistic regression analyses. We controlled for gender, age, and length of employment. Controlling for baseline depressive symptoms, linear regression analysis showed that burnout symptoms at time 1 (T1) did not predict depressive symptoms at time 2 (T2). Baseline depressive symptoms accounted for about 88% of the association between T1 burnout and T2 depressive symptoms. Only baseline depressive symptoms predicted depressive symptoms at follow-up. Similarly, logistic regression analysis revealed that burnout symptoms at T1 did not predict incident cases of major depression at T2 when depressive symptoms at T1 were included in the predictive model. Only baseline depressive symptoms predicted cases of major depression at follow-up. This study does not support the view that burnout is a phase in the development of depression. Assessing burnout symptoms in addition to "classical" depressive symptoms may not always improve our ability to predict future depression.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Correlation of sensory bitterness in dairy protein hydrolysates: Comparison of prediction models built using sensory, chromatographic and electronic tongue data.

PubMed

Newman, J; Egan, T; Harbourne, N; O'Riordan, D; Jacquier, J C; O'Sullivan, M

2014-08-01

Sensory evaluation can be problematic for ingredients with a bitter taste during research and development phase of new food products. In this study, 19 dairy protein hydrolysates (DPH) were analysed by an electronic tongue and their physicochemical characteristics, the data obtained from these methods were correlated with their bitterness intensity as scored by a trained sensory panel and each model was also assessed by its predictive capabilities. The physiochemical characteristics of the DPHs investigated were degree of hydrolysis (DH%), and data relating to peptide size and relative hydrophobicity from size exclusion chromatography (SEC) and reverse phase (RP) HPLC. Partial least square regression (PLS) was used to construct the prediction models. All PLS regressions had good correlations (0.78 to 0.93) with the strongest being the combination of data obtained from SEC and RP HPLC. However, the PLS with the strongest predictive power was based on the e-tongue which had the PLS regression with the lowest root mean predicted residual error sum of squares (PRESS) in the study. The results show that the PLS models constructed with the e-tongue and the combination of SEC and RP-HPLC has potential to be used for prediction of bitterness and thus reducing the reliance on sensory analysis in DPHs for future food research. Copyright © 2014 Elsevier B.V. All rights reserved.

Understanding the Impact of School Factors on School Counselor Burnout: A Mixed-Methods Study

ERIC Educational Resources Information Center

Bardhoshi, Gerta; Schweinle, Amy; Duncan, Kelly

2014-01-01

This mixed-methods study investigated the relationship between burnout and performing noncounseling duties among a national sample of professional school counselors, while identifying school factors that could attenuate this relationship. Results of regression analyses indicate that performing noncounseling duties significantly predicted burnout…

Role of the Big Five Personality Traits in Predicting College Students' Academic Motivation and Achievement

ERIC Educational Resources Information Center

Komarraju, Meera; Karau, Steven J.; Schmeck, Ronald R.

2009-01-01

College students (308 undergraduates) completed the Five Factor Inventory and the Academic Motivations Scale, and reported their college grade point average (GPA). A correlation analysis revealed an interesting pattern of significant relationships. Further, regression analyses indicated that conscientiousness and openness explained 17% of the…

The Role of Homework in Cognitive-Behavioral Therapy for Cocaine Dependence

ERIC Educational Resources Information Center

Gonzalez, Vivian M.; Schmitz, Joy M.; DeLaune, Katherine A.

2006-01-01

This study examines the effect of homework compliance on treatment outcome in 123 participants receiving cognitive-behavioral therapy (CBT) for cocaine dependence. Regression analyses revealed a significant relationship between homework compliance and cocaine use that was moderated by readiness to change. Homework compliance predicted less cocaine…

College Women's Value Orientations toward Family, Career, and Graduate School.

ERIC Educational Resources Information Center

Battle, Ann; Wigfield, Allan

2003-01-01

Scales assessing intention to attend graduate school and family/career values were completed by 216 college women. Multiple regression analyses demonstrated that components of task value (intrinsic-attainment, utility, cost) predicted graduate study intentions. Strong career orientation was positively related to the valuing of graduate education.…

Alcohol Use and Drinking Motives among Sanctioned and Nonsanctioned Students

ERIC Educational Resources Information Center

Doumas, Diana M.

2017-01-01

This study examined differences in the relationship of drinking motives to drinking behavior among sanctioned and nonsanctioned 1st-year students (N = 298). Results of hierarchical regression analyses indicated that for both sanctioned and nonsanctioned students, alcohol use was predicted by social and enhancement motives, and alcohol-related…

No Parent Left Behind: Strengthening Ties between Educators and African American Parents/Guardians.

ERIC Educational Resources Information Center

Thompson, Gail L.

2003-01-01

Used regression analyses to identify variables predicting the six most frequently cited problems that concerned African American parents and guardians of children enrolled in urban schools. Data from parent/guardian surveys highlighted six problems: school district racial climate; math problems; suspension; writing problems; reading comprehension…

Examining Predictors of Group Leader Self-Efficacy for Preservice School Counselors

ERIC Educational Resources Information Center

Springer, Sarah I.

2016-01-01

Group counseling is an important treatment modality used to support clients in a variety of therapeutic settings. This article highlights the results of an exploratory study that examined site supervisory factors that predicted group leader self-efficacy for preservice school counselors. Results of multiple regression analyses suggest meaningful…

Multi-Informant Predictors of Social Inclusion for Students with Autism Spectrum Disorders Attending Mainstream School

ERIC Educational Resources Information Center

Jones, Alice P.; Frederickson, Norah

2010-01-01

This study examined differential profiles of behavioural characteristics predictive of successful inclusion in mainstream education for children with autism spectrum disorders (ASD) and comparison students. Multiple regression analyses using behavioural ratings from parents, teachers and peers found some evidence for differential profiles…

Active Commuting Patterns at a Large, Midwestern College Campus

ERIC Educational Resources Information Center

Bopp, Melissa; Kaczynski, Andrew; Wittman, Pamela

2011-01-01

Objective: To understand patterns and influences on active commuting (AC) behavior. Participants: Students and faculty/staff at a university campus. Methods: In April-May 2008, respondents answered an online survey about mode of travel to campus and influences on commuting decisions. Hierarchical regression analyses predicted variance in walking…

Anxiety, Outcome Expectancies, and Young People's Willingness to Engage in Contact with the Elderly

ERIC Educational Resources Information Center

Hutchison, Paul; Fox, Edward; Laas, Anna Maria; Matharu, Jasmin; Urzi, Serena

2010-01-01

A cross-sectional study (N = 61) investigated the relationship between young people's previous experiences of intergenerational contact and their willingness to engage in future contact with the elderly. Regression analyses confirmed that frequent positive intergenerational contact predicted more positive outcome expectancies, less intergroup…

Regression Analyses of Self-Regulatory Concepts to Predict Community College Math Achievement and Persistence

ERIC Educational Resources Information Center

Gramlich, Stephen Peter

2010-01-01

Open door admissions at community colleges bring returning adults, first timers, low achievers, disabled persons, and immigrants. Passing and retention rates for remedial and non-developmental math courses can be comparatively inadequate (LAVC, 2005; CCPRDC, 2000; SBCC, 2004; Seybert & Soltz, 1992; Waycaster, 2002). Mathematics achievement…

Client Predictors of Short-term Psychotherapy Outcomes among Asian and White American Outpatients

PubMed Central

Kim, Jin E.; Zane, Nolan W.; Blozis, Shelley A.

2015-01-01

Purpose To examine predictors of psychotherapy outcomes, focusing on client characteristics that are especially salient for culturally diverse clients. Method Sixty clients (31 women; 27 White Americans, 33 Asian Americans) participated in this treatment study. Client characteristics were measured at pre-treatment, and outcomes were measured post-fourth session via therapist ratings of functioning and symptomatology. Regression analyses were utilized to test for predictors of outcomes, and bootstrap analyses were utilized to test for mediators. Results Higher levels of somatic symptoms predicted lower psychosocial functioning at post-treatment. Avoidant coping style predicted more negative symptoms and more psychological discomfort. Non-English language preference predicted worse outcomes; this effect was mediated by an avoidant coping style. Conclusions Language preference, avoidant coping style, and somatic symptoms predicted treatment outcome in a culturally diverse sample. Findings suggest that race/ethnicity-related variables may function through mediating proximal variables to affect outcomes. PMID:22836681

Application of classification tree and logistic regression for the management and health intervention plans in a community-based study.

PubMed

Teng, Ju-Hsi; Lin, Kuan-Chia; Ho, Bin-Shenq

2007-10-01

A community-based aboriginal study was conducted and analysed to explore the application of classification tree and logistic regression. A total of 1066 aboriginal residents in Yilan County were screened during 2003-2004. The independent variables include demographic characteristics, physical examinations, geographic location, health behaviours, dietary habits and family hereditary diseases history. Risk factors of cardiovascular diseases were selected as the dependent variables in further analysis. The completion rate for heath interview is 88.9%. The classification tree results find that if body mass index is higher than 25.72 kg m(-2) and the age is above 51 years, the predicted probability for number of cardiovascular risk factors > or =3 is 73.6% and the population is 322. If body mass index is higher than 26.35 kg m(-2) and geographical latitude of the village is lower than 24 degrees 22.8', the predicted probability for number of cardiovascular risk factors > or =4 is 60.8% and the population is 74. As the logistic regression results indicate that body mass index, drinking habit and menopause are the top three significant independent variables. The classification tree model specifically shows the discrimination paths and interactions between the risk groups. The logistic regression model presents and analyses the statistical independent factors of cardiovascular risks. Applying both models to specific situations will provide a different angle for the design and management of future health intervention plans after community-based study.

Prediction of elemental creep. [steady state and cyclic data from regression analysis

NASA Technical Reports Server (NTRS)

Davis, J. W.; Rummler, D. R.

1975-01-01

Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

A new predictive indicator for development of pressure ulcers in bedridden patients based on common laboratory tests results.

PubMed

Hatanaka, N; Yamamoto, Y; Ichihara, K; Mastuo, S; Nakamura, Y; Watanabe, M; Iwatani, Y

2008-04-01

Various scales have been devised to predict development of pressure ulcers on the basis of clinical and laboratory data, such as the Braden Scale (Braden score), which is used to monitor activity and skin conditions of bedridden patients. However, none of these scales facilitates clinically reliable prediction. To develop a clinical laboratory data-based predictive equation for the development of pressure ulcers. Subjects were 149 hospitalised patients with respiratory disorders who were monitored for the development of pressure ulcers over a 3-month period. The proportional hazards model (Cox regression) was used to analyse the results of 12 basic laboratory tests on the day of hospitalisation in comparison with Braden score. Pressure ulcers developed in 38 patients within the study period. A Cox regression model consisting solely of Braden scale items showed that none of these items contributed to significantly predicting pressure ulcers. Rather, a combination of haemoglobin (Hb), C-reactive protein (CRP), albumin (Alb), age, and gender produced the best model for prediction. Using the set of explanatory variables, we created a new indicator based on a multiple logistic regression equation. The new indicator showed high sensitivity (0.73) and specificity (0.70), and its diagnostic power was higher than that of Alb, Hb, CRP, or the Braden score alone. The new indicator may become a more useful clinical tool for predicting presser ulcers than Braden score. The new indicator warrants verification studies to facilitate its clinical implementation in the future.

Psychopathy and criminal violence: the moderating effect of ethnicity.

PubMed

Walsh, Zach

2013-10-01

This study aimed to determine the cross-ethnic stability of the predictive relationship of psychopathy for violence. Participants were 424 adult male jail inmates. Psychopathy was assessed using the Psychopathy Checklist-Revised and criminal violence was assessed using a comprehensive database of arrests for violent crimes. Ethnic categories included the groups that make up the vast majority of U.S. inmates: European American (EA, n = 166), African American (AA, n = 174), and Latino American (LA, n = 84). Ethnically aggregated Cox regression survival analyses identified predictive effects for psychopathy. Disaggregated analyses identified ethnic differences: Psychopathy was more strongly predictive of violence among EA (R² = .13, 95% CI [.04, .22], p < .01) relative to AA inmates (R² = .05, 95% CI [.00, .11], p < .01) and was not related to violence among LA participants (R² = .02, 95% CI [.00, .08], p = .22). Receiver operating characteristic curve analyses yielded an equivalent pattern of results. These findings add to a growing literature suggesting cross-ethnic variability in the predictive power of psychopathy for violence. PsycINFO Database Record (c) 2013 APA, all rights reserved

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

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

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

Predicting Treatment Outcomes and Responder Subsets in Scleroderma-related Interstitial Lung Disease

PubMed Central

Roth, Michael D.; Tseng, Chi-Hong; Clements, Philip J.; Furst, Daniel E.; Tashkin, Donald P.; Goldin, Jonathan G.; Khanna, Dinesh; Kleerup, Eric C.; Li, Ning; Elashoff, David; Elashoff, Robert E.

2014-01-01

Objectives To identify baseline characteristics of patients with Scleroderma-Related Interstitial Lung Disease (SSc-ILD) which predict the most favorable response to a 12-month treatment with oral cyclophosphamide (CYC). Methods Regression analyses were retrospectively applied to the Scleroderma Lung Study data in order to identify baseline characteristics that correlated with the absolute change in %-predicted Forced Vital Capacity (FVC) and the placebo-adjusted change in %-predicted FVC over time (the CYC treatment effect). Results Completion of the CYC arm of the Scleroderma Lung Study was associated with a placebo-adjusted improvement in %-predicted FVC of 2.11% at 12 months which increased to 4.16% when patients were followed for another 6 months (p=0.014). Multivariate regression analyses identified the maximal severity of reticular infiltrates on baseline high-resolution computerized tomography (HRCT), the modified Rodnan Skin Score (mRSS), and Mahler's Baseline Dyspnea Index (BDI) as independent correlates of treatment response. When patients were stratified based on whether 50% or more of any lung zone was involved by reticular infiltrates on HRCT and/or the presence of a mRSS of at least 23, a subgroup emerged with an average CYC treatment effect of 4.73% at 12 months and 9.81% at 18 months (p<0.001). Conversely, there was no treatment effect (−0.58%) in patients with less severe HRCT findings and a lower mRSS. Conclusions A retrospective analysis of the Scleroderma Lung Study identified the severity of reticular infiltrates on baseline HRCT and the baseline mRSS as patient features that might predict responsiveness to CYC therapy. PMID:21547897

A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

NASA Astrophysics Data System (ADS)

Zhou, Yapeng; Huang, Miaohua; Chen, Yupu; Tao, Ye

2016-07-01

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in an intelligent battery management system. The capacity and internal resistance are often used as the batteries health indicator (HI) for quantifying degradation and predicting RUL. However, on-line measurement of capacity and internal resistance are hardly realizable due to the not fully charged and discharged condition and the extremely expensive cost, respectively. Therefore, there is a great need to find an optional way to deal with this plight. In this work, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation modeling and RUL prediction. Moreover, Box-Cox transformation is employed to improve HI performance. Then Pearson and Spearman correlation analyses are utilized to evaluate the similarity between real capacity and the estimated capacity derived from the HI. Next, both simple statistical regression technique and optimized relevance vector machine are employed to predict the RUL based on the presented HI. The correlation analyses and prediction results show the efficiency and effectiveness of the proposed HI for battery degradation modeling and RUL prediction.

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

PubMed

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value<0.001), whereas RDW is not a prediction index of AP severity (P-value>0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value<0.001), and MEWS is not an independent prediction index of AP severity (P-value>0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-value<0.001). The constructed model is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP and serum Ca2+ individually (P-value<0.01). Verification of the internal validity of the models by bootstrapping is favorable. BISAP and serum Ca2+ have high predictive value for the severity of AP. However, the model built by combining BISAP and serum Ca2+ is remarkably superior to those of BISAP and serum Ca2+ individually. Furthermore, this model is simple, practical and appropriate for clinical use. Copyright © 2016. Published by Elsevier Masson SAS.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

Quality of Family Context or Sibling Status? Influences on Cognitive Development

ERIC Educational Resources Information Center

Freijo, Enrique B. Arranz; Oliva, Alfredo; Olabarrieta, Fernando; Martin, Juan Luis; Manzano, Ainhoa; Richards, Martin P. M.

2008-01-01

This study analyzes the influence of socioeconomic status, quality of family context and sibling status on cognitive development in a sample of 551 five-year-old children. The regression analyses confirmed the predictive value of socioeconomic status and quality of family context on cognitive development. The quality of family context mediates the…

Ethnic Identity and Loneliness in Predicting Suicide Risk in Latino College Students

ERIC Educational Resources Information Center

Chang, Edward C.; Díaz, Lizbeth; Lucas, Abigael G.; Lee, Jerin; Powell, Nicholas J.; Kafelghazal, Sally; Chartier, Sarah J.; Morris, Lily E.; Marshall-Broaden, Tey'Ariana M.; Hirsch, Jameson K.; Jeglic, Elizabeth L.

2017-01-01

This study examined the role of ethnic identity and loneliness as predictors of suicide risk, namely, hopelessness and suicidal behaviors, in Latino college students. One hundred sixty Latino students completed a survey assessing for the aforementioned constructs. Results of conducting regression analyses indicated that ethnic identity was a…

Predictors of Accounting Salaries: A Comparison of Bachelor Degree Graduate Salaries with Associate Degree Graduate Salaries

ERIC Educational Resources Information Center

Tickell, Geoffrey

2009-01-01

This paper reports on an investigation comparing the employment salary of bachelor degree in accounting graduates with associate degree in accounting graduates two years after their graduation. Using hierarchical regression analyses, this study shows the predictive strength of participants' academic qualifications, age, gender, GPA, professional…

Emotional Support and Expectations from Parents, Teachers, and Peers Predict Adolescent Competence at School

ERIC Educational Resources Information Center

Wentzel, Kathryn R.; Russell, Shannon; Baker, Sandra

2016-01-01

We examined perceived emotional support and expectations from parents, teachers, and classmates in relation to Mexican American adolescents' (n = 398) social behavior and academic functioning. Results of regression analyses indicated that direct associations between emotional support and expectations differ as a function of source and domain;…

The Impact of Teasing and Bullying on Schoolwide Academic Performance

ERIC Educational Resources Information Center

Lacey, Anna; Cornell, Dewey

2013-01-01

Hierarchical regression analyses conducted at the school level found that the perceived prevalence of teasing and bullying was predictive of schoolwide passing rates on state-mandated achievement testing used to meet No Child Left Behind requirements. These findings could not be attributed to the proportion of minority students in the school,…

Managing Perceived Stress among College Students: The Roles of Social Support and Dysfunctional Coping

ERIC Educational Resources Information Center

Chao, Ruth Chu-Lien

2012-01-01

The author examined the conditions (i.e., social support and dysfunctional coping) under which perceived stress predicted psychological well-being in 459 college students. Hierarchical regression analyses indicated a significant 2-way interaction (Perceived Stress x Social Support) and a significant 3-way interaction (Perceived Stress x Social…

Attachment and Depression Differentially Influence Nicotine Dependence among Male and Female Undergraduates: A Preliminary Study.

ERIC Educational Resources Information Center

McChargue, Dennis E.; Cohen, Lee M.; Cook, Jessica W.

2004-01-01

The authors surveyed a convenience sample of 208 undergraduate students who reported that they smoked cigarettes. The primary hypothesis they tested was whether gender predicted nicotine dependence. They further tested whether depression and attachment would mediate or moderate this relationship. Hierarchical regression analyses with social…

Predictors of quality of life for fathers and mothers of children with autistic disorder.

PubMed

Dardas, Latefa Ali; Ahmad, Muayyad M

2014-06-01

A constant challenge for Quality of Life (QoL) research is tapping the most predictive indicators for a specific population. This study has sought to examine predictors of QoL for fathers and mothers of children with Autistic Disorder. Two multiple regression analyses were performed for fathers (N=70) and mothers (N=114) of children with Autistic Disorder. Six predictors were entered into the regression equation: Parental Distress (PD), Parent-Child Dysfunction Interaction (PCDI), Difficult Child Characteristics (DC), Household income, and the child's with Autistic Disorder age and number of siblings. The analyses revealed that only PD was a significant predictor for both parent's QoL, whereas DC, household income, and number of siblings were able to predict only mothers' QoL. To our knowledge, this is the first study to focus on predictors of QoL among both fathers and mothers of children with Autistic Disorder. The results from the current study can have several implications for professionals and researchers targeting the primary force contributing to the wellbeing of children with Autistic Disorder, the parents. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

What happens at work stays at work? Workplace supervisory social interactions and blood pressure outcomes.

PubMed

Wong, Jennifer H K; Kelloway, E Kevin

2016-04-01

We investigated the relationship between workplace supervisory social interactions and blood pressure outcomes using hourly diary entries and ambulatory blood pressure data from an experience sampling study of 55 long-term care employees. After accounting for relevant cardiovascular controls, significant effects of supervisory interactions on cardiovascular reactivity and recovery were found. Multilevel analyses revealed that negatively perceived supervisory interactions predicted higher systolic blood pressure at work (B = -1.59, p < .05, N observations = 422). Using time-lagged hierarchical regression analyses, the average perceived valence of supervisory interactions at work predicted average systolic blood pressure recovery after work (B = -14.52, p < .05, N = 33). Specifically, negatively perceived supervisory interactions at work predicted poorer cardiovascular recovery after work. Suggestions for improving practices in organizations and in experience sampling research are discussed. (c) 2016 APA, all rights reserved).

Reflected appraisals and perceived importance of significant others' appraisals as predictors of college athletes' self-perceptions of competence.

PubMed

Amorose, Anthony J

2003-03-01

This study examined the reflected appraisal process with college athletes (N = 325). Specifically, the study tested (a) the relative influence of the reflected appraisals of mothers, fathers, coaches, and teammates (i.e., how athletes perceive these others view their ability) on athletes' self-perceptions of competence, and (b) whether the importance placed on these significant others as sources of competence information moderated the relationship. Based on a factor analysis, composite variables were formed representing the reflected appraisals of the athletes' parents (i.e., father, mother) and the reflected appraisals of sport-others (i.e., coach, teammates). Regression analyses revealed that the reflected appraisals of parents (beta = .21) and sport-others (beta = .55) predicted self-perceptions of competence (p < .05, R2 = .45). Follow-up analyses determined that the reflected appraisal of sport-others was a significantly stronger predictor. Hierarchical regression analyses revealed that the interaction of reflected appraisals and the importance of significant others did not significantly add to the prediction of self-perceptions of competence (p > .05, deltaR2 = .01) beyond the independent effects of these constructs. Results are discussed in terms of the reflected appraisal process and the influence of significant others on athletes' self-perceptions.

Emotional exhaustion and cognitive performance in apparently healthy teachers: a longitudinal multi-source study.

PubMed

Feuerhahn, Nicolas; Stamov-Roßnagel, Christian; Wolfram, Maren; Bellingrath, Silja; Kudielka, Brigitte M

2013-10-01

We investigate how emotional exhaustion (EE), the core component of burnout, relates to cognitive performance, job performance and health. Cognitive performance was assessed by self-rated cognitive stress symptoms, self-rated and peer-rated cognitive impairments in everyday tasks and a neuropsychological test of learning and memory (LGT-3); job performance and physical health were gauged by self-reports. Cross-sectional linear regression analyses in a sample of 100 teachers confirm that EE is negatively related to cognitive performance as assessed by self-rating and peer-rating as well as neuropsychological testing (all p < .05). Longitudinal linear regression analyses confirm similar trends (p < .10) for self-rated and peer-rated cognitive performance. Executive control deficits might explain impaired cognitive performance in EE. In longitudinal analyses, EE also significantly predicts physical health. Contrary to our expectations, EE does not affect job performance. When reversed causation is tested, none of the outcome variables at Time 1 predict EE at Time 2. This speaks against cognitive dysfunctioning serving as a vulnerability factor for exhaustion. In sum, results underpin the negative consequences of EE for cognitive performance and health, which are relevant for individuals and organizations alike. In this way, findings might contribute to the understanding of the burnout syndrome. Copyright © 2012 John Wiley & Sons, Ltd.

Do in-training evaluation reports deserve their bad reputations? A study of the reliability and predictive ability of ITER scores and narrative comments.

PubMed

Ginsburg, Shiphra; Eva, Kevin; Regehr, Glenn

2013-10-01

Although scores on in-training evaluation reports (ITERs) are often criticized for poor reliability and validity, ITER comments may yield valuable information. The authors assessed across-rotation reliability of ITER scores in one internal medicine program, ability of ITER scores and comments to predict postgraduate year three (PGY3) performance, and reliability and incremental predictive validity of attendings' analysis of written comments. Numeric and narrative data from the first two years of ITERs for one cohort of residents at the University of Toronto Faculty of Medicine (2009-2011) were assessed for reliability and predictive validity of third-year performance. Twenty-four faculty attendings rank-ordered comments (without scores) such that each resident was ranked by three faculty. Mean ITER scores and comment rankings were submitted to regression analyses; dependent variables were PGY3 ITER scores and program directors' rankings. Reliabilities of ITER scores across nine rotations for 63 residents were 0.53 for both postgraduate year one (PGY1) and postgraduate year two (PGY2). Interrater reliabilities across three attendings' rankings were 0.83 for PGY1 and 0.79 for PGY2. There were strong correlations between ITER scores and comments within each year (0.72 and 0.70). Regressions revealed that PGY1 and PGY2 ITER scores collectively explained 25% of variance in PGY3 scores and 46% of variance in PGY3 rankings. Comment rankings did not improve predictions. ITER scores across multiple rotations showed decent reliability and predictive validity. Comment ranks did not add to the predictive ability, but correlation analyses suggest that trainee performance can be measured through these comments.

Do MCAT scores predict USMLE scores? An analysis on 5 years of medical student data.

PubMed

Gauer, Jacqueline L; Wolff, Josephine M; Jackson, J Brooks

2016-01-01

The purpose of this study was to determine the associations and predictive values of Medical College Admission Test (MCAT) component and composite scores prior to 2015 with U.S. Medical Licensure Exam (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) scores, with a focus on whether students scoring low on the MCAT were particularly likely to continue to score low on the USMLE exams. Multiple linear regression, correlation, and chi-square analyses were performed to determine the relationship between MCAT component and composite scores and USMLE Step 1 and Step 2 CK scores from five graduating classes (2011-2015) at the University of Minnesota Medical School ( N =1,065). The multiple linear regression analyses were both significant ( p <0.001). The three MCAT component scores together explained 17.7% of the variance in Step 1 scores ( p< 0.001) and 12.0% of the variance in Step 2 CK scores ( p <0.001). In the chi-square analyses, significant, albeit weak associations were observed between almost all MCAT component scores and USMLE scores (Cramer's V ranged from 0.05 to 0.24). Each of the MCAT component scores was significantly associated with USMLE Step 1 and Step 2 CK scores, although the effect size was small. Being in the top or bottom scoring range of the MCAT exam was predictive of being in the top or bottom scoring range of the USMLE exams, although the strengths of the associations were weak to moderate. These results indicate that MCAT scores are predictive of student performance on the USMLE exams, but, given the small effect sizes, should be considered as part of the holistic view of the student.

Do MCAT scores predict USMLE scores? An analysis on 5 years of medical student data

PubMed Central

Gauer, Jacqueline L.; Wolff, Josephine M.; Jackson, J. Brooks

2016-01-01

Introduction The purpose of this study was to determine the associations and predictive values of Medical College Admission Test (MCAT) component and composite scores prior to 2015 with U.S. Medical Licensure Exam (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) scores, with a focus on whether students scoring low on the MCAT were particularly likely to continue to score low on the USMLE exams. Method Multiple linear regression, correlation, and chi-square analyses were performed to determine the relationship between MCAT component and composite scores and USMLE Step 1 and Step 2 CK scores from five graduating classes (2011–2015) at the University of Minnesota Medical School (N=1,065). Results The multiple linear regression analyses were both significant (p<0.001). The three MCAT component scores together explained 17.7% of the variance in Step 1 scores (p<0.001) and 12.0% of the variance in Step 2 CK scores (p<0.001). In the chi-square analyses, significant, albeit weak associations were observed between almost all MCAT component scores and USMLE scores (Cramer's V ranged from 0.05 to 0.24). Discussion Each of the MCAT component scores was significantly associated with USMLE Step 1 and Step 2 CK scores, although the effect size was small. Being in the top or bottom scoring range of the MCAT exam was predictive of being in the top or bottom scoring range of the USMLE exams, although the strengths of the associations were weak to moderate. These results indicate that MCAT scores are predictive of student performance on the USMLE exams, but, given the small effect sizes, should be considered as part of the holistic view of the student. PMID:27702431

Prediction of cold and heat patterns using anthropometric measures based on machine learning.

PubMed

Lee, Bum Ju; Lee, Jae Chul; Nam, Jiho; Kim, Jong Yeol

2018-01-01

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2014-01-01

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE. PMID:24727289

Predicting vulnerability to hopelessness. A longitudinal analysis.

PubMed

Bonner, R L; Rich, A R

1991-01-01

The role of loneliness, irrational beliefs, and deficient reasons for living in predicting vulnerability to hopelessness under conditions of negative life stress was examined. Subjects (N = 178) completed the UCLA Loneliness Scale. Rational Beliefs Inventory, and the Reasons for Living Inventory at the beginning of the semester. Then, at midterm, measures of negative life stress, depression, and hopelessness were obtained from the same subjects. It was hypothesized that the vulnerability factors would interact with negative life stress to predict hopelessness, independent of depressed mood. The results of multiple regression analyses supported this hypothesis. Implications for research, prevention, and treatment are noted.

Comparative study of contrast-enhanced ultrasound qualitative and quantitative analysis for identifying benign and malignant breast tumor lumps.

PubMed

Liu, Jian; Gao, Yun-Hua; Li, Ding-Dong; Gao, Yan-Chun; Hou, Ling-Mi; Xie, Ting

2014-01-01

To compare the value of contrast-enhanced ultrasound (CEUS) qualitative and quantitative analysis in the identification of breast tumor lumps. Qualitative and quantitative indicators of CEUS for 73 cases of breast tumor lumps were retrospectively analyzed by univariate and multivariate approaches. Logistic regression was applied and ROC curves were drawn for evaluation and comparison. The CEUS qualitative indicator-generated regression equation contained three indicators, namely enhanced homogeneity, diameter line expansion and peak intensity grading, which demonstrated prediction accuracy for benign and malignant breast tumor lumps of 91.8%; the quantitative indicator-generated regression equation only contained one indicator, namely the relative peak intensity, and its prediction accuracy was 61.5%. The corresponding areas under the ROC curve for qualitative and quantitative analyses were 91.3% and 75.7%, respectively, which exhibited a statistically significant difference by the Z test (P<0.05). The ability of CEUS qualitative analysis to identify breast tumor lumps is better than with quantitative analysis.

Mental health indicator interaction in predicting substance abuse treatment outcomes in nevada.

PubMed

Greenfield, Lawrence; Wolf-Branigin, Michael

2009-01-01

Indicators of co-occurring mental health and substance abuse problems routinely collected at treatment admission in 19 State substance abuse treatment systems include a dual diagnosis and a State mental health (cognitive impairment) agency referral. These indicators have yet to be compared as predictors of treatment outcomes. 1. Compare both indices as outcomes predictors individually and interactively. 2. Assess relationship of both indices to other client risk factors, e.g., physical/sexual abuse. Client admission and discharge records from the Nevada substance abuse treatment program, spanning 1995-2001 were reviewed (n = 17,591). Logistic regression analyses predicted treatment completion with significant improvement (33%) and treatment readmission following discharge (21%). Using Cox regression, the number of days from discharge to treatment readmission was predicted. Examined as predictors were two mental health indicators and their interaction with other admission and treatment variables controlled. Neither mental health indicator alone significantly predicted any of the three outcomes; however, the interaction between the two indicators significantly predicted each outcome (p < .05). Having both indices was highly associated with physical/sexual abuse, domestic violence, homelessness, out of labor force and prior treatment. Indicator interactions may help improve substance abuse treatment outcomes prediction.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

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

Developing a dengue forecast model using machine learning: A case study in China.

PubMed

Guo, Pi; Liu, Tao; Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-10-01

In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

Are low wages risk factors for hypertension?

PubMed Central

Du, Juan

2012-01-01

Objective: Socio-economic status (SES) is strongly correlated with hypertension. But SES has several components, including income and correlations in cross-sectional data need not imply SES is a risk factor. This study investigates whether wages—the largest category within income—are risk factors. Methods: We analysed longitudinal, nationally representative US data from four waves (1999, 2001, 2003 and 2005) of the Panel Study of Income Dynamics. The overall sample was restricted to employed persons age 25–65 years, n = 17 295. Separate subsamples were constructed of persons within two age groups (25–44 and 45–65 years) and genders. Hypertension incidence was self-reported based on physician diagnosis. Our study was prospective since data from three base years (1999, 2001, 2003) were used to predict newly diagnosed hypertension for three subsequent years (2001, 2003, 2005). In separate analyses, data from the first base year were used to predict time-to-reporting hypertension. Logistic regressions with random effects and Cox proportional hazards regressions were run. Results: Negative and strongly statistically significant correlations between wages and hypertension were found both in logistic and Cox regressions, especially for subsamples containing the younger age group (25–44 years) and women. Correlations were stronger when three health variables—obesity, subjective measures of health and number of co-morbidities—were excluded from regressions. Doubling the wage was associated with 25–30% lower chances of hypertension for persons aged 25–44 years. Conclusions: The strongest evidence for low wages being risk factors for hypertension among working people were for women and persons aged 25–44 years. PMID:22262559

Are low wages risk factors for hypertension?

PubMed

Leigh, J Paul; Du, Juan

2012-12-01

Socio-economic status (SES) is strongly correlated with hypertension. But SES has several components, including income and correlations in cross-sectional data need not imply SES is a risk factor. This study investigates whether wages-the largest category within income-are risk factors. We analysed longitudinal, nationally representative US data from four waves (1999, 2001, 2003 and 2005) of the Panel Study of Income Dynamics. The overall sample was restricted to employed persons age 25-65 years, n = 17 295. Separate subsamples were constructed of persons within two age groups (25-44 and 45-65 years) and genders. Hypertension incidence was self-reported based on physician diagnosis. Our study was prospective since data from three base years (1999, 2001, 2003) were used to predict newly diagnosed hypertension for three subsequent years (2001, 2003, 2005). In separate analyses, data from the first base year were used to predict time-to-reporting hypertension. Logistic regressions with random effects and Cox proportional hazards regressions were run. Negative and strongly statistically significant correlations between wages and hypertension were found both in logistic and Cox regressions, especially for subsamples containing the younger age group (25-44 years) and women. Correlations were stronger when three health variables-obesity, subjective measures of health and number of co-morbidities-were excluded from regressions. Doubling the wage was associated with 25-30% lower chances of hypertension for persons aged 25-44 years. The strongest evidence for low wages being risk factors for hypertension among working people were for women and persons aged 25-44 years.

[Psychosocial risk factors at work as predictors of mobbing].

PubMed

Meseguer de Pedro, Mariano; Soler Sánchez, María I; García-Izquierdo, Mariano; Sáez Navarro, M C; Sánchez Meca, Julio

2007-05-01

This work analyses the way in which various psychosocial risk indicators may predict mobbing. A sample of 638 workers, 168 men and 470 women, from the fruit-and-vegetable sector was evaluated. An anonymous questionnaire was administered to all employees who were present on the evaluation days in the companies comprising the study. After analysing the data obtained with the mobbing questionnaire NAQ-RE (Sáez, García-Izquierdo, and Llor, 2003) and with the psychosocial risk factors evaluation method of the INSHT (Martín and Pérez, 1997), using canonical regression, we found that several psychosocial factors such as role definition, mental workload, interest in the workers, and supervision / participation predict two types of mobbing: personal mobbing and work-performance-related mobbing.

Alcohol and tobacco use and cognitive-motivational variables in school settings: effects on academic performance in Spanish adolescents.

PubMed

Inglés, Cándido J; Torregrosa, María S; Rodríguez-Marín, Jesús; García del Castillo, José A; Gázquez, José J; García-Fernández, José M; Delgado, Beatriz

2013-01-01

The aim of the present study was to analyze: (a) the relationship between alcohol and tobacco use and academic performance, and (b) the predictive role of psycho-educational factors and alcohol and tobacco abuse on academic performance in a sample of 352 Spanish adolescents from grades 8 to 10 of Compulsory Secondary Education. The Self-Description Questionnaire-II, the Sydney Attribution Scale, and the Achievement Goal Tendencies Questionnaire were administered in order to analyze cognitive-motivational variables. Alcohol and tobacco abuse, sex, and grade retention were also measured using self-reported questions. Academic performance was measured by school records. Frequency analyses and logistic regression analyses were used. Frequency analyses revealed that students who abuse of tobacco and alcohol show a higher rate of poor academic performance. Logistic regression analyses showed that health behaviours, and educational and cognitive-motivational variables exert a different effect on academic performance depending on the academic area analyzed. These results point out that not only academic, but also health variables should be address to improve academic performance in adolescence.

Variability in in vitro fertilization outcomes of prepubertal goat oocytes explained by basic semen analyses.

PubMed

Palomo, M J; Quintanilla, R; Izquierdo, M D; Mogas, T; Paramio, M T

2016-12-01

This work analyses the changes that caprine spermatozoa undergo during in vitro fertilization (IVF) of in vitro matured prepubertal goat oocytes and their relationship with IVF outcome, in order to obtain an effective model that allows prediction of in vitro fertility on the basis of semen assessment. The evolution of several sperm parameters (motility, viability and acrosomal integrity) during IVF and their relationship with three IVF outcome criteria (total penetration, normal penetration and cleavage rates) were studied in a total of 56 IVF replicates. Moderate correlation coefficients between some sperm parameters and IVF outcome were observed. In addition, stepwise multiple regression analyses were conducted that considered three grouping of sperm parameters as potential explanatory variables of the three IVF outcome criteria. The proportion of IVF outcome variation that can be explained by the fitted models ranged from 0.62 to 0.86, depending upon the trait analysed and the variables considered. Seven out of 32 sperm parameters were selected as partial covariates in at least one of the nine multiple regression models. Among these, progressive sperm motility assessed immediately after swim-up, the percentage of dead sperm with intact acrosome and the incidence of acrosome reaction both determined just before the gamete co-culture, and finally the proportion of viable spermatozoa at 17 h post-insemination were the most frequently selected sperm parameters. Nevertheless, the predictive ability of these models must be confirmed in a larger sample size experiment.

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

Motivators of Adult Women Enrolled in a Community College

ERIC Educational Resources Information Center

Johnston, Connie Dianne

2010-01-01

The goal of this study was to describe what motivates adult women enrolled in a community college to pursue higher education. Utilizing profile analysis and multiple regression analyses, this study investigated the extent to which gender, English as a first language, and age predicted the seven factors of the Education Participation Scale (A-form)…

Diameter and height growth of suppressed grand fir saplings after overstory removal.

Treesearch

K.W. Seidel

1980-01-01

The 2- and 5-year diameter and height growth of suppressed grand fir (Abies grandis (Dougl. ex D. Don) Lindl.) advance reproduction was measured in central Oregon after the overstory was removed. Multiple regression analyses were used to predict growth response as a function of individual tree variables. The resulting equations, although highly...

Spelling Ability in College Students Predicted by Decoding, Print Exposure, and Vocabulary

ERIC Educational Resources Information Center

Ocal, Turkan; Ehri, Linnea

2017-01-01

This study examines students' exposure to print, vocabulary and decoding as predictors of spelling skills. Participants were 42 college students (Mean age 22.5, SD = 7.87; 31 females and 11 males). Hierarchical regression analyses showed that most of the variance in spelling was explained by vocabulary knowledge. When vocabulary was entered first…

Couples at Risk Following the Death of Their Child: Predictors of Grief versus Depression

ERIC Educational Resources Information Center

Wijngaards-de Meij, Leoniek; Stroebe, Margaret; Schut, Henk; Stroebe, Wolfgang; van den Bout, Jan; van der Heijden, Peter; Dijkstra, Iris

2005-01-01

This longitudinal study examined the relative impact of major variables for predicting adjustment (in terms of both grief and depression) among bereaved parents following the death of their child. Couples (N = 219) participated 6, 13, and 20 months postloss. Use of multilevel regression analyses enabled assessment of the impact of several…

Learner Characteristics Predict Performance and Confidence in E-Learning: An Analysis of User Behavior and Self-Evaluation

ERIC Educational Resources Information Center

Jeske, Debora; Roßnagell, Christian Stamov; Backhaus, Joy

2014-01-01

We examined the role of learner characteristics as predictors of four aspects of e-learning performance, including knowledge test performance, learning confidence, learning efficiency, and navigational effectiveness. We used both self reports and log file records to compute the relevant statistics. Regression analyses showed that both need for…

Finite Element Creep Damage Analyses and Life Prediction of P91 Pipe Containing Local Wall Thinning Defect

NASA Astrophysics Data System (ADS)

Xue, Jilin; Zhou, Changyu

2016-03-01

Creep continuum damage finite element (FE) analyses were performed for P91 steel pipe containing local wall thinning (LWT) defect subjected to monotonic internal pressure, monotonic bending moment and combined internal pressure and bending moment by orthogonal experimental design method. The creep damage lives of pipe containing LWT defect under different load conditions were obtained. Then, the creep damage life formulas were regressed based on the creep damage life results from FE method. At the same time a skeletal point rupture stress was found and used for life prediction which was compared with creep damage lives obtained by continuum damage analyses. From the results, the failure lives of pipe containing LWT defect can be obtained accurately by using skeletal point rupture stress method. Finally, the influence of LWT defect geometry was analysed, which indicated that relative defect depth was the most significant factor for creep damage lives of pipe containing LWT defect.

Predictors of Prosocial Behavior among Chinese High School Students in Hong Kong

PubMed Central

Siu, Andrew M. H.; Shek, Daniel T. L.; Lai, Frank H. Y.

2012-01-01

This study examined the correlates and predictors of prosocial behavior among Chinese adolescents in Hong Kong. A sample of 518 high school students responded to a questionnaire containing measures of antisocial and prosocial behavior, prosocial norms, pragmatic values, moral reasoning, and empathy. Preliminary analyses showed that there were gender differences in some of the measures. While correlation analyses showed that parental education, prosocial norms, pragmatic values, moral reasoning, and empathy were related to prosocial behavior, regression analyses showed that prosocial norms, pragmatic values, and empathy dimensions (personal distress and empathy) were key predictors of it. The findings are largely consistent with theoretical predictions and previous research findings, other than the negative relationship between personal distress and prosocial behavior. The study also underscores the importance of values and norms in predicting prosocial behavior, which has been largely neglected in previous studies. PMID:22919326

Social vulnerability and bullying in children with Asperger syndrome.

PubMed

Sofronoff, Kate; Dark, Elizabeth; Stone, Valerie

2011-05-01

Children with Asperger syndrome (AS) have IQ within the normal range but specific impairments in theory of mind, social interaction and communication skills. The majority receive education in mainstream schools and research suggests they are bullied more than typically developing peers. The current study aimed to evaluate factors that predict bullying for such children and also to examine a new measure, the Social Vulnerability Scale (SVS). One hundred and thirty three parents of children with AS completed the SVS and of these 92 parents completed both the SVS and questionnaires measuring anxiety, anger, behaviour problems, social skills and bullying. Regression analyses revealed that these variables together strongly predicted bullying, but that social vulnerability was the strongest predictor. Test-re-test and internal consistency analyses of the SVS demonstrated sound psychometric properties and factor analyses revealed two sub-scales: gullibility and credulity. Limitations of the study are acknowledged and suggestions for future research discussed.

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.

Mining hidden data to predict patient prognosis: texture feature extraction and machine learning in mammography

NASA Astrophysics Data System (ADS)

Leighs, J. A.; Halling-Brown, M. D.; Patel, M. N.

2018-03-01

The UK currently has a national breast cancer-screening program and images are routinely collected from a number of screening sites, representing a wealth of invaluable data that is currently under-used. Radiologists evaluate screening images manually and recall suspicious cases for further analysis such as biopsy. Histological testing of biopsy samples confirms the malignancy of the tumour, along with other diagnostic and prognostic characteristics such as disease grade. Machine learning is becoming increasingly popular for clinical image classification problems, as it is capable of discovering patterns in data otherwise invisible. This is particularly true when applied to medical imaging features; however clinical datasets are often relatively small. A texture feature extraction toolkit has been developed to mine a wide range of features from medical images such as mammograms. This study analysed a dataset of 1,366 radiologist-marked, biopsy-proven malignant lesions obtained from the OPTIMAM Medical Image Database (OMI-DB). Exploratory data analysis methods were employed to better understand extracted features. Machine learning techniques including Classification and Regression Trees (CART), ensemble methods (e.g. random forests), and logistic regression were applied to the data to predict the disease grade of the analysed lesions. Prediction scores of up to 83% were achieved; sensitivity and specificity of the models trained have been discussed to put the results into a clinical context. The results show promise in the ability to predict prognostic indicators from the texture features extracted and thus enable prioritisation of care for patients at greatest risk.

Short- and long-term theory-based predictors of physical activity in women who participated in a weight-management program.

PubMed

Wasserkampf, A; Silva, M N; Santos, I C; Carraça, E V; Meis, J J M; Kremers, S P J; Teixeira, P J

2014-12-01

This study analyzed psychosocial predictors of the Theory of Planned Behavior (TPB) and Self-Determination Theory (SDT) and evaluated their associations with short- and long-term moderate plus vigorous physical activity (MVPA) and lifestyle physical activity (PA) outcomes in women who underwent a weight-management program. 221 participants (age 37.6 ± 7.02 years) completed a 12-month SDT-based lifestyle intervention and were followed-up for 24 months. Multiple linear regression analyses tested associations between psychosocial variables and self-reported short- and long-term PA outcomes. Regression analyses showed that control constructs of both theories were significant determinants of short- and long-term MVPA, whereas affective and self-determination variables were strong predictors of short- and long-term lifestyle PA. Regarding short-term prediction models, TPB constructs were stronger in predicting MVPA, whereas SDT was more effective in predicting lifestyle PA. For long-term models, both forms of PA were better predicted by SDT in comparison to TPB. These results highlight the importance of comparing health behavior theories to identify the mechanisms involved in the behavior change process. Control and competence constructs are crucial during early adoption of structured PA behaviors, whereas affective and intrinsic sources of motivation are more involved in incidental types of PA, particularly in relation to behavioral maintenance. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Comparison of enzyme-linked immunosorbent assay and gas chromatography procedures for the detection of cyanazine and metolachlor in surface water samples

USGS Publications Warehouse

Schraer, S.M.; Shaw, D.R.; Boyette, M.; Coupe, R.H.; Thurman, E.M.

2000-01-01

Enzyme-linked immunosorbent assay (ELISA) data from surface water reconnaissance were compared to data from samples analyzed by gas chromatography for the pesticide residues cyanazine (2-[[4-chloro-6-(ethylamino)-l,3,5-triazin-2-yl]amino]-2-methylpropanenitrile ) and metolachlor (2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide). When ELISA analyses were duplicated, cyanazine and metolachlor detection was found to have highly reproducible results; adjusted R2s were 0.97 and 0.94, respectively. When ELISA results for cyanazine were regressed against gas chromatography results, the models effectively predicted cyanazine concentrations from ELISA analyses (adjusted R2s ranging from 0.76 to 0.81). The intercepts and slopes for these models were not different from 0 and 1, respectively. This indicates that cyanazine analysis by ELISA is expected to give the same results as analysis by gas chromatography. However, regressing ELISA analyses for metolachlor against gas chromatography data provided more variable results (adjusted R2s ranged from 0.67 to 0.94). Regression models for metolachlor analyses had two of three intercepts that were not different from 0. Slopes for all metolachlor regression models were significantly different from 1. This indicates that as metolachlor concentrations increase, ELISA will over- or under-estimate metolachlor concentration, depending on the method of comparison. ELISA can be effectively used to detect cyanazine and metolachlor in surface water samples. However, when detections of metolachlor have significant consequences or implications it may be necessary to use other analytical methods.

Personality, cognitive appraisal and adjustment in chronic pain patients.

PubMed

Herrero, Ana M; Ramírez-Maestre, Carmen; González, Vanessa

2008-11-01

This study investigated the relationship between clinical personality patterns and cognitive appraisal as well as their repercussions on adjustment to chronic pain in a sample of 91 patients. It was predicted that clinical personality patterns would be related to adjustment and cognitive appraisal processes, whereas cognitive appraisals would be related to anxiety, depression and levels of perceived pain. The instruments used were as follows: the Millon Clinical Multiaxial Inventory, the Cognitive Appraisal Questionnaire, the Hospital Anxiety and Depression Scale, and the McGill Pain Questionnaire. Multiple regression analyses, the Kruskal-Wallis test, and the Mann Whitney U-test were used to analyse the data obtained. The results show that certain clinical personality patterns were associated with poor adjustment to chronic pain. The use of cognitive appraisal of harm predicted higher anxiety levels and greater perceived pain in chronic pain patients. The use of cognitive appraisals of challenge predicted lower depression levels.

Age of acquisition predicts naming and lexical-decision performance above and beyond 22 other predictor variables: an analysis of 2,342 words.

PubMed

Cortese, Michael J; Khanna, Maya M

2007-08-01

Age of acquisition (AoA) ratings were obtained and were used in hierarchical regression analyses to predict naming and lexical-decision performance for 2,342 words (from Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004). In the analyses, AoA was included in addition to the set of predictors used by Balota et al. (2004). AoA significantly predicted latency performance on both tasks above and beyond the standard predictor set. However, AoA was more strongly related to lexical-decision performance than to naming performance. Finally, the previously reported effect of imageability on naming latencies by Balota et al. was not significant with AoA included as a factor. These results are consistent with the idea either that AoA has a semantic/lexical locus or that AoA effects emerge primarily in situations in which the input-output mapping is arbitrary.

When Significant Others Suffer: German Validation of the Burden Assessment Scale (BAS)

PubMed Central

Hunger, Christina; Krause, Lena; Hilzinger, Rebecca; Ditzen, Beate; Schweitzer, Jochen

2016-01-01

There is a need of an economical, reliable, and valid instrument in the German-speaking countries to measure the burden of relatives who care for mentally ill persons. We translated the Burden Assessment Scale (BAS) and conducted a study investigating factor structure, psychometric quality and predictive validity. We used confirmative factor analyses (CFA, maximum-likelihood method) to examine the dimensionality of the German BAS in a sample of 215 relatives (72% women; M = 32 years, SD = 14, range: 18 to 77; 39% employed) of mentally ill persons (50% (ex-)partner or (best) friend; M = 32 years, SD = 13, range 8 to 64; main complaints were depression and/or anxiety). Cronbach’s α determined the internal consistency. We examined predictive validity using regression analyses including the BAS and validated scales of social systems functioning (Experience In Social Systems Questionnaire, EXIS.pers, EXIS.org) and psychopathology (Brief Symptom Inventory, BSI). Variables that might have influenced the dependent variables (e.g. age, gender, education, employment and civil status) were controlled by their introduction in the first step, and the BAS in the second step of the regression analyses. A model with four correlated factors (Disrupted Activities, Personal Distress, Time Perspective, Guilt) showed the best fit. With respect to the number of items included, the internal consistency was very good. The modified German BAS predicted relatives’ social systems functioning and psychopathology. The economical design makes the 19-item BAS promising for practice-oriented research, and for studies under time constraints. Strength, limitations and future directions are discussed. PMID:27764109

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.

The relative importance of two different mathematical abilities to mathematical achievement.

PubMed

Nunes, Terezinha; Bryant, Peter; Barros, Rossana; Sylva, Kathy

2012-03-01

Two distinct abilities, mathematical reasoning and arithmetic skill, might make separate and specific contributions to mathematical achievement. However, there is little evidence to inform theory and educational practice on this matter. The aims of this study were (1) to assess whether mathematical reasoning and arithmetic make independent contributions to the longitudinal prediction of mathematical achievement over 5 years and (2) to test the specificity of this prediction. Data from Avon Longitudinal Study of Parents and Children (ALSPAC) were available on 2,579 participants for analyses of KS2 achievement and on 1,680 for the analyses of KS3 achievement. Hierarchical regression analyses were used to assess the independence and specificity of the contribution of mathematical reasoning and arithmetic skill to the prediction of achievement in KS2 and KS3 mathematics, science, and English. Age, intelligence, and working memory (WM) were controls in these analyses. Mathematical reasoning and arithmetic did make independent contributions to the prediction of mathematical achievement; mathematical reasoning was by far the stronger predictor of the two. These predictions were specific in so far as these measures were more strongly related to mathematics than to science or English. Intelligence and WM were non-specific predictors; intelligence contributed more to the prediction of science than of maths, and WM predicted maths and English equally well. There is clear justification for making a distinction between mathematical reasoning and arithmetic skills. The implication is that schools must plan explicitly to improve mathematical reasoning as well as arithmetic skills. ©2011 The British Psychological Society.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Is the Predictability of New-Onset Postpartum Depression Better During Pregnancy or in the Early Postpartum Period? A Prospective Study in Croatian Women.

PubMed

Nakić Radoš, Sandra; Herman, Radoslav; Tadinac, Meri

2016-01-01

The researchers' aim was to examine whether it was better to predict new-onset postpartum depression (PPD) during pregnancy or immediately after childbirth. A prospective study conducted in Croatia followed women (N = 272) from the third trimester of pregnancy through the early postpartum period (within the first 3 postpartum days), to 6 weeks postpartum. Questionnaires on depression, anxiety, stress, coping, self-esteem, and social support were administered. Through regression analyses we showed that PPD symptoms could be equally predicted by variables from pregnancy (30.3%) and the early postpartum period (34.0%), with a small advantage of PPD prediction in the early postpartum period.

Predicting hypothetical willingness to participate (WTP) in a future phase III HIV vaccine trial among high-risk adolescents.

PubMed

Giocos, Georgina; Kagee, Ashraf; Swartz, Leslie

2008-11-01

The present study sought to determine whether the Theory of Planned Behaviour predicted stated hypothetical willingness to participate (WTP) in future Phase III HIV vaccine trials among South African adolescents. Hierarchical logistic regression analyses showed that The Theory of Planned Behaviour (TPB) significantly predicted WTP. Of all the predictors, Subjective norms significantly predicted WTP (OR = 1.19, 95% C.I. = 1.06-1.34). A stepwise logistic regression analysis revealed that Subjective Norms (OR = 1.19, 95% C.I. = 1.07-1.34) and Attitude towards participation in an HIV vaccine trial (OR = 1.32, 95% C.I. = 1.00-1.74) were significant predictors of WTP. The addition of Knowledge of HIV vaccines and HIV vaccine trials, Perceived self-risk of HIV infection, Health-promoting behaviours and Attitudes towards HIV/AIDS yielded non-significant results. These findings provide support for the Theory of Reasoned Action (TRA) and suggest that psychosocial factors may play an important role in WTP in Phase III HIV vaccine trials among adolescents.

Undergraduates' intentions to take a second language proficiency test: a comparison of predictions from the theory of planned behavior and social cognitive theory.

PubMed

Lin, Bih-Jiau; Chiou, Wen-Bin

2010-06-01

English competency has become essential for obtaining a better job or succeeding in higher education in Taiwan. Thus, passing the General English Proficiency Test is important for college students in Taiwan. The current study applied Ajzen's theory of planned behavior and the notions of outcome expectancy and self-efficacy from Bandura's social cognitive theory to investigate college students' intentions to take the General English Proficiency Test. The formal sample consisted of 425 undergraduates (217 women, 208 men; M age = 19.5 yr., SD = 1.3). The theory of planned behavior showed greater predictive ability (R2 = 33%) of intention than the social cognitive theory (R2 = 7%) in regression analysis and made a unique contribution to prediction of actual test-taking behavior one year later in logistic regression. Within-model analyses indicated that subjective norm in theory of planned behavior and outcome expectancy in social cognitive theory are crucial factors in predicting intention. Implications for enhancing undergraduates' intentions to take the English proficiency test are discussed.

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows.

PubMed

Visentin, G; McDermott, A; McParland, S; Berry, D P; Kenny, O A; Brodkorb, A; Fenelon, M A; De Marchi, M

2015-09-01

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predictors of success of external cephalic version and cephalic presentation at birth among 1253 women with non-cephalic presentation using logistic regression and classification tree analyses.

PubMed

Hutton, Eileen K; Simioni, Julia C; Thabane, Lehana

2017-08-01

Among women with a fetus with a non-cephalic presentation, external cephalic version (ECV) has been shown to reduce the rate of breech presentation at birth and cesarean birth. Compared with ECV at term, beginning ECV prior to 37 weeks' gestation decreases the number of infants in a non-cephalic presentation at birth. The purpose of this secondary analysis was to investigate factors associated with a successful ECV procedure and to present this in a clinically useful format. Data were collected as part of the Early ECV Pilot and Early ECV2 Trials, which randomized 1776 women with a fetus in breech presentation to either early ECV (34-36 weeks' gestation) or delayed ECV (at or after 37 weeks). The outcome of interest was successful ECV, defined as the fetus being in a cephalic presentation immediately following the procedure, as well as at the time of birth. The importance of several factors in predicting successful ECV was investigated using two statistical methods: logistic regression and classification and regression tree (CART) analyses. Among nulliparas, non-engagement of the presenting part and an easily palpable fetal head were independently associated with success. Among multiparas, non-engagement of the presenting part, gestation less than 37 weeks and an easily palpable fetal head were found to be independent predictors of success. These findings were consistent with results of the CART analyses. Regardless of parity, descent of the presenting part was the most discriminating factor in predicting successful ECV and cephalic presentation at birth. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Predicting stress in pre-registration nursing students.

PubMed

Pryjmachuk, Steven; Richards, David A

2007-02-01

To determine which variables from a pool of potential predictors predict General Health Questionnaire 'caseness' in pre-registration nursing students. Cross-sectional survey, utilizing self-report measures of sources of stress, stress (psychological distress) and coping, together with pertinent demographic measures such as sex, ethnicity, educational programme and nursing specialty being pursued, and age, social class and highest qualifications on entry to the programme. Questionnaire packs were distributed to all pre-registration nursing students (N=1,362) in a large English university. Completed packs were coded, entered into statistical software and subjected to a series of logistic regression analyses. Of the questionnaire packs 1,005 (74%) were returned, of which up to 973 were available for the regression analyses undertaken. Four logistic regression models were considered and, on the principle of parsimony, a single model was chosen for discussion. This model suggested that the key predictors of caseness in the population studied were self-report of pressure, whether or not respondents had children (specifically, whether these children were pre-school or school-age), scores on a 'personal problems' scale and the type of coping employed. The overall caseness rate among the population was around one-third. Since self-report and personal, rather than academic, concerns predict stress, personal teachers need to play a key role in supporting students through 'active listening', especially when students self-report high levels of stress and where personal/social problems are evident. The work-life balance of students, especially those with child-care responsibilities, should be a central tenet in curriculum design in nurse education (and, indeed, the education of other professional and occupational groups). There may be some benefit in offering stress management (coping skills) training to nursing students and, indeed, students of other disciplines.

Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

PubMed

Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

2018-02-01

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.

Human Papillomavirus (HPV) Vaccination and Adolescent Girls' Knowledge and Sexuality in Western Uganda: A Comparative Cross-Sectional Study.

PubMed

Turiho, Andrew Kampikaho; Muhwezi, Wilson Winston; Okello, Elialilia Sarikiaeli; Tumwesigye, Nazarius Mbona; Banura, Cecil; Katahoire, Anne Ruhweza

2015-01-01

The purpose of the study was to investigate the influence of human papillomavirus (HPV) vaccination on adolescent girls' knowledge of HPV and HPV vaccine, perception of sexual risk and intentions for sexual debut. This cross-sectional comparative study was conducted in Ibanda and Mbarara districts. Data was collected using a standardized self-administered questionnaire and analyzed using the Statistical Package for the Social Sciences computer software. Univariate, bivariate, and logistic regression analyses were conducted with significance level set at p < .05. Results showed that HPV vaccination was associated with being knowledgeable (Crude OR: 5.26, CI: 2.32-11.93; p = 0.000). Vaccination against HPV did not predict perception of sexual risk. Knowledge was low (only 87/385 or 22.6% of vaccinated girls were knowledgeable), but predicted perception of a high sexual risk (Adjusted OR: 3.12, CI: 1.37-3.63; p = 0.008). HPV vaccination, knowledge and perceived sexual risk did not predict sexual behaviour intentions. High parental communication was associated with adolescent attitudes that support postponement of sexual debut in both bivariate and multiple regression analyses. In conclusion, findings of this study suggest that HPV vaccination is not likely to encourage adolescent sexual activity. Influence of knowledge on sexual behaviour intentions was not definitively explained. Prospective cohort studies were proposed to address the emerging questions.

Relationships of protective factors to stress and symptoms of illness.

PubMed

Dolbier, Christyn L; Smith, Shanna E; Steinhardt, Mary A

2007-01-01

To examine relationships of work and individual protective factors to health outcomes. Participants from 2 corporate samples completed measures of supervisor support, hardiness, coping, global stress, and symptoms of illness. Regression analyses indicated that higher scores on hardiness and approach coping and being male predicted lower scores on stress and symptoms of illness. Additionally, supervisor support predicted fewer symptoms of illness but did not have a spillover effect onto stress. Interventions that enhance individual protective factors primarily and work protective factors secondarily may be most effective in reducing stress and illness among employees.

The contributions of handedness and working memory to episodic memory.

PubMed

Sahu, Aparna; Christman, Stephen D; Propper, Ruth E

2016-11-01

Past studies have independently shown associations of working memory and degree of handedness with episodic memory retrieval. The current study takes a step ahead by examining whether handedness and working memory independently predict episodic memory. In agreement with past studies, there was an inconsistent-handed advantage for episodic memory; however, this advantage was absent for working memory tasks. Furthermore, regression analyses showed handedness, and complex working memory predicted episodic memory performance at different times. Results are discussed in light of theories of episodic memory and hemispheric interaction.

Abdominal girth and vertebral column length aid in predicting intrathecal hyperbaric bupivacaine dose for elective cesarean section

PubMed Central

Wei, Chang-Na; Zhou, Qing-He; Wang, Li-Zhong

2017-01-01

Abstract Currently, there is no consensus on how to determine the optimal dose of intrathecal bupivacaine for an individual undergoing an elective cesarean section. In this study, we developed a regression equation between intrathecal 0.5% hyperbaric bupivacaine volume and abdominal girth and vertebral column length, to determine a suitable block level (T5) for elective cesarean section patients. In phase I, we analyzed 374 parturients undergoing an elective cesarean section that received a suitable dose of intrathecal 0.5% hyperbaric bupivacaine after a combined spinal-epidural (CSE) was performed at the L3/4 interspace. Parturients with T5 blockade to pinprick were selected for establishing the regression equation between 0.5% hyperbaric bupivacaine volume and vertebral column length and abdominal girth. Six parturient and neonatal variables, intrathecal 0.5% hyperbaric bupivacaine volume, and spinal anesthesia spread were recorded. Bivariate line correlation analyses, multiple line regression analyses, and 2-tailed t tests or chi-square test were performed, as appropriate. In phase II, another 200 parturients with CSE for elective cesarean section were enrolled to verify the accuracy of the regression equation. In phase I, a total of 143 parturients were selected to establish the following regression equation: YT5 = 0.074X1 − 0.022X2 − 0.017 (YT5 = 0.5% hyperbaric bupivacaine volume for T5 block level; X1 = vertebral column length; and X2 = abdominal girth). In phase II, a total of 189 participants were enrolled in the study to verify the accuracy of the regression equation, and 155 parturients with T5 blockade were deemed eligible, which accounted for 82.01% of all participants. This study evaluated parturients with T5 blockade to pinprick after a CSE for elective cesarean section to establish a regression equation between parturient vertebral column length and abdominal girth and 0.5% hyperbaric intrathecal bupivacaine volume. This equation can accurately predict the suitable intrathecal hyperbaric bupivacaine dose for elective cesarean section. PMID:28834913

Abdominal girth and vertebral column length aid in predicting intrathecal hyperbaric bupivacaine dose for elective cesarean section.

PubMed

Wei, Chang-Na; Zhou, Qing-He; Wang, Li-Zhong

2017-08-01

Currently, there is no consensus on how to determine the optimal dose of intrathecal bupivacaine for an individual undergoing an elective cesarean section. In this study, we developed a regression equation between intrathecal 0.5% hyperbaric bupivacaine volume and abdominal girth and vertebral column length, to determine a suitable block level (T5) for elective cesarean section patients.In phase I, we analyzed 374 parturients undergoing an elective cesarean section that received a suitable dose of intrathecal 0.5% hyperbaric bupivacaine after a combined spinal-epidural (CSE) was performed at the L3/4 interspace. Parturients with T5 blockade to pinprick were selected for establishing the regression equation between 0.5% hyperbaric bupivacaine volume and vertebral column length and abdominal girth. Six parturient and neonatal variables, intrathecal 0.5% hyperbaric bupivacaine volume, and spinal anesthesia spread were recorded. Bivariate line correlation analyses, multiple line regression analyses, and 2-tailed t tests or chi-square test were performed, as appropriate. In phase II, another 200 parturients with CSE for elective cesarean section were enrolled to verify the accuracy of the regression equation.In phase I, a total of 143 parturients were selected to establish the following regression equation: YT5 = 0.074X1 - 0.022X2 - 0.017 (YT5 = 0.5% hyperbaric bupivacaine volume for T5 block level; X1 = vertebral column length; and X2 = abdominal girth). In phase II, a total of 189 participants were enrolled in the study to verify the accuracy of the regression equation, and 155 parturients with T5 blockade were deemed eligible, which accounted for 82.01% of all participants.This study evaluated parturients with T5 blockade to pinprick after a CSE for elective cesarean section to establish a regression equation between parturient vertebral column length and abdominal girth and 0.5% hyperbaric intrathecal bupivacaine volume. This equation can accurately predict the suitable intrathecal hyperbaric bupivacaine dose for elective cesarean section.

Academic Procrastination and the Performance of Graduate-Level Cooperative Groups in Research Methods Courses

ERIC Educational Resources Information Center

Jiao, Qun G.; DaRos-Voseles, Denise A.; Collins, Kathleen M. T.; Onwuegbuzie, Anthony J.

2011-01-01

This study examined the extent to which academic procrastination predicted the performance of cooperative groups in graduate-level research methods courses. A total of 28 groups was examined (n = 83 students), ranging in size from 2 to 5 (M = 2.96, SD = 1.10). Multiple regression analyses revealed that neither within-group mean nor within-group…

Implications of Interactions among Society, Education and Technology: A Comparison of Multiple Linear Regression and Multilevel Modeling in Mathematics Achievement Analyses

ERIC Educational Resources Information Center

Deering, Pamela Rose

2014-01-01

This research compares and contrasts two approaches to predictive analysis of three years' of school district data to investigate relationships between student and teacher characteristics and math achievement as measured by the state-mandated Maryland School Assessment mathematics exam. The sample for the study consisted of 3,514 students taught…

Does personality influence job acquisition and tenure in people with severe mental illness enrolled in supported employment programs?

PubMed

Fortin, Guillaume; Lecomte, Tania; Corbière, Marc

2017-06-01

When employment difficulties in people with severe mental illness (SMI) occur, it could be partly linked to issues not specific to SMI, such as personality traits or problems. Despite the fact that personality has a marked influence on almost every aspect of work behavior, it has scarcely been investigated in the context of employment for people with SMI. We aimed to evaluate if personality was more predictive than clinical variables of different competitive work outcomes, namely acquisition of competitive employment, delay to acquisition and job tenure. A sample of 82 people with a SMI enrolled in supported employment programs (SEP) was recruited and asked to complete various questionnaires and interviews. Statistical analyses included logistic regressions and survival analyses (Cox regressions). Prior employment, personality problems and negative symptoms are significantly related to acquisition of a competitive employment and to delay to acquisition whereas the conscientiousness personality trait was predictive of job tenure. Our results point out the relevance of personality traits and problems as predictors of work outcomes in people with SMI registered in SEP. Future studies should recruit larger samples and also investigate these links with other factors related to work outcomes.

Prediction accuracy of direct and indirect approaches, and their relationships with prediction ability of calibration models.

PubMed

Belay, T K; Dagnachew, B S; Boison, S A; Ådnøy, T

2018-03-28

Milk infrared spectra are routinely used for phenotyping traits of interest through links developed between the traits and spectra. Predicted individual traits are then used in genetic analyses for estimated breeding value (EBV) or for phenotypic predictions using a single-trait mixed model; this approach is referred to as indirect prediction (IP). An alternative approach [direct prediction (DP)] is a direct genetic analysis of (a reduced dimension of) the spectra using a multitrait model to predict multivariate EBV of the spectral components and, ultimately, also to predict the univariate EBV or phenotype for the traits of interest. We simulated 3 traits under different genetic (low: 0.10 to high: 0.90) and residual (zero to high: ±0.90) correlation scenarios between the 3 traits and assumed the first trait is a linear combination of the other 2 traits. The aim was to compare the IP and DP approaches for predictions of EBV and phenotypes under the different correlation scenarios. We also evaluated relationships between performances of the 2 approaches and the accuracy of calibration equations. Moreover, the effect of using different regression coefficients estimated from simulated phenotypes (β p ), true breeding values (β g ), and residuals (β r ) on performance of the 2 approaches were evaluated. The simulated data contained 2,100 parents (100 sires and 2,000 cows) and 8,000 offspring (4 offspring per cow). Of the 8,000 observations, 2,000 were randomly selected and used to develop links between the first and the other 2 traits using partial least square (PLS) regression analysis. The different PLS regression coefficients, such as β p , β g , and β r , were used in subsequent predictions following the IP and DP approaches. We used BLUP analyses for the remaining 6,000 observations using the true (co)variance components that had been used for the simulation. Accuracy of prediction (of EBV and phenotype) was calculated as a correlation between predicted and true values from the simulations. The results showed that accuracies of EBV prediction were higher in the DP than in the IP approach. The reverse was true for accuracy of phenotypic prediction when using β p but not when using β g and β r , where accuracy of phenotypic prediction in the DP was slightly higher than in the IP approach. Within the DP approach, accuracies of EBV when using β g were higher than when using β p only at the low genetic correlation scenario. However, we found no differences in EBV prediction accuracy between the β p and β g in the IP approach. Accuracy of the calibration models increased with an increase in genetic and residual correlations between the traits. Performance of both approaches increased with an increase in accuracy of the calibration models. In conclusion, the DP approach is a good strategy for EBV prediction but not for phenotypic prediction, where the classical PLS regression-based equations or the IP approach provided better results. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Do Work Characteristics Predict Health Deterioration Among Employees with Chronic Diseases?

PubMed

de Wind, Astrid; Boot, Cécile R L; Sewdas, Ranu; Scharn, Micky; van den Heuvel, Swenne G; van der Beek, Allard J

2018-06-01

Purpose In our ageing workforce, the increasing numbers of employees with chronic diseases are encouraged to prolong their working lives. It is important to prevent health deterioration in this vulnerable group. This study aims to investigate whether work characteristics predict health deterioration over a 3-year period among employees with (1) chronic diseases, and, more specifically, (2) musculoskeletal and psychological disorders. Methods The study population consisted of 5600 employees aged 45-64 years with a chronic disease, who participated in the Dutch Study on Transitions in Employment, Ability and Motivation (STREAM). Information on work characteristics was derived from the baseline questionnaire. Health deterioration was defined as a decrease in general health (SF-12) between baseline and follow-up (1-3 years). Crude and adjusted logistic regression analyses were performed to investigate prediction of health deterioration by work characteristics. Subgroup analyses were performed for employees with musculoskeletal and psychological disorders. Results At follow-up, 19.2% of the employees reported health deterioration (N = 1075). Higher social support of colleagues or supervisor predicted health deterioration in the crude analyses in the total group, and the groups with either musculoskeletal or psychological disorders (ORs 1.11-1.42). This effect was not found anymore in the adjusted analyses. The other work characteristics did not predict health deterioration in any group. Conclusions This study did not support our hypothesis that work characteristics predict health deterioration among employees with chronic diseases. As our study population succeeded continuing employment to 45 years and beyond, it was probably a relatively healthy selection of employees.

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

PubMed

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

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

PubMed

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

2015-06-01

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

Pediatric Irritable Bowel Syndrome Patient and Parental Characteristics Differ by Care Management Type.

PubMed

Hollier, John M; Czyzewski, Danita I; Self, Mariella M; Weidler, Erica M; Smith, E O'Brian; Shulman, Robert J

2017-03-01

This study evaluates whether certain patient or parental characteristics are associated with gastroenterology (GI) referral versus primary pediatrics care for pediatric irritable bowel syndrome (IBS). A retrospective clinical trial sample of patients meeting pediatric Rome III IBS criteria was assembled from a single metropolitan health care system. Baseline socioeconomic status (SES) and clinical symptom measures were gathered. Various instruments measured participant and parental psychosocial traits. Study outcomes were stratified by GI referral versus primary pediatrics care. Two separate analyses of SES measures and GI clinical symptoms and psychosocial measures identified key factors by univariate and multiple logistic regression analyses. For each analysis, identified factors were placed in unadjusted and adjusted multivariate logistic regression models to assess their impact in predicting GI referral. Of the 239 participants, 152 were referred to pediatric GI, and 87 were managed in primary pediatrics care. Of the SES and clinical symptom factors, child self-assessment of abdominal pain duration and lower percentage of people living in poverty were the strongest predictors of GI referral. Among the psychosocial measures, parental assessment of their child's functional disability was the sole predictor of GI referral. In multivariate logistic regression models, all selected factors continued to predict GI referral in each model. Socioeconomic environment, clinical symptoms, and functional disability are associated with GI referral. Future interventions designed to ameliorate the effect of these identified factors could reduce unnecessary specialty consultations and health care overutilization for IBS.

Predicted effect size of lisdexamfetamine treatment of attention deficit/hyperactivity disorder (ADHD) in European adults: Estimates based on indirect analysis using a systematic review and meta-regression analysis.

PubMed

Fridman, M; Hodgkins, P S; Kahle, J S; Erder, M H

2015-06-01

There are few approved therapies for adults with attention-deficit/hyperactivity disorder (ADHD) in Europe. Lisdexamfetamine (LDX) is an effective treatment for ADHD; however, no clinical trials examining the efficacy of LDX specifically in European adults have been conducted. Therefore, to estimate the efficacy of LDX in European adults we performed a meta-regression of existing clinical data. A systematic review identified US- and Europe-based randomized efficacy trials of LDX, atomoxetine (ATX), or osmotic-release oral system methylphenidate (OROS-MPH) in children/adolescents and adults. A meta-regression model was then fitted to the published/calculated effect sizes (Cohen's d) using medication, geographical location, and age group as predictors. The LDX effect size in European adults was extrapolated from the fitted model. Sensitivity analyses performed included using adult-only studies and adding studies with placebo designs other than a standard pill-placebo design. Twenty-two of 2832 identified articles met inclusion criteria. The model-estimated effect size of LDX for European adults was 1.070 (95% confidence interval: 0.738, 1.401), larger than the 0.8 threshold for large effect sizes. The overall model fit was adequate (80%) and stable in the sensitivity analyses. This model predicts that LDX may have a large treatment effect size in European adults with ADHD. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Regional Regression Equations to Estimate Flow-Duration Statistics at Ungaged Stream Sites in Connecticut

USGS Publications Warehouse

Ahearn, Elizabeth A.

2010-01-01

Multiple linear regression equations for determining flow-duration statistics were developed to estimate select flow exceedances ranging from 25- to 99-percent for six 'bioperiods'-Salmonid Spawning (November), Overwinter (December-February), Habitat Forming (March-April), Clupeid Spawning (May), Resident Spawning (June), and Rearing and Growth (July-October)-in Connecticut. Regression equations also were developed to estimate the 25- and 99-percent flow exceedances without reference to a bioperiod. In total, 32 equations were developed. The predictive equations were based on regression analyses relating flow statistics from streamgages to GIS-determined basin and climatic characteristics for the drainage areas of those streamgages. Thirty-nine streamgages (and an additional 6 short-term streamgages and 28 partial-record sites for the non-bioperiod 99-percent exceedance) in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Weighted least squares regression analysis was used to determine the predictive equations; weights were assigned based on record length. The basin characteristics-drainage area, percentage of area with coarse-grained stratified deposits, percentage of area with wetlands, mean monthly precipitation (November), mean seasonal precipitation (December, January, and February), and mean basin elevation-are used as explanatory variables in the equations. Standard errors of estimate of the 32 equations ranged from 10.7 to 156 percent with medians of 19.2 and 55.4 percent to predict the 25- and 99-percent exceedances, respectively. Regression equations to estimate high and median flows (25- to 75-percent exceedances) are better predictors (smaller variability of the residual values around the regression line) than the equations to estimate low flows (less than 75-percent exceedance). The Habitat Forming (March-April) bioperiod had the smallest standard errors of estimate, ranging from 10.7 to 20.9 percent. In contrast, the Rearing and Growth (July-October) bioperiod had the largest standard errors, ranging from 30.9 to 156 percent. The adjusted coefficient of determination of the equations ranged from 77.5 to 99.4 percent with medians of 98.5 and 90.6 percent to predict the 25- and 99-percent exceedances, respectively. Descriptive information on the streamgages used in the regression, measured basin and climatic characteristics, and estimated flow-duration statistics are provided in this report. Flow-duration statistics and the 32 regression equations for estimating flow-duration statistics in Connecticut are stored on the U.S. Geological Survey World Wide Web application ?StreamStats? (http://water.usgs.gov/osw/streamstats/index.html). The regression equations developed in this report can be used to produce unbiased estimates of select flow exceedances statewide.

Comparative values of medical school assessments in the prediction of internship performance.

PubMed

Lee, Ming; Vermillion, Michelle

2018-02-01

Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

Early post-stroke cognition in stroke rehabilitation patients predicts functional outcome at 13 months.

PubMed

Wagle, Jørgen; Farner, Lasse; Flekkøy, Kjell; Bruun Wyller, Torgeir; Sandvik, Leiv; Fure, Brynjar; Stensrød, Brynhild; Engedal, Knut

2011-01-01

To identify prognostic factors associated with functional outcome at 13 months in a sample of stroke rehabilitation patients. Specifically, we hypothesized that cognitive functioning early after stroke would predict long-term functional outcome independently of other factors. 163 stroke rehabilitation patients underwent a structured neuropsychological examination 2-3 weeks after hospital admittance, and their functional status was subsequently evaluated 13 months later with the modified Rankin Scale (mRS) as outcome measure. Three predictive models were built using linear regression analyses: a biological model (sociodemographics, apolipoprotein E genotype, prestroke vascular factors, lesion characteristics and neurological stroke-related impairment); a functional model (pre- and early post-stroke cognitive functioning, personal and instrumental activities of daily living, ADL, and depressive symptoms), and a combined model (including significant variables, with p value <0.05, from the biological and functional models). A combined model of 4 variables best predicted long-term functional outcome with explained variance of 49%: neurological impairment (National Institute of Health Stroke Scale; β = 0.402, p < 0.001), age (β = 0.233, p = 0.001), post-stroke cognitive functioning (Repeatable Battery of Neuropsychological Status, RBANS; β = -0.248, p = 0.001) and prestroke personal ADL (Barthel Index; β = -0.217, p = 0.002). Further linear regression analyses of which RBANS indexes and subtests best predicted long-term functional outcome showed that Coding (β = -0.484, p < 0.001) and Figure Copy (β = -0.233, p = 0.002) raw scores at baseline explained 42% of the variance in mRS scores at follow-up. Early post-stroke cognitive functioning as measured by the RBANS is a significant and independent predictor of long-term functional post-stroke outcome. Copyright © 2011 S. Karger AG, Basel.

Time Series Analysis and Forecasting of Wastewater Inflow into Bandar Tun Razak Sewage Treatment Plant in Selangor, Malaysia

NASA Astrophysics Data System (ADS)

Abunama, Taher; Othman, Faridah

2017-06-01

Analysing the fluctuations of wastewater inflow rates in sewage treatment plants (STPs) is essential to guarantee a sufficient treatment of wastewater before discharging it to the environment. The main objectives of this study are to statistically analyze and forecast the wastewater inflow rates into the Bandar Tun Razak STP in Kuala Lumpur, Malaysia. A time series analysis of three years’ weekly influent data (156weeks) has been conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model. Various combinations of ARIMA orders (p, d, q) have been tried to select the most fitted model, which was utilized to forecast the wastewater inflow rates. The linear regression analysis was applied to testify the correlation between the observed and predicted influents. ARIMA (3, 1, 3) model was selected with the highest significance R-square and lowest normalized Bayesian Information Criterion (BIC) value, and accordingly the wastewater inflow rates were forecasted to additional 52weeks. The linear regression analysis between the observed and predicted values of the wastewater inflow rates showed a positive linear correlation with a coefficient of 0.831.

The nonlinear relations of the approximate number system and mathematical language to early mathematics development.

PubMed

Purpura, David J; Logan, Jessica A R

2015-12-01

Both mathematical language and the approximate number system (ANS) have been identified as strong predictors of early mathematics performance. Yet, these relations may be different depending on a child's developmental level. The purpose of this study was to evaluate the relations between these domains across different levels of ability. Participants included 114 children who were assessed in the fall and spring of preschool on a battery of academic and cognitive tasks. Children were 3.12 to 5.26 years old (M = 4.18, SD = .58) and 53.6% were girls. Both mixed-effect and quantile regressions were conducted. The mixed-effect regressions indicated that mathematical language, but not the ANS, nor other cognitive domains, predicted mathematics performance. However, the quantile regression analyses revealed a more nuanced relation among domains. Specifically, it was found that mathematical language and the ANS predicted mathematical performance at different points on the ability continuum. These dual nonlinear relations indicate that different mechanisms may enhance mathematical acquisition dependent on children's developmental abilities. (c) 2015 APA, all rights reserved).

Do peritraumatic emotions differentially predict PTSD symptom clusters? Initial evidence for emotion specificity.

PubMed

Dewey, Daniel; Schuldberg, David; Madathil, Renee

2014-08-01

This study investigated whether specific peritraumatic emotions differentially predict PTSD symptom clusters in individuals who have experienced stressful life events. Hypotheses were developed based on the SPAARS model of PTSD. It was predicted that the peritraumatic emotions of anger, disgust, guilt, and fear would significantly predict re-experiencing and avoidance symptoms, while only fear would predict hyperarousal. Undergraduate students (N = 144) participated in this study by completing a packet of self-report questionnaires. Multiple regression analyses were conducted with PCL-S symptom cluster scores as dependent variables and peritraumatic fear, guilt, anger, shame, and disgust as predictor variables. As hypothesized, peritraumatic anger, guilt, and fear all significantly predicted re-experiencing. However, only fear predicted avoidance, and anger significantly predicted hyperarousal. Results are discussed in relation to the theoretical role of emotions in the etiology of PTSD following the experience of a stressful life event.

Family and school environmental predictors of sleep bruxism in children.

PubMed

Rossi, Debora; Manfredini, Daniele

2013-01-01

To identify potential predictors of self-reported sleep bruxism (SB) within children's family and school environments. A total of 65 primary school children (55.4% males, mean age 9.3 ± 1.9 years) were administered a 10-item questionnaire investigating the prevalence of self-reported SB as well as nine family and school-related potential bruxism predictors. Regression analyses were performed to assess the correlation between the potential predictors and SB. A positive answer to the self-reported SB item was endorsed by 18.8% of subjects, with no sex differences. Multiple variable regression analysis identified a final model showing that having divorced parents and not falling asleep easily were the only two weak predictors of self-reported SB. The percentage of explained variance for SB by the final multiple regression model was 13.3% (Nagelkerke's R² = 0.133). While having a high specificity and a good negative predictive value, the model showed unacceptable sensitivity and positive predictive values. The resulting accuracy to predict the presence of self-reported SB was 73.8%. The present investigation suggested that, among family and school-related matters, having divorced parents and not falling asleep easily were two predictors, even if weak, of a child's self-report of SB.

The Hispanic Americans Baseline Alcohol Survey (HABLAS):Predictive invariance of Demographic Characteristics on Attitudes towards Alcohol across Hispanic National Groups#

PubMed Central

Mills, Britain A.; Caetano, Raul; Bernstein, Ira H.

2011-01-01

This study compares the demographic predictors of items assessing attitudes towards drinking across Hispanic national groups. Data were from the 2006 Hispanic Americans Baseline Alcohol Survey (HABLAS), which used a multistage cluster sample design to interview 5,224 individuals randomly selected from the household population in Miami, New York, Philadelphia, Houston, and Los Angeles. Predictive invariance of demographic predictors of alcohol attitudes over four Hispanic national groups (Puerto Rican, Cuban, Mexican, and South/Central Americans) was examined using multiple-group seemingly unrelated probit regression. The analyses examined whether the influence of various demographic predictors varied across the Hispanic national groups in their regression coefficients, item intercepts, and error correlations. The hypothesis of predictive invariance was supported. Hispanic groups did not differ in how demographic predictors related to individual attitudinal items (regression slopes were invariant). In addition, the groups did not differ in attitudinal endorsement rates once demographic covariates were taken into account (item intercepts were invariant). Although Hispanic groups have different attitudes about alcohol, the influence of multiple demographic characteristics on alcohol attitudes operates similarly across Hispanic groups. Future models of drinking behavior in adult Hispanics need not posit moderating effects of group on the relation between these background characteristics and attitudes. PMID:25379120

Radon-222 concentrations in ground water and soil gas on Indian reservations in Wisconsin

USGS Publications Warehouse

DeWild, John F.; Krohelski, James T.

1995-01-01

For sites with wells finished in the sand and gravel aquifer, the coefficient of determination (R2) of the regression of concentration of radon-222 in ground water as a function of well depth is 0.003 and the significance level is 0.32, which indicates that there is not a statistically significant relation between radon-222 concentrations in ground water and well depth. The coefficient of determination of the regression of radon-222 in ground water and soil gas is 0.19 and the root mean square error of the regression line is 271 picocuries per liter. Even though the significance level (0.036) indicates a statistical relation, the root mean square error of the regression is so large that the regression equation would not give reliable predictions. Because of an inadequate number of samples, similar statistical analyses could not be performed for sites with wells finished in the crystalline and sedimentary bedrock aquifers.

Linear models for calculating digestibile energy for sheep diets.

PubMed

Fonnesbeck, P V; Christiansen, M L; Harris, L E

1981-05-01

Equations for estimating the digestible energy (DE) content of sheep diets were generated from the chemical contents and a factorial description of diets fed to lambs in digestion trials. The diet factors were two forages (alfalfa and grass hay), harvested at three stages of maturity (late vegetative, early bloom and full bloom), fed in two ingredient combinations (all hay or a 50:50 hay and corn grain mixture) and prepared by two forage texture processes (coarsely chopped or finely chopped and pelleted). The 2 x 3 x 2 x 2 factorial arrangement produced 24 diet treatments. These were replicated twice, for a total of 48 lamb digestion trials. In model 1 regression equations, DE was calculated directly from chemical composition of the diet. In model 2, regression equations predicted the percentage of digested nutrient from the chemical contents of the diet and then DE of the diet was calculated as the sum of the gross energy of the digested organic components. Expanded forms of model 1 and model 2 were also developed that included diet factors as qualitative indicator variables to adjust the regression constant and regression coefficients for the diet description. The expanded forms of the equations accounted for significantly more variation in DE than did the simple models and more accurately estimated DE of the diet. Information provided by the diet description proved as useful as chemical analyses for the prediction of digestibility of nutrients. The statistics indicate that, with model 1, neutral detergent fiber and plant cell wall analyses provided as much information for the estimation of DE as did model 2 with the combined information from crude protein, available carbohydrate, total lipid, cellulose and hemicellulose. Regression equations are presented for estimating DE with the most currently analyzed organic components, including linear and curvilinear variables and diet factors that significantly reduce the standard error of the estimate. To estimate De of a diet, the user utilizes the equation that uses the chemical analysis information and diet description most effectively.

Resilience linked to personality dimensions, alexithymia and affective symptoms in motor functional neurological disorders.

PubMed

Jalilianhasanpour, Rozita; Williams, Benjamin; Gilman, Isabelle; Burke, Matthew J; Glass, Sean; Fricchione, Gregory L; Keshavan, Matcheri S; LaFrance, W Curt; Perez, David L

2018-04-01

Reduced resilience, a construct associated with maladaptive stress coping and a predisposing vulnerability for Functional Neurological Disorders (FND), has been under-studied compared to other neuropsychiatric factors in FND. This prospective case-control study investigated self-reported resilience in patients with FND compared to controls and examined relationships between resilience and affective symptoms, personality traits, alexithymia, health status and adverse life event burden. 50 individuals with motor FND and 47 healthy controls participated. A univariate test followed by a logistic regression analysis investigated group-level differences in Connor-Davidson Resilience Scale (CD-RISC) scores. For within-group analyses performed separately in patients with FND and controls, univariate screening tests followed by multivariate linear regression analyses examined factors associated with self-reported resilience. Adjusting for age, gender, education status, ethnicity and lifetime adverse event burden, patients with FND reported reduced resilience compared to controls. Within-group analyses in patients with FND showed that individual-differences in mental health, extraversion, conscientiousness, and openness positively correlated with CD-RISC scores; post-traumatic stress disorder symptom severity, depression, anxiety, alexithymia and neuroticism scores negatively correlated with CD-RISC scores. Extraversion independently predicted resilience scores in patients with FND. In control subjects, univariate associations were appreciated between CD-RISC scores and gender, personality traits, anxiety, alexithymia and physical health; conscientiousness independently predicted resilience in controls. Patients with FND reported reduced resilience, and CD-RISC scores covaried with other important predisposing vulnerabilities for the development of FND. Future research should investigate if the CD-RISC is predictive of clinical outcomes in patients with FND. Copyright © 2018 Elsevier Inc. All rights reserved.

Ultrasound predictors of placental invasion: the Placenta Accreta Index.

PubMed

Rac, Martha W F; Dashe, Jodi S; Wells, C Edward; Moschos, Elysia; McIntire, Donald D; Twickler, Diane M

2015-03-01

We sought to apply a standardized evaluation of ultrasound parameters for the prediction of placental invasion in a high-risk population. This was a retrospective review of gravidas with ≥1 prior cesarean delivery who received an ultrasound diagnosis of placenta previa or low-lying placenta in the third trimester at our institution from 1997 through 2011. Sonographic images were reviewed by an investigator blinded to pregnancy outcome and sonography reports. Parameters assessed included loss of retroplacental clear zone, irregularity and width of uterine-bladder interface, smallest myometrial thickness, presence of lacunar spaces, and bridging vessels. Diagnosis of placental invasion was based on histologic confirmation. Statistical analyses were performed using linear logistic regression and multiparametric analyses to generate a predictive equation evaluated using a receiver operating characteristic curve. Of 184 gravidas who met inclusion criteria, 54 (29%) had invasion confirmed on hysterectomy specimen. All sonographic parameters were associated with placental invasion (P < .001). Constructing a receiver operating characteristic curve, the combination of smallest sagittal myometrial thickness, lacunae, and bridging vessels, in addition to number of cesarean deliveries and placental location, yielded an area under the curve of 0.87 (95% confidence interval, 0.80-0.95). Using logistic regression, a predictive equation was generated, termed the "Placenta Accreta Index." Each parameter was weighted to create a 9-point scale in which a score of 0-9 provided a probability of invasion that ranged from 2-96%, respectively. Assignment of the Placenta Accreta Index may be helpful in predicting individual patient risk for morbidly adherent placenta. Copyright © 2015 Elsevier Inc. All rights reserved.

Predictors of hopelessness among clinically depressed youth.

PubMed

Becker-Weidman, Emily G; Reinecke, Mark A; Jacobs, Rachel H; Martinovich, Zoran; Silva, Susan G; March, John S

2009-05-01

Factors that distinguish depressed individuals who become hopeless from those who do not are poorly understood. In this study, predictors of hopelessness were examined in a sample of 439 clinically depressed adolescents participating in the Treatment for Adolescents with Depression Study (TADS). The total score of the Beck Hopelessness Scale (BHS) was used to assess hopelessness at baseline. Multiple regression and logistic regression analyses were conducted to evaluate the extent to which variables were associated with hopelessness and determine which cluster of measures best predicted clinically significantly hopelessness. Hopelessness was associated with greater depression severity, poor social problem-solving, cognitive distortions, and family conflict. View of self, view of the world, internal attributional style, need for social approval, positive problem-solving orientation, and family problems consistently emerged as the best predictors of hopelessness in depressed youth. Cognitive and familial factors predict those depressed youth who have high levels of hopelessness.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

Individual and community risk factors and sexually transmitted diseases among arrested youths: a two level analysis.

PubMed

Dembo, Richard; Belenko, Steven; Childs, Kristina; Wareham, Jennifer; Schmeidler, James

2009-08-01

High rates of infection for chlamydia and gonorrhea have been noted among youths involved in the juvenile justice system. Although both individual and community-level factors have been found to be associated with sexually transmitted disease (STD) risk, their relative importance has not been tested in this population. A two-level logistic regression analysis was completed to assess the influence of individual-level and community-level predictors on STD test results among arrested youths processed at a centralized intake facility. Results from weighted two level logistic regression analyses (n = 1,368) indicated individual-level factors of gender (being female), age, race (being African American), and criminal history predicted the youths' positive STD status. For the community-level predictors, concentrated disadvantage significantly and positively predicted the youths' STD status. Implications of these findings for future research and public health policy are discussed.

Can health indicators and psychosocial characteristics predict attrition in youths with overweight and obesity seeking ambulatory treatment? Data from a retrospective longitudinal study in a paediatric clinic in Luxembourg.

PubMed

Pit-Ten Cate, Ineke M; Samouda, Hanen; Schierloh, Ulrike; Jacobs, Julien; Vervier, Jean Francois; Stranges, Saverio; Lair, Marie Lise; Beaufort, Carine de

2017-09-03

The current study aimed to identify factors that could predict attrition in youths starting ambulatory treatment to control or lose weight. Retrospective longitudinal study. Paediatric clinic: ambulatory treatment programme. A youth sample (n=191; 89 boys; aged 7-17 years) completed measures of demographic characteristics, and health and psychosocial traits before starting an ambulatory weight management programme. Anthropometric and biological markers related to obesity were also obtained. Tests of mean differences and regression analyses were used to investigate the relationship between these variables and attrition after 1 year. The χ 2 and t test results showed both psychosocial and health indicators differentiated between participants who continued attending the treatment programme and those who dropped out. More specifically, youths that dropped out of treatment were significantly older, had higher body mass index z scores, higher levels of insulin, triglycerides and HOMA-IR, reported poorer health, had more conduct problems and were more dissatisfied with themselves and their bodies before starting treatment. Results of regression analyses revealed that weight status (anthropometric and biological markers), age and body dissatisfaction predicted attrition (overall prediction success 73%; prediction success for continued attendance 90/91%; prediction success for dropouts 42/44%). Attrition, but especially the continued attendance in treatment, can be successfully predicted by age, weight status and body dissatisfaction. For patients who present with one or more risk factors, careful consideration is needed to decide which (combination of) inpatient or outpatient programme may facilitate prolonged engagement of the patient and hence may be most effective in establishing weight loss. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Mind-Mindedness as a Multidimensional Construct: Appropriate and Nonattuned Mind-Related Comments Independently Predict Infant-Mother Attachment in a Socially Diverse Sample

ERIC Educational Resources Information Center

Meins, Elizabeth; Fernyhough, Charles; de Rosnay, Marc; Arnott, Bronia; Leekam, Susan R.; Turner, Michelle

2012-01-01

In a socially diverse sample of 206 infant-mother pairs, we investigated predictors of infants' attachment security at 15 months, with a particular emphasis on mothers' tendency to comment appropriately or in a non-attuned manner on their 8-month-olds' internal states (so-called mind-mindedness). Multinomial logistic regression analyses showed…

Early presence of anti-angiogenesis-related adverse events as a potential biomarker of antitumor efficacy in metastatic gastric cancer patients treated with apatinib: a cohort study.

PubMed

Liu, Xinyang; Qin, Shukui; Wang, Zhichao; Xu, Jianming; Xiong, Jianping; Bai, Yuxian; Wang, Zhehai; Yang, Yan; Sun, Guoping; Wang, Liwei; Zheng, Leizhen; Xu, Nong; Cheng, Ying; Guo, Weijian; Yu, Hao; Liu, Tianshu; Lagiou, Pagona; Li, Jin

2017-09-05

Reliable biomarkers of apatinib response in gastric cancer (GC) are lacking. We investigated the association between early presence of common adverse events (AEs) and clinical outcomes in metastatic GC patients. We conducted a retrospective cohort study using data on 269 apatinib-treated GC patients in two clinical trials. AEs were assessed at baseline until 28 days after the last dose of apatinib. Clinical outcomes were compared between patients with and without hypertension (HTN), proteinuria, or hand and foot syndrome (HFS) in the first 4 weeks. Time-to-event variables were assessed using Kaplan-Meier methods and Cox proportional hazard regression models. Binary endpoints were assessed using logistic regression models. Landmark analyses were performed as sensitivity analyses. Predictive model was analyzed, and risk scores were calculated to predict overall survival. Presence of AEs in the first 4 weeks was associated with prolonged median overall survival (169 vs. 103 days, log-rank p = 0.0039; adjusted hazard ratio (HR) 0.64, 95% confidence interval [CI] 0.64-0.84, p = 0.001), prolonged median progression-free survival (86.5 vs. 62 days, log-rank p = 0.0309; adjusted HR 0.69, 95% CI 0.53-0.91, p = 0.007), and increased disease control rate (54.67 vs. 32.77%; adjusted odds ratio 2.67, p < 0.001). Results remained significant in landmark analyses. The onset of any single AE or any combinations of the AEs were all statistically significantly associated with prolonged OS, except for the presence of proteinuria. An AE-based prediction model and subsequently derived scoring system showed high calibration and discrimination in predicting overall survival. Presence of HTN, proteinuria, or HFS during the first cycle of apatinib treatment was a viable biomarker of antitumor efficacy in metastatic GC patients.

Summer microhabitat use of fluvial bull trout in Eastern Oregon streams

USGS Publications Warehouse

Al-Chokhachy, R.; Budy, P.

2007-01-01

The management and recovery of populations of bull trout Salvelinus confluentus requires a comprehensive understanding of habitat use across different systems, life stages, and life history forms. To address these needs, we collected microhabitat use and availability data in three fluvial populations of bull trout in eastern Oregon. We evaluated diel differences in microhabitat use, the consistency of microhabitat use across systems and size-classes based on preference, and our ability to predict bull trout microhabitat use. Diel comparisons suggested bull trout continue to use deeper microhabitats with cover but shift into significantly slower habitats during nighttime periods; however, we observed no discrete differences in substrate use patterns across diel periods. Across life stages, we found that both juvenile and adult bull trout used slow-velocity microhabitats with cover, but the use of specific types varied. Both logistic regression and habitat preference analyses suggested that adult bull trout used deeper habitats than juveniles. Habitat preference analyses suggested that bull trout habitat use was consistent across all three systems, as chi-square tests rejected the null hypotheses that microhabitats were used in proportion to those available (P < 0.0001). Validation analyses indicated that the logistic regression models (juvenile and adult) were effective at predicting bull trout absence across all tests (specificity values = 100%); however, our ability to accurately predict bull trout absence was limited (sensitivity values = 0% across all tests). Our results highlight the limitations of the models used to predict microhabitat use for fish species like bull trout, which occur at naturally low densities. However, our results also demonstrate that bull trout microhabitat use patterns are generally consistent across systems, a pattern that parallels observations at both similar and larger scales and across life history forms. Thus, our results, in combination with previous bull trout habitat studies, provide managers with benchmarks for restoration in highly degraded systems.

Analyses of non-fatal accidents in an opencast mine by logistic regression model - a case study.

PubMed

Onder, Seyhan; Mutlu, Mert

2017-09-01

Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Gender differences in body consciousness and substance use among high-risk adolescents.

PubMed

Black, David Scott; Sussman, Steve; Unger, Jennifer; Pokhrel, Pallav; Sun, Ping

2010-08-01

This study explores the association between private and public body consciousness and past 30-day cigarette, alcohol, marijuana, and hard drug use among adolescents. Self-reported data from alterative high school students in California were analyzed (N = 976) using multilevel regression models to account for student clustering within schools. Separate regression analyses were conducted for males and females. Both cross-sectional baseline data and one-year longitudinal prediction models indicated that body consciousness is associated with specific drug use categories differentially by gender. Findings suggest that body consciousness accounts for additional variance in substance use etiology not explained by previously recognized dispositional variables.

Consumer factors predicting level of treatment response to illness management and recovery.

PubMed

White, Dominique A; McGuire, Alan B; Luther, Lauren; Anderson, Adrienne I; Phalen, Peter; McGrew, John H

2017-12-01

This study aims to identify consumer-level predictors of level of treatment response to illness management and recovery (IMR) to target the appropriate consumers and aid psychiatric rehabilitation settings in developing intervention adaptations. Secondary analyses from a multisite study of IMR were conducted. Self-report data from consumer participants of the parent study (n = 236) were analyzed for the current study. Consumers completed prepost surveys assessing illness management, coping, goal-related hope, social support, medication adherence, and working alliance. Correlations and multiple regression analyses were run to identify self-report variables that predicted level of treatment response to IMR. Analyses revealed that goal-related hope significantly predicted level of improved illness self-management, F(1, 164) = 10.93, p < .001, R2 = .248, R2 change = .05. Additionally, we found that higher levels of maladaptive coping at baseline were predictive of higher levels of adaptive coping at follow-up, F(2, 180) = 5.29, p < .02, R2 = .38, R2 change = .02. Evidence did not support additional predictors. Previously, consumer-level predictors of level of treatment response have not been explored for IMR. Although 2 significant predictors were identified, study findings suggest more work is needed. Future research is needed to identify additional consumer-level factors predictive of IMR treatment response in order to identify who would benefit most from this treatment program. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

DOE PAGES

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; ...

2016-04-07

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

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

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Developing a dengue forecast model using machine learning: A case study in China

PubMed Central

Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-01-01

Background In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Methodology/Principal findings Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. Conclusion and significance The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics. PMID:29036169

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.

Self-regulated learning and achievement by middle-school children.

PubMed

Sink, C A; Barnett, J E; Hixon, J E

1991-12-01

The relationship of self-regulated learning to the achievement test scores of 62 Grade 6 students was studied. Generally, the metacognitive and affective variables correlated significantly with teachers' grades and standardized test scores in mathematics, reading, and science. Planning and self-assessment significantly predicted the six measures of achievement. Step-wise multiple regression analyses using the metacognitive and affective variables largely indicate that students' and teachers' perceptions of scholastic ability and planning appear to be the most salient factors in predicting academic performance. The locus of control dimension had no utility in predicting classroom grades and performance on standardized measures of achievement. The implications of the findings for teaching and learning are discussed.

Relations among early adolescents' parent-adolescent attachment, perceived social competence, and friendship quality.

PubMed

Boling, Melissa W; Barry, Carolyn McNamara; Kotchick, Beth A; Lowry, Jen

2011-12-01

To assess whether the relation between attachment and friendship quality may be explained by social competence, 113 students in Grades 7 and 8 from the Baltimore metropolitan area completed self-report questionnaires on the variables of interest. In hierarchical regression analyses, both maternal Affective Quality of Attachment and the interaction of School with paternal Affective Quality of Attachment predicted social competence. Also, the interaction of School with paternal Affective Quality of Attachment predicted negative friendship features, whereas social competence predicted positive friendship features. These findings provide support for a pathway between adolescents' attachment to both parents and adolescents' perceived social competence and, in turn, their friendship quality.

Methods for estimating flood frequency in Montana based on data through water year 1998

USGS Publications Warehouse

Parrett, Charles; Johnson, Dave R.

2004-01-01

Annual peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (T-year floods) were determined for 660 gaged sites in Montana and in adjacent areas of Idaho, Wyoming, and Canada, based on data through water year 1998. The updated flood-frequency information was subsequently used in regression analyses, either ordinary or generalized least squares, to develop equations relating T-year floods to various basin and climatic characteristics, equations relating T-year floods to active-channel width, and equations relating T-year floods to bankfull width. The equations can be used to estimate flood frequency at ungaged sites. Montana was divided into eight regions, within which flood characteristics were considered to be reasonably homogeneous, and the three sets of regression equations were developed for each region. A measure of the overall reliability of the regression equations is the average standard error of prediction. The average standard errors of prediction for the equations based on basin and climatic characteristics ranged from 37.4 percent to 134.1 percent. Average standard errors of prediction for the equations based on active-channel width ranged from 57.2 percent to 141.3 percent. Average standard errors of prediction for the equations based on bankfull width ranged from 63.1 percent to 155.5 percent. In most regions, the equations based on basin and climatic characteristics generally had smaller average standard errors of prediction than equations based on active-channel or bankfull width. An exception was the Southeast Plains Region, where all equations based on active-channel width had smaller average standard errors of prediction than equations based on basin and climatic characteristics or bankfull width. Methods for weighting estimates derived from the basin- and climatic-characteristic equations and the channel-width equations also were developed. The weights were based on the cross correlation of residuals from the different methods and the average standard errors of prediction. When all three methods were combined, the average standard errors of prediction ranged from 37.4 percent to 120.2 percent. Weighting of estimates reduced the standard errors of prediction for all T-year flood estimates in four regions, reduced the standard errors of prediction for some T-year flood estimates in two regions, and provided no reduction in average standard error of prediction in two regions. A computer program for solving the regression equations, weighting estimates, and determining reliability of individual estimates was developed and placed on the USGS Montana District World Wide Web page. A new regression method, termed Region of Influence regression, also was tested. Test results indicated that the Region of Influence method was not as reliable as the regional equations based on generalized least squares regression. Two additional methods for estimating flood frequency at ungaged sites located on the same streams as gaged sites also are described. The first method, based on a drainage-area-ratio adjustment, is intended for use on streams where the ungaged site of interest is located near a gaged site. The second method, based on interpolation between gaged sites, is intended for use on streams that have two or more streamflow-gaging stations.

Computational toxicology: Its essential role in reducing drug attrition.

PubMed

Naven, R T; Louise-May, S

2015-12-01

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them. © The Author(s) 2015.

School-Based Racial and Gender Discrimination among African American Adolescents: Exploring Gender Variation in Frequency and Implications for Adjustment

PubMed Central

Chavous, Tabbye M.; Griffin, Tiffany M.

2012-01-01

The present study examined school-based racial and gender discrimination experiences among African American adolescents in Grade 8 (n = 204 girls; n = 209 boys). A primary goal was exploring gender variation in frequency of both types of discrimination and associations of discrimination with academic and psychological functioning among girls and boys. Girls and boys did not vary in reported racial discrimination frequency, but boys reported more gender discrimination experiences. Multiple regression analyses within gender groups indicated that among girls and boys, racial discrimination and gender discrimination predicted higher depressive symptoms and school importance and racial discrimination predicted self-esteem. Racial and gender discrimination were also negatively associated with grade point average among boys but were not significantly associated in girls’ analyses. Significant gender discrimination X racial discrimination interactions resulted in the girls’ models predicting psychological outcomes and in boys’ models predicting academic achievement. Taken together, findings suggest the importance of considering gender- and race-related experiences in understanding academic and psychological adjustment among African American adolescents. PMID:22837794

School-Based Racial and Gender Discrimination among African American Adolescents: Exploring Gender Variation in Frequency and Implications for Adjustment.

PubMed

Cogburn, Courtney D; Chavous, Tabbye M; Griffin, Tiffany M

2011-01-03

The present study examined school-based racial and gender discrimination experiences among African American adolescents in Grade 8 (n = 204 girls; n = 209 boys). A primary goal was exploring gender variation in frequency of both types of discrimination and associations of discrimination with academic and psychological functioning among girls and boys. Girls and boys did not vary in reported racial discrimination frequency, but boys reported more gender discrimination experiences. Multiple regression analyses within gender groups indicated that among girls and boys, racial discrimination and gender discrimination predicted higher depressive symptoms and school importance and racial discrimination predicted self-esteem. Racial and gender discrimination were also negatively associated with grade point average among boys but were not significantly associated in girls' analyses. Significant gender discrimination X racial discrimination interactions resulted in the girls' models predicting psychological outcomes and in boys' models predicting academic achievement. Taken together, findings suggest the importance of considering gender- and race-related experiences in understanding academic and psychological adjustment among African American adolescents.

Systems analysis of stress and positive perceptions in mothers and fathers of pre-school children with autism.

PubMed

Hastings, Richard P; Kovshoff, Hanna; Ward, Nicholas J; degli Espinosa, Francesca; Brown, Tony; Remington, Bob

2005-10-01

Systemic analyses of psychological functioning in families of children with autism have typically shown that parents report different experiences (e.g., stress) and that siblings may also be affected. The purpose of the present research was more explicitly to address relationships between child, partner, and parent variables. Parents of 48 children with autism (41 mother-father pairs) reported on child characteristics, and their own stress and mental health. Mothers were found to report both more depression and more positive perceptions than fathers. Regression analyses revealed that paternal stress and positive perceptions were predicted by maternal depression; maternal stress was predicted by their children's behavior problems (not adaptive behavior or autism symptoms) and by their partner's depression. The future testing of the mechanisms underlying these results is discussed. In addition, the need is emphasized for more systemic analyses to understand the psychological functioning of children with autism and their siblings and parents.

Sources of variability in language development of children with cochlear implants: age at implantation, parental language, and early features of children's language construction.

PubMed

Szagun, Gisela; Schramm, Satyam A

2016-05-01

The aim of the present study was to analyze the relative influence of age at implantation, parental expansions, and child language internal factors on grammatical progress in children with cochlear implants (CI). Data analyses used two longitudinal corpora of spontaneous speech samples, one with twenty-two and one with twenty-six children, implanted between 0;6 and 3;10. Analyses were performed on the combined and separate samples. Regression analyses indicate that early child MLU is the strongest predictor of child MLU two and two-and-a-half years later, followed by parental expansions and age at implantation. Associations between earliest MLU gains and MLU two years later point to stability of individual differences. Early type and token frequencies of determiners predict MLU two years later more strongly than early frequency of lexical words. We conclude that features of CI children's very early language have considerable predictive value for later language outcomes.

Multivariate prediction of upper limb prosthesis acceptance or rejection.

PubMed

Biddiss, Elaine A; Chau, Tom T

2008-07-01

To develop a model for prediction of upper limb prosthesis use or rejection. A questionnaire exploring factors in prosthesis acceptance was distributed internationally to individuals with upper limb absence through community-based support groups and rehabilitation hospitals. A total of 191 participants (59 prosthesis rejecters and 132 prosthesis wearers) were included in this study. A logistic regression model, a C5.0 decision tree, and a radial basis function neural network were developed and compared in terms of sensitivity (prediction of prosthesis rejecters), specificity (prediction of prosthesis wearers), and overall cross-validation accuracy. The logistic regression and neural network provided comparable overall accuracies of approximately 84 +/- 3%, specificity of 93%, and sensitivity of 61%. Fitting time-frame emerged as the predominant predictor. Individuals fitted within two years of birth (congenital) or six months of amputation (acquired) were 16 times more likely to continue prosthesis use. To increase rates of prosthesis acceptance, clinical directives should focus on timely, client-centred fitting strategies and the development of improved prostheses and healthcare for individuals with high-level or bilateral limb absence. Multivariate analyses are useful in determining the relative importance of the many factors involved in prosthesis acceptance and rejection.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

The Predictive Effects of Protection Motivation Theory on Intention and Behaviour of Physical Activity in Patients with Type 2 Diabetes.

PubMed

Ali Morowatisharifabad, Mohammad; Abdolkarimi, Mahdi; Asadpour, Mohammad; Fathollahi, Mahmood Sheikh; Balaee, Parisa

2018-04-15

Theory-based education tailored to target behaviour and group can be effective in promoting physical activity. The purpose of this study was to examine the predictive power of Protection Motivation Theory on intent and behaviour of Physical Activity in Patients with Type 2 Diabetes. This descriptive study was conducted on 250 patients in Rafsanjan, Iran. To examine the scores of protection motivation theory structures, a researcher-made questionnaire was used. Its validity and reliability were confirmed. The level of physical activity was also measured by the International Short - form Physical Activity Inventory. Its validity and reliability were also approved. Data were analysed by statistical tests including correlation coefficient, chi-square, logistic regression and linear regression. The results revealed that there was a significant correlation between all the protection motivation theory constructs and the intention to do physical activity. The results showed that the Theory structures were able to predict 60% of the variance of physical activity intention. The results of logistic regression demonstrated that increase in the score of physical activity intent and self - efficacy increased the chance of higher level of physical activity by 3.4 and 1.5 times, respectively OR = (3.39, 1.54). Considering the ability of protection motivation theory structures to explain the physical activity behaviour, interventional designs are suggested based on the structures of this theory, especially to improve self -efficacy as the most powerful factor in predicting physical activity intention and behaviour.

Anxiety sensitivity, catastrophic misinterpretations and panic self-efficacy in the prediction of panic disorder severity: towards a tripartite cognitive model of panic disorder.

PubMed

Sandin, Bonifacio; Sánchez-Arribas, Carmen; Chorot, Paloma; Valiente, Rosa M

2015-04-01

The present study examined the contribution of three main cognitive factors (i.e., anxiety sensitivity, catastrophic misinterpretations of bodily symptoms, and panic self-efficacy) in predicting panic disorder (PD) severity in a sample of patients with a principal diagnosis of panic disorder. It was hypothesized that anxiety sensitivity (AS), catastrophic misinterpretation of bodily sensations, and panic self-efficacy are uniquely related to panic disorder severity. One hundred and sixty-eight participants completed measures of AS, catastrophic misinterpretations of panic-like sensations, and panic self-efficacy prior to receiving treatment. Results of multiple linear regression analyses indicated that AS, catastrophic misinterpretations and panic self-efficacy independently predicted panic disorder severity. Results of path analyses indicated that AS was direct and indirectly (mediated by catastrophic misinterpretations) related with panic severity. Results provide evidence for a tripartite cognitive account of panic disorder. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting the mental health of college students with psychological capital.

PubMed

Selvaraj, Priscilla Rose; Bhat, Christine Suniti

2018-06-01

Behavioral health treatment is grounded in the medical model with language of deficits and problems, rather than resources and strengths. With developments in the field of positive psychology, re-focusing on well-being rather than illness is possible. The primary purpose of this study was to examine relationships and predictions that exist between levels of mental health in college students, i.e., flourishing, moderate mental health, and languishing, and psychological capital (PsyCap). For this cross-sectional, exploratory study survey method was used for data collection and for analyses of results a series of descriptive, correlation, ANOVA, and multiple regression analyses were done. Results indicated that developing positive psychological strengths such as hope, efficacy, resilience, and optimism (acronym HERO) within college students significantly increased their positive mental health. Based on the predictive nature of PsyCap, mental health professionals may engage more in creating programs incorporating PsyCap development intervention for college students. Implications for counseling and programmatic services for college students are presented along with suggestions for future research.

Water-quality data and Escherichia coli predictions for selected karst catchments of the upper Duck River watershed in central Tennessee, 2007–10

USGS Publications Warehouse

Murphy, Jennifer C.; Farmer, James; Layton, Alice

2016-06-13

The U.S. Geological Survey, in cooperation with the Tennessee Duck River Development Agency, monitored water quality at several locations in the upper Duck River watershed between October 2007 and September 2010. Discrete water samples collected at 24 sites in the watershed were analyzed for water quality, and Escherichia coli (E. coli) and enterococci concentrations. Additional analyses, including the determination of anthropogenic-organic compounds, bacterial concentration of resuspended sediment, and bacterial-source tracking, were performed at a subset of sites. Continuous monitoring of streamflow, turbidity, and specific conductance was conducted at seven sites; a subset of sites also was monitored for water temperature and dissolved oxygen concentration. Multiple-regression models were developed to predict instantaneous E. coli concentrations and loads at sites with continuous monitoring. This data collection effort, along with the E. coli models and predictions, support analyses of the relations among land use, bacteria source and transport, and basin hydrology in the upper Duck River watershed.

Psychological need-satisfaction and subjective well-being within social groups.

PubMed

Sheldon, Kennon M; Bettencourt, B Ann

2002-03-01

Five candidate measures of psychological need-satisfaction were evaluated as predictors of high positive and low negative mood within the group, intrinsic motivation for group activities, and high commitment to the group. Consistent with self-determination theory (Deci & Ryan, 1991), personal autonomy and interpersonal relatedness both predicted positive outcomes. Consistent with optimal distinctiveness theory (Brewer, 1991), feeling included within the group, feeling personally distinctive within the group, and feeling that the group is distinctive compared to other groups, also predicted positive outcomes. Simultaneous regression analyses indicated that the five needs were differentially related to the different well-being indicators, and also suggested that group inclusion may be the most important need to satisfy within group contexts. Supplementary analyses showed that members of formal groups felt less personal autonomy, but more group distinctiveness, compared to informal group members.

Peering into the "black box" of education interventions and attitude change: Audience characteristics moderate the effectiveness…and then only toward specific targets.

PubMed

Mansoori-Rostam, Sara Michelle; Tate, Charlotte Chucky

2017-01-01

To probe the inconsistent link between education and attitude change toward minority social groups, we conducted a field study that focused on audience characteristics and education about lesbian, gay, and transgender (LGT) targets. Participants enrolled in a sexuality course were compared to those in a neurology course, both taught by the same professor. Multiple regression analyses predicted attitude change toward LGT targets from social dominance orientation (SDO), right-wing authoritarianism (RWA), ratings of professor's characteristics, SDO by course interaction, and RWA by course interaction. Only the SDO by course interaction significantly predicted attitude change. Simple slopes analyses indicated that high-SDO participants in the sexuality course showed the most positive attitude change. These findings suggest that education may reduce prejudice for certain audience characteristics.

Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data.

PubMed

Asano, Junichi; Hirakawa, Akihiro

2017-01-01

The Cox proportional hazards cure model is a survival model incorporating a cure rate with the assumption that the population contains both uncured and cured individuals. It contains a logistic regression for the cure rate, and a Cox regression to estimate the hazard for uncured patients. A single predictive model for both the cure and hazard can be developed by using a cure model that simultaneously predicts the cure rate and hazards for uncured patients; however, model selection is a challenge because of the lack of a measure for quantifying the predictive accuracy of a cure model. Recently, we developed an area under the receiver operating characteristic curve (AUC) for determining the cure rate in a cure model (Asano et al., 2014), but the hazards measure for uncured patients was not resolved. In this article, we propose novel C-statistics that are weighted by the patients' cure status (i.e., cured, uncured, or censored cases) for the cure model. The operating characteristics of the proposed C-statistics and their confidence interval were examined by simulation analyses. We also illustrate methods for predictive model selection and for further interpretation of variables using the proposed AUCs and C-statistics via application to breast cancer data.

An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study.

PubMed

Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qiyun; Fang, Junqun

2017-02-01

An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women.This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1.The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0.95), and 0.68 (0.78), respectively. The areas under the receiver operating curve on the testing and training sets are 0.87 and 0.97, respectively.This study suggests that the BPNN model could be used to predict the risk of CHD in individuals. This model should be further improved by large-sample-size research.

Which Food Security Determinants Predict Adequate Vegetable Consumption among Rural Western Australian Children?

PubMed Central

Godrich, Stephanie L.; Lo, Johnny; Davies, Christina R.; Darby, Jill; Devine, Amanda

2017-01-01

Improving the suboptimal vegetable consumption among the majority of Australian children is imperative in reducing chronic disease risk. The objective of this research was to determine whether there was a relationship between food security determinants (FSD) (i.e., food availability, access, and utilisation dimensions) and adequate vegetable consumption among children living in regional and remote Western Australia (WA). Caregiver-child dyads (n = 256) living in non-metropolitan/rural WA completed cross-sectional surveys that included questions on FSD, demographics and usual vegetable intake. A total of 187 dyads were included in analyses, which included descriptive and logistic regression analyses via IBM SPSS (version 23). A total of 13.4% of children in this sample had adequate vegetable intake. FSD that met inclusion criteria (p ≤ 0.20) for multivariable regression analyses included price; promotion; quality; location of food outlets; variety of vegetable types; financial resources; and transport to outlets. After adjustment for potential demographic confounders, the FSD that predicted adequate vegetable consumption were, variety of vegetable types consumed (p = 0.007), promotion (p = 0.017), location of food outlets (p = 0.027), and price (p = 0.043). Food retail outlets should ensure that adequate varieties of vegetable types (i.e., fresh, frozen, tinned) are available, vegetable messages should be promoted through food retail outlets and in community settings, towns should include a range of vegetable purchasing options, increase their reliance on a local food supply and increase transport options to enable affordable vegetable purchasing. PMID:28054955

[The mediating role of the interpersonal schemas between parenting styles and psychological symptoms: a schema focused view].

PubMed

Soygüt, Gonca; Cakir, Zehra

2009-01-01

The first aim of this study was to examine the relationships between perceived parenting styles and interpersonal schemas. The second purpose was to investigate the mediator role of interpersonal schemas between perceived parenting styles and psychological symptoms. University students (N=94), ages ranging between 17-26, attending to different faculty and classes, have completed Interpersonal Schema Questionnaire, Young Parenting Inventory and Symptom Check List-90. A series of regression analyses revealed that perceived parenting styles have predictive power on a number of interpersonal schemas. Further analyses pointed out that the mediator role of Hostility situation of interpersonal schemas between psychological symptoms and normative, belittling/criticizing, pessimistic/worried parenting styles on the mother forms (Sobel z= 1.94-2.08, p < .01); and normative, belittling/criticizing, emotionally depriving, pessimistic/worried, punitive, and restricted/emotionally inhibited parenting styles (Sobel z= 2.20-2.86, p < .05-.01) on the father forms of the scales. Regression analyses pointed out the predictive power of perceived parenting styles on interpersonal schemas. Moreover, the mediator role of interpersonal schemas between perceived parenting styles and psychological symptoms was also observed. Excluding pessimistic/anxious parenting styles, perceived parenting styles of mothers and fathers differed in their relation to psychological symptoms. In overall evaluation, we believe that, although schemas and parental styles have some universalities in relation to their impacts on psychological health, further research is necessary to address their implications and possible paternal differences in our collectivistic cultural context.

Which Food Security Determinants Predict Adequate Vegetable Consumption among Rural Western Australian Children?

PubMed

Godrich, Stephanie L; Lo, Johnny; Davies, Christina R; Darby, Jill; Devine, Amanda

2017-01-03

Improving the suboptimal vegetable consumption among the majority of Australian children is imperative in reducing chronic disease risk. The objective of this research was to determine whether there was a relationship between food security determinants (FSD) (i.e., food availability, access, and utilisation dimensions) and adequate vegetable consumption among children living in regional and remote Western Australia (WA). Caregiver-child dyads ( n = 256) living in non-metropolitan/rural WA completed cross-sectional surveys that included questions on FSD, demographics and usual vegetable intake. A total of 187 dyads were included in analyses, which included descriptive and logistic regression analyses via IBM SPSS (version 23). A total of 13.4% of children in this sample had adequate vegetable intake. FSD that met inclusion criteria ( p ≤ 0.20) for multivariable regression analyses included price; promotion; quality; location of food outlets; variety of vegetable types; financial resources; and transport to outlets. After adjustment for potential demographic confounders, the FSD that predicted adequate vegetable consumption were, variety of vegetable types consumed ( p = 0.007), promotion ( p = 0.017), location of food outlets ( p = 0.027), and price ( p = 0.043). Food retail outlets should ensure that adequate varieties of vegetable types (i.e., fresh, frozen, tinned) are available, vegetable messages should be promoted through food retail outlets and in community settings, towns should include a range of vegetable purchasing options, increase their reliance on a local food supply and increase transport options to enable affordable vegetable purchasing.

Gender Roles and Substance Use Among Mexican American Adolescents: A Relationship Moderated by Acculturation?

PubMed Central

Kulis, Stephen; Marsiglia, Flavio Francisco; Nagoshi, Julie L.

2012-01-01

This research assesses the effects of adaptive/maladaptive gender roles and acculturation in predicting substance use in a 2007 sample of 1466 Mexican American seventh-grade adolescents from Phoenix, Arizona, USA. Multiple regression analyses found significant effects for both adaptive and maladaptive gender roles, as well as several gender-specific interactions between gender roles and linguistic acculturation that predicted substance use. Limitations of the research are noted, as well as implications for understanding the impact of acculturation on how gender roles differentially affect substance use in Mexican American boys versus girls. PMID:22136419

Juvenile entry into prostitution: the role of emotional abuse.

PubMed

Roe-Sepowitz, Dominique E

2012-05-01

This study seeks to assess the nature and extent of childhood emotional abuse among adult women in a residential prostitution-exiting program. Regression analyses were conducted to assess the unique role of childhood emotional abuse in the prediction of age of entry into prostitution. Childhood emotional abuse, a history of running away during childhood, and participating in survival-based exchanges of sex were significantly associated with the commercial sexual exploitation of girls younger than age 18, while childhood emotional abuse contributed to predicting a younger age of entry. Results are discussed regarding policy, prevention, and future research.

Prediction of health levels by remote sensing

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.

Alcohol-Related Facebook Activity Predicts Alcohol Use Patterns in College Students

PubMed Central

Marczinski, Cecile A.; Hertzenberg, Heather; Goddard, Perilou; Maloney, Sarah F.; Stamates, Amy L.; O’Connor, Kathleen

2016-01-01

The purpose of this study was to determine if a brief 10-item alcohol-related Facebook® activity (ARFA) questionnaire would predict alcohol use patterns in college students (N = 146). During a single laboratory session, participants first privately logged on to their Facebook® profiles while they completed the ARFA measure, which queries past 30 day postings related to alcohol use and intoxication. Participants were then asked to complete five additional questionnaires: three measures of alcohol use (the Alcohol Use Disorders Identification Test [AUDIT], the Timeline Follow-Back [TLFB], and the Personal Drinking Habits Questionnaire [PDHQ]), the Barratt Impulsiveness Scale (BIS-11), and the Marlowe-Crowne Social Desirability Scale (MC-SDS). Regression analyses revealed that total ARFA scores were significant predictors of recent drinking behaviors, as assessed by the AUDIT, TLFB, and PDHQ measures. Moreover, impulsivity (BIS-11) and social desirability (MC-SDS) did not predict recent drinking behaviors when ARFA total scores were included in the regressions. The findings suggest that social media activity measured via the ARFA scale may be useful as a research tool for identifying risky alcohol use. PMID:28138317

Can biomechanical variables predict improvement in crouch gait?

PubMed Central

Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

2011-01-01

Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

Comparative evaluation of urinary PCA3 and TMPRSS2: ERG scores and serum PHI in predicting prostate cancer aggressiveness.

PubMed

Tallon, Lucile; Luangphakdy, Devillier; Ruffion, Alain; Colombel, Marc; Devonec, Marian; Champetier, Denis; Paparel, Philippe; Decaussin-Petrucci, Myriam; Perrin, Paul; Vlaeminck-Guillem, Virginie

2014-07-30

It has been suggested that urinary PCA3 and TMPRSS2:ERG fusion tests and serum PHI correlate to cancer aggressiveness-related pathological criteria at prostatectomy. To evaluate and compare their ability in predicting prostate cancer aggressiveness, PHI and urinary PCA3 and TMPRSS2:ERG (T2) scores were assessed in 154 patients who underwent radical prostatectomy for biopsy-proven prostate cancer. Univariate and multivariate analyses using logistic regression and decision curve analyses were performed. All three markers were predictors of a tumor volume≥0.5 mL. Only PHI predicted Gleason score≥7. T2 score and PHI were both independent predictors of extracapsular extension(≥pT3), while multifocality was only predicted by PCA3 score. Moreover, when compared to a base model (age, digital rectal examination, serum PSA, and Gleason sum at biopsy), the addition of both PCA3 score and PHI to the base model induced a significant increase (+12%) when predicting tumor volume>0.5 mL. PHI and urinary PCA3 and T2 scores can be considered as complementary predictors of cancer aggressiveness at prostatectomy.

Associations among perceptual anomalies, social anxiety, and paranoia in a college student sample.

PubMed

Tone, Erin B; Goulding, Sandra M; Compton, Michael T

2011-07-30

Recent evidence suggests that normal-range paranoid ideation may be particularly likely to develop in individuals disposed to both social anxiety and perceptual anomalies. This study was designed to test the hypothesis that among college students in an unselected sample, social anxiety and experience of perceptual anomalies would not only each independently predict the experience of self-reported paranoid ideation, but would also interact to predict paranoid patterns of thought. A diverse sample of 644 students completed a large battery of self-report measures, as well as the five-factor Paranoia/Suspiciousness Questionnaire (PSQ). We conducted hierarchical multiple regression analyses predicting scores on each PSQ factor from responses on measures of social anxiety, perceptual aberration, and the interaction between the two constructs. Current general negative affect was covaried in all analyses. We found that both social anxiety and perceptual aberrations, along with negative affect, predicted multiple dimensions of paranoia as measured by the PSQ; the two constructs did not, however, interact significantly to predict any dimensions. Our findings suggest that perceptual aberration and anxiety may contribute to normal-range paranoid ideation in an additive rather than an interactive manner. Copyright © 2011 Elsevier Ltd. All rights reserved.

Use of admission serum lactate and sodium levels to predict mortality in necrotizing soft-tissue infections.

PubMed

Yaghoubian, Arezou; de Virgilio, Christian; Dauphine, Christine; Lewis, Roger J; Lin, Matthew

2007-09-01

Simple admission laboratory values can be used to classify patients with necrotizing soft-tissue infection (NSTI) into high and low mortality risk groups. Chart review. Public teaching hospital. All patients with NSTI from 1997 through 2006. Variables analyzed included medical history, admission vital signs, laboratory values, and microbiologic findings. Data analyses included univariate and classification and regression tree analyses. Mortality. One hundred twenty-four patients were identified with NSTI. The overall mortality rate was 21 of 124 (17%). On univariate analysis, factors associated with mortality included a history of cancer (P = .03), intravenous drug abuse (P < .001), low systolic blood pressure on admission (P = .03), base deficit (P = .009), and elevated white blood cell count (P = .06). On exploratory classification and regression tree analysis, admission serum lactate and sodium levels were predictors of mortality, with a sensitivity of 100%, specificity of 28%, positive predictive value of 23%, and negative predictive value of 100%. A serum lactate level greater than or equal to 54.1 mg/dL (6 mmol/L) alone was associated with a 32% mortality, whereas a serum sodium level greater than or equal to 135 mEq/L combined with a lactate level less than 54.1 mg/dL was associated with a mortality of 0%. Mortality for NSTIs remains high. A simple model, using admission serum lactate and serum sodium levels, may help identify patients at greatest risk for death.

Prediction of reported consumption of selected fat-containing foods.

PubMed

Tuorila, H; Pangborn, R M

1988-10-01

A total of 100 American females (mean age = 20.8 years) completed a questionnaire, in which their beliefs, evaluations, liking and consumption (frequency, consumption compared to others, intention to consume) of milk, cheese, ice cream, chocolate and "high-fat foods" were measured. For the design and analysis, the basic frame of reference was the Fishbein-Ajzen model of reasoned action, but the final analyses were carried out with stepwise multiple regression analysis. In addition to the components of the Fishbein-Ajzen model, beliefs and evaluations were used as independent variables. On the average, subjects reported liking all the products but not "high-fat foods", and thought that milk and cheese were "good for you" whereas the remaining items were "bad for you". Principal component analysis for beliefs revealed factors related to pleasantness/benefit aspects, to health and weight concern and to the "functionality" of the foods. In stepwise multiple regression analyses, liking was the predominant predictor of reported consumption for all the foods, but various belief factors, particularly those related to concern with weight, also significantly predicted consumption. Social factors played only a minor role. The multiple R's of the predictive functions varied from 0.49 to 0.74. The fact that all four foods studied elicited individual sets of beliefs and belief structures, and that none of them was rated similar to the generic "high-fat foods", emphasizes that consumers attach meaning to integrated food entities rather than to ingredients.

Perceived family and peer invalidation as predictors of adolescent suicidal behaviors and self-mutilation.

PubMed

Yen, Shirley; Kuehn, Kevin; Tezanos, Katherine; Weinstock, Lauren M; Solomon, Joel; Spirito, Anthony

2015-03-01

The present study investigates the longitudinal relationship between perceived family and peer invalidation and adolescent suicidal events (SE) and self-mutilation (SM) in a 6 month follow-up (f/u) study of adolescents admitted to an inpatient psychiatric unit for suicide risk. Adolescents (n=119) and their parent(s) were administered interviews and self-report assessments at baseline and at a 6 month f/u, with 99 (83%) completing both assessments. The Adolescent Longitudinal Interval Follow-Up Evaluation (A-LIFE) was modified to provide weekly ratings (baseline and each week of f/u) for perceived family and peer invalidation. Regression analyses examined whether: 1) Prospectively rated perceived family and peer invalidation at baseline predicted SE and SM during f/u; and 2) chronicity of perceived invalidation operationalized as proportion of weeks at moderate to high invalidation during f/u was associated with SE and SM during f/u. Multiple regression analyses, controlling for previously identified covariates, revealed that perceived family invalidation predicted SE over f/u for boys only and perceived peer invalidation predicted SM over f/u in the overall sample. This was the case for both baseline and f/u ratings of perceived invalidation. Our results demonstrate the adverse impact of perceived family and peer invalidation. Specifically, boys who experienced high perceived family invalidation were more likely to have an SE over f/u. Both boys and girls who experienced high perceived peer invalidation were more likely to engage in SM over f/u.

Methods for estimating selected low-flow frequency statistics for unregulated streams in Kentucky

USGS Publications Warehouse

Martin, Gary R.; Arihood, Leslie D.

2010-01-01

This report provides estimates of, and presents methods for estimating, selected low-flow frequency statistics for unregulated streams in Kentucky including the 30-day mean low flows for recurrence intervals of 2 and 5 years (30Q2 and 30Q5) and the 7-day mean low flows for recurrence intervals of 5, 10, and 20 years (7Q2, 7Q10, and 7Q20). Estimates of these statistics are provided for 121 U.S. Geological Survey streamflow-gaging stations with data through the 2006 climate year, which is the 12-month period ending March 31 of each year. Data were screened to identify the periods of homogeneous, unregulated flows for use in the analyses. Logistic-regression equations are presented for estimating the annual probability of the selected low-flow frequency statistics being equal to zero. Weighted-least-squares regression equations were developed for estimating the magnitude of the nonzero 30Q2, 30Q5, 7Q2, 7Q10, and 7Q20 low flows. Three low-flow regions were defined for estimating the 7-day low-flow frequency statistics. The explicit explanatory variables in the regression equations include total drainage area and the mapped streamflow-variability index measured from a revised statewide coverage of this characteristic. The percentage of the station low-flow statistics correctly classified as zero or nonzero by use of the logistic-regression equations ranged from 87.5 to 93.8 percent. The average standard errors of prediction of the weighted-least-squares regression equations ranged from 108 to 226 percent. The 30Q2 regression equations have the smallest standard errors of prediction, and the 7Q20 regression equations have the largest standard errors of prediction. The regression equations are applicable only to stream sites with low flows unaffected by regulation from reservoirs and local diversions of flow and to drainage basins in specified ranges of basin characteristics. Caution is advised when applying the equations for basins with characteristics near the applicable limits and for basins with karst drainage features.

Multi-system influences on adolescent risky sexual behavior.

PubMed

Chen, Angela Chia-Chen; Thompson, Elaine Adams; Morrison-Beedy, Dianne

2010-12-01

We examined multi-system influences on risky sexual behavior measured by cumulative sexual risk index and number of nonromantic sexual partners among 4,465 single, sexually experienced adolescents. Hierarchical Poisson regression analyses were conducted with Wave I-II data from the National Longitudinal Study of Adolescent Health. Individual and family factors predicted both outcome measures. Neighborhood set predicted cumulative sexual risk index only, and peer factors predicted the number of nonromantic sexual partners only. School set did not predict either outcome. There were significant associations among risky sexual behavior, drug use, and delinquent behaviors. The results highlight the need for multifaceted prevention programs that address relevant factors related to family, peer and neighborhood influence as well as individual factors among sexually active adolescents. Copyright © 2010 Wiley Periodicals, Inc.

The role of parental perceptions of tic frequency and intensity in predicting tic-related functional impairment in youth with chronic tic disorders.

PubMed

Espil, Flint M; Capriotti, Matthew R; Conelea, Christine A; Woods, Douglas W

2014-12-01

Tic severity is composed of several dimensions. Tic frequency and intensity are two such dimensions, but little empirical data exist regarding their relative contributions to functional impairment in those with chronic tic disorders (CTD). The present study examined the relative contributions of these dimensions in predicting tic-related impairment across several psychosocial domains. Using data collected from parents of youth with CTD, multivariate regression analyses revealed that both tic frequency and intensity predicted tic-related impairment in several areas; including family and peer relationships, school interference, and social endeavors, even when controlling for the presence of comorbid anxiety symptoms and Attention Deficit Hyperactivity Disorder diagnostic status. Results showed that tic intensity predicted more variance across more domains than tic frequency.

Effects of psychosocial work factors on lifestyle changes: a cohort study.

PubMed

Allard, Karin Olofsson; Thomsen, Jane Frølund; Mikkelsen, Sigurd; Rugulies, Reiner; Mors, Ole; Kærgaard, Anette; Kolstad, Henrik A; Kaerlev, Linda; Andersen, Johan Hviid; Hansen, Ase Marie; Bonde, Jens Peter

2011-12-01

To evaluate the effect of the demand-control-support model, the effort-reward imbalance model, and emotional demands on smoking, alcohol consumption, physical activity, and body mass index. This is a 2-year prospective cohort study of 3224 public sector employees. Measures were assessed with questionnaires. Multiple regression analyses were used to predict changes in lifestyle factors. Low reward predicted smoking, low-decision latitude predicted being inactive, and high demands predicted high-alcohol consumption but only for men at follow-up even after controlling for potential confounders. There were no other significant findings in the expected direction except for some of the confounders. We found only limited and inconsistent support for the hypothesis that a poor psychosocial work environment is associated with an adverse lifestyle.

On the incremental validity of irrational beliefs to predict subjective well-being while controlling for personality factors.

PubMed

Spörrle, Matthias; Strobel, Maria; Tumasjan, Andranik

2010-11-01

This research examines the incremental validity of irrational thinking as conceptualized by Albert Ellis to predict diverse aspects of subjective well-being while controlling for the influence of personality factors. Rational-emotive behavior therapy (REBT) argues that irrational beliefs result in maladaptive emotions leading to reduced well-being. Although there is some early scientific evidence for this relation, it has never been investigated whether this connection would still persist when statistically controlling for the Big Five personality factors, which were consistently found to be important determinants of well-being. Regression analyses revealed significant incremental validity of irrationality over personality factors when predicting life satisfaction, but not when predicting subjective happiness. Results are discussed with respect to conceptual differences between these two aspects of subjective well-being.

Parental warmth, control, and indulgence and their relations to adjustment in Chinese children: a longitudinal study.

PubMed

Chen, X; Liu, M; Li, D

2000-09-01

A sample of children, initially 12 years old, in the People's Republic of China participated in this 2-year longitudinal study. Data on parental warmth, control, and indulgence were collected from children's self-reports. Information concerning social, academic, and psychological adjustment was obtained from multiple sources. The results indicated that parenting styles might be a function of child gender and change with age. Regression analyses revealed that parenting styles of fathers and mothers predicted different outcomes. Whereas maternal warmth had significant contributions to the prediction of emotional adjustment, paternal warmth significantly predicted later social and school achievement. It was also found that paternal, but not maternal, indulgence significantly predicted children's adjustment difficulties. The contributions of the parenting variables might be moderated by the child's initial conditions.

Job characteristics and burnout: The moderating roles of emotional intelligence, motivation and pay among bank employees.

PubMed

Salami, Samuel O; Ajitoni, Sunday O

2016-10-01

This study investigated the prediction of burnout from job characteristics, emotional intelligence, motivation and pay among bank employees. It also examined the interactions of emotional intelligence, motivation, pay and job characteristics in the prediction of burnout. Data obtained from 230 (Males = 127, Females = 103) bank employees were analysed using Pearson's Product Moment Correlation and multiple regression analysis. Results showed that theses variables jointly and separately negatively predicted burnout components. The results further indicated that emotional intelligence, motivation and pay separately interacted with some job characteristic components to negatively predict some burnout components. The findings imply that emotional intelligence, motivation and pay could be considered by counsellors when designing interventions to reduce burnout among bank employees. © 2015 International Union of Psychological Science.

Salt Content Determination for Bentonite Mine Spoil: Saturation Extracts Versus 1:5 Extracts

Treesearch

Marguerite E. Voorhees; Daniel W. Uresk

2004-01-01

The reliability of estimating salt content in saturated extracts from 1:5 (1spoil:5water) extract levels for bentonite mine spoil was examined by regression analyses. Nine chemical variables were examined that included pH, EC, Ca++, Mg++, Na+, K+, HCO3-, SO4-, and Cl-. Ion concentrations from 1:5 extracts were estimated with high predictability for Ca++, Mg++, Na+, SO4...

The Impact of Additional Weekdays of Active Commuting to School on Children Achieving a Criterion of 300+ Minutes of Moderate-to-Vigorous Physical Activity

ERIC Educational Resources Information Center

Daly-Smith, Andy J. W.; McKenna, Jim; Radley, Duncan; Long, Jonathan

2011-01-01

Objective: To investigate the value of additional days of active commuting for meeting a criterion of 300+ minutes of moderate-to-vigorous physical activity (MVPA; 60+ mins/day x 5) during the school week. Methods: Based on seven-day diaries supported by teachers, binary logistic regression analyses were used to predict achievement of MVPA…

Validation of a prediction model that allows direct comparison of the Oxford Knee Score and American Knee Society clinical rating system.

PubMed

Maempel, J F; Clement, N D; Brenkel, I J; Walmsley, P J

2015-04-01

This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p < 0.001) when using the sum of the AKS knee and function scores. Addition of body mass index and age (other statistically significant predictors of OKS) to the algorithm did not significantly increase the predictive value. The simple regression model was used to predict the OKS in a group of 236 patients who were clinically assessed nine to ten years after TKR using the AKS system. The predicted OKS was compared with actual OKS in the second group. Intra-class correlation demonstrated excellent reliability (r = 0.81, 95% confidence intervals 0.75 to 0.85) for the combined knee and function score when used to predict OKS. Our findings will facilitate comparison of outcome data from studies and registries using either the OKS or the AKS scores and may also be of value for those undertaking meta-analyses and systematic reviews. ©2015 The British Editorial Society of Bone & Joint Surgery.

The influence of authentic leadership on safety climate in nursing.

PubMed

Dirik, Hasan Fehmi; Seren Intepeler, Seyda

2017-07-01

This study analysed nurses' perceptions of authentic leadership and safety climate and examined the contribution of authentic leadership to the safety climate. It has been suggested and emphasised that authentic leadership should be used as a guidance to ensure quality care and the safety of patients and health-care personnel. This predictive study was conducted with 350 nurses in three Turkish hospitals. The data were collected using the Authentic Leadership Questionnaire and the Safety Climate Survey and analysed using hierarchical regression analysis. The mean authentic leadership perception and the safety climate scores of the nurses were 2.92 and 3.50, respectively. The percentage of problematic responses was found to be less than 10% for only four safety climate items. Hierarchical regression analysis revealed that authentic leadership significantly predicted the safety climate. Procedural and political improvements are required in terms of the safety climate in institutions, where the study was conducted, and authentic leadership increases positive perceptions of safety climate. Exhibiting the characteristics of authentic leadership, or improving them and reflecting them on to personnel can enhance the safety climate. Planning information sharing meetings to raise the personnel's awareness of safety climate and systemic improvements can contribute to creating safe care climates. © 2017 John Wiley & Sons Ltd.

Predicting the geographic distribution of a species from presence-only data subject to detection errors

USGS Publications Warehouse

Dorazio, Robert M.

2012-01-01

Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point-process models and binary-regression models for case-augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point-process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence-only sample sizes. Analyses of presence-only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site-occupancy analyses of detections and nondetections of these species.

Polygenic scores via penalized regression on summary statistics.

PubMed

Mak, Timothy Shin Heng; Porsch, Robert Milan; Choi, Shing Wan; Zhou, Xueya; Sham, Pak Chung

2017-09-01

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred. © 2017 WILEY PERIODICALS, INC.

Statistical Approaches for Spatiotemporal Prediction of Low Flows

NASA Astrophysics Data System (ADS)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be problematic. Spatiotemporal prediction of L-moments appeared highly uncertain for higher-order moments resulting in unrealistic future low flow values. All in all, the results promote an inclusion of simple statistical methods in climate change impact assessment.

Bootstrap investigation of the stability of a Cox regression model.

PubMed

Altman, D G; Andersen, P K

1989-07-01

We describe a bootstrap investigation of the stability of a Cox proportional hazards regression model resulting from the analysis of a clinical trial of azathioprine versus placebo in patients with primary biliary cirrhosis. We have considered stability to refer both to the choice of variables included in the model and, more importantly, to the predictive ability of the model. In stepwise Cox regression analyses of 100 bootstrap samples using 17 candidate variables, the most frequently selected variables were those selected in the original analysis, and no other important variable was identified. Thus there was no reason to doubt the model obtained in the original analysis. For each patient in the trial, bootstrap confidence intervals were constructed for the estimated probability of surviving two years. It is shown graphically that these intervals are markedly wider than those obtained from the original model.

Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

USGS Publications Warehouse

Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

2015-01-01

Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

Temporal predictors of health-related quality of life in elderly people with diabetes: results of a German cohort study.

PubMed

Maatouk, Imad; Wild, Beate; Wesche, Daniela; Herzog, Wolfgang; Raum, Elke; Müller, Heiko; Rothenbacher, Dietrich; Stegmaier, Christa; Schellberg, Dieter; Brenner, Hermann

2012-01-01

The aim of the study was to determine predictors that influence health-related quality of life (HRQOL) in a large cohort of elderly diabetes patients from primary care over a follow-up period of five years. At the baseline measurement of the ESTHER cohort study (2000-2002), 1375 out of 9953 participants suffered from diabetes (13.8%). 1057 of these diabetes patients responded to the second-follow up (2005-2007). HRQOL at baseline and follow-up was measured using the SF-12; mental component scores (MCS) and physical component scores (PCS) were calculated; multiple linear regression models were used to determine predictors of HRQOL at follow-up. As possible predictors for HRQOL, the following baseline variables were examined: treatment with insulin, glycated hemoglobin (HbA1c), number of diabetes related complications, number of comorbid diseases, Body-Mass-Index (BMI), depression and HRQOL. Regression analyses were adjusted for sociodemographic variables and smoking status. 1034 patients (97.8%) responded to the SF-12 both at baseline and after five years and were therefore included in the study. Regression analyses indicated that significant predictors of decreased MCS were a lower HRQOL, a higher number of diabetes related complications and a reported history of depression at baseline. Complications, BMI, smoking and HRQOL at baseline significantly predicted PCS at the five year follow-up. Our findings expand evidence from previous cross-sectional data indicating that in elderly diabetes patients, depression, diabetes related complications, smoking and BMI are temporally predictive for HRQOL.

Temporal Predictors of Health-Related Quality of Life in Elderly People with Diabetes: Results of a German Cohort Study

PubMed Central

Wesche, Daniela; Herzog, Wolfgang; Raum, Elke; Müller, Heiko; Rothenbacher, Dietrich; Stegmaier, Christa; Schellberg, Dieter; Brenner, Hermann

2012-01-01

Background The aim of the study was to determine predictors that influence health-related quality of life (HRQOL) in a large cohort of elderly diabetes patients from primary care over a follow-up period of five years. Methods and Results At the baseline measurement of the ESTHER cohort study (2000–2002), 1375 out of 9953 participants suffered from diabetes (13.8%). 1057 of these diabetes patients responded to the second-follow up (2005–2007). HRQOL at baseline and follow-up was measured using the SF-12; mental component scores (MCS) and physical component scores (PCS) were calculated; multiple linear regression models were used to determine predictors of HRQOL at follow-up. As possible predictors for HRQOL, the following baseline variables were examined: treatment with insulin, glycated hemoglobin (HbA1c), number of diabetes related complications, number of comorbid diseases, Body-Mass-Index (BMI), depression and HRQOL. Regression analyses were adjusted for sociodemographic variables and smoking status. 1034 patients (97.8%) responded to the SF-12 both at baseline and after five years and were therefore included in the study. Regression analyses indicated that significant predictors of decreased MCS were a lower HRQOL, a higher number of diabetes related complications and a reported history of depression at baseline. Complications, BMI, smoking and HRQOL at baseline significantly predicted PCS at the five year follow-up. Conclusions Our findings expand evidence from previous cross-sectional data indicating that in elderly diabetes patients, depression, diabetes related complications, smoking and BMI are temporally predictive for HRQOL. PMID:22292092

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study.

PubMed

Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier

2018-02-01

Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the Period-Amplitude and Amplitude-Period Relationships

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

2008-01-01

Examined are Period-Amplitude and Amplitude-Period relationships based on the cyclic behavior of the 12-month moving averages of monthly mean sunspot numbers for cycles 0.23, both in terms of Fisher's exact tests for 2x2 contingency tables and linear regression analyses. Concerning the Period-Amplitude relationship (same cycle), because cycle 23's maximum amplitude is known to be 120.8, the inferred regressions (90-percent prediction intervals) suggest that its period will be 131 +/- 24 months (using all cycles) or 131 +/- 18 months (ignoring cycles 2 and 4, which have the extremes of period, 108 and 164 months, respectively). Because cycle 23 has already persisted for 142 months (May 1996 through February 2008), based on the latter prediction, it should end before September 2008. Concerning the Amplitude-Period relationship (following cycle maximum amplitude versus preceding cycle period), because cycle 23's period is known to be at least 142 months, the inferred regressions (90-percent prediction intervals) suggest that cycle 24's maximum amplitude will be about less than or equal to 96.1 +/- 55.0 (using all cycle pairs) or less than or equal to 91.0 +/- 36.7 (ignoring statistical outlier cycle pairs). Hence, cycle 24's maximum amplitude is expected to be less than 151, perhaps even less than 128, unless cycle pair 23/24 proves to be a statistical outlier.

Linear regression analysis of Hospital Episode Statistics predicts a large increase in demand for elective hand surgery in England.

PubMed

Bebbington, Emily; Furniss, Dominic

2015-02-01

We integrated two factors, demographic population shifts and changes in prevalence of disease, to predict future trends in demand for hand surgery in England, to facilitate workforce planning. We analysed Hospital Episode Statistics data for Dupuytren's disease, carpal tunnel syndrome, cubital tunnel syndrome, and trigger finger from 1998 to 2011. Using linear regression, we estimated trends in both diagnosis and surgery until 2030. We integrated this regression with age specific population data from the Office for National Statistics in order to estimate how this will contribute to a change in workload over time. There has been a significant increase in both absolute numbers of diagnoses and surgery for all four conditions. Combined with future population data, we calculate that the total operative burden for these four conditions will increase from 87,582 in 2011 to 170,166 (95% confidence interval 144,517-195,353) in 2030. The prevalence of these diseases in the ageing population, and increasing prevalence of predisposing factors such as obesity and diabetes, may account for the predicted increase in workload. The most cost effective treatments must be sought, which requires high quality clinical trials. Our methodology can be applied to other sub-specialties to help anticipate the need for future service provision. Copyright © 2014 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

PubMed

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

A statistical model to predict total column ozone in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Tan, K. C.; Lim, H. S.; Mat Jafri, M. Z.

2016-03-01

This study aims to predict monthly columnar ozone in Peninsular Malaysia based on concentrations of several atmospheric gases. Data pertaining to five atmospheric gases (CO2, O3, CH4, NO2, and H2O vapor) were retrieved by satellite scanning imaging absorption spectrometry for atmospheric chartography from 2003 to 2008 and used to develop a model to predict columnar ozone in Peninsular Malaysia. Analyses of the northeast monsoon (NEM) and the southwest monsoon (SWM) seasons were conducted separately. Based on the Pearson correlation matrices, columnar ozone was negatively correlated with H2O vapor but positively correlated with CO2 and NO2 during both the NEM and SWM seasons from 2003 to 2008. This result was expected because NO2 is a precursor of ozone. Therefore, an increase in columnar ozone concentration is associated with an increase in NO2 but a decrease in H2O vapor. In the NEM season, columnar ozone was negatively correlated with H2O (-0.847), NO2 (0.754), and CO2 (0.477); columnar ozone was also negatively but weakly correlated with CH4 (-0.035). In the SWM season, columnar ozone was highly positively correlated with NO2 (0.855), CO2 (0.572), and CH4 (0.321) and also highly negatively correlated with H2O (-0.832). Both multiple regression and principal component analyses were used to predict the columnar ozone value in Peninsular Malaysia. We obtained the best-fitting regression equations for the columnar ozone data using four independent variables. Our results show approximately the same R value (≈ 0.83) for both the NEM and SWM seasons.

Predicting having condoms available among adolescents: the role of personal norm and enjoyment.

PubMed

Jellema, Ilke J; Abraham, Charles; Schaalma, Herman P; Gebhardt, Winifred A; van Empelen, Pepijn

2013-05-01

Having condoms available has been shown to be an important predictor of condom use. We examined whether or not personal norm and goal enjoyment contribute to predicting having condoms available in the context of cognition specified by the theory of planned behaviour (TPB). Prospective survey study, with a baseline and follow-up measurement (at 3 months). Data were gathered using an online survey. In total 282 adolescents (mean age = 15.6, 74% female adolescents) completed both questionnaires. At baseline, demographics, sexual experience, condom use, TPB variables, descriptive norm, personal norm, and enjoyment towards having condoms available were measured. At T2 (3 months later) having condoms available was measured. Direct and moderating effects of personal norm and goal enjoyment were examined by means of hierarchical linear regression analyses. Regression analyses yielded a direct effect of self-efficacy and personal norm on condom availability. In addition, moderation of the intention-behaviour relation by goal enjoyment added to the variance explained. The final model explained approximately 35% of the variance in condom availability. Personal norm and goal enjoyment add to the predictive utility of a TPB model of having condoms available and may be useful intervention targets. What is already known about this subject? Having condoms available is an important prerequisite for actual condom use. The theory of planned behaviour has successfully been applied to explain condom availability behaviour. The theory of planned behaviour has been criticized for not adequately taking into account affective motivation. What does this study add? Personal norm and goal enjoyment add to the predictive utility of the model. Personal norm explains condom availability directly, enjoyment increases intention enactment. Personal norm and goal enjoyment therefore are useful intervention targets. © 2012 The British Psychological Society.

Predictive Accuracy of Violence Risk Scale-Sexual Offender Version Risk and Change Scores in Treated Canadian Aboriginal and Non-Aboriginal Sexual Offenders.

PubMed

Olver, Mark E; Sowden, Justina N; Kingston, Drew A; Nicholaichuk, Terry P; Gordon, Audrey; Beggs Christofferson, Sarah M; Wong, Stephen C P

2018-04-01

The present study examined the predictive properties of Violence Risk Scale-Sexual Offender version (VRS-SO) risk and change scores among Aboriginal and non-Aboriginal sexual offenders in a combined sample of 1,063 Canadian federally incarcerated men. All men participated in sexual offender treatment programming through the Correctional Service of Canada (CSC) at sites across its five regions. The Static-99R was also examined for comparison purposes. In total, 393 of the men were identified as Aboriginal (i.e., First Nations, Métis, Circumpolar) while 670 were non-Aboriginal and primarily White. Aboriginal men scored significantly higher on the Static-99R and VRS-SO and had higher rates of sexual and violent recidivism; however, there were no significant differences between Aboriginal and non-Aboriginal groups on treatment change with both groups demonstrating close to a half-standard deviation of change pre and post treatment. VRS-SO risk and change scores significantly predicted sexual and violent recidivism over fixed 5- and 10-year follow-ups for both racial/ancestral groups. Cox regression survival analyses also demonstrated positive treatment changes to be significantly associated with reductions in sexual and violent recidivism among Aboriginal and non-Aboriginal men after controlling baseline risk. A series of follow-up Cox regression analyses demonstrated that risk and change score information accounted for much of the observed differences between Aboriginal and non-Aboriginal men in rates of sexual recidivism; however, marked group differences persisted in rates of general violent recidivism even after controlling for these covariates. The results support the predictive properties of VRS-SO risk and change scores with treated Canadian Aboriginal sexual offenders.

Empirical relations of rock properties of outcrop and core samples from the Northwest German Basin for geothermal drilling

NASA Astrophysics Data System (ADS)

Reyer, D.; Philipp, S. L.

2014-09-01

Information about geomechanical and physical rock properties, particularly uniaxial compressive strength (UCS), are needed for geomechanical model development and updating with logging-while-drilling methods to minimise costs and risks of the drilling process. The following parameters with importance at different stages of geothermal exploitation and drilling are presented for typical sedimentary and volcanic rocks of the Northwest German Basin (NWGB): physical (P wave velocities, porosity, and bulk and grain density) and geomechanical parameters (UCS, static Young's modulus, destruction work and indirect tensile strength both perpendicular and parallel to bedding) for 35 rock samples from quarries and 14 core samples of sandstones and carbonate rocks. With regression analyses (linear- and non-linear) empirical relations are developed to predict UCS values from all other parameters. Analyses focus on sedimentary rocks and were repeated separately for clastic rock samples or carbonate rock samples as well as for outcrop samples or core samples. Empirical relations have high statistical significance for Young's modulus, tensile strength and destruction work; for physical properties, there is a wider scatter of data and prediction of UCS is less precise. For most relations, properties of core samples plot within the scatter of outcrop samples and lie within the 90% prediction bands of developed regression functions. The results indicate the applicability of empirical relations that are based on outcrop data on questions related to drilling operations when the database contains a sufficient number of samples with varying rock properties. The presented equations may help to predict UCS values for sedimentary rocks at depth, and thus develop suitable geomechanical models for the adaptation of the drilling strategy on rock mechanical conditions in the NWGB.

glmnetLRC f/k/a lrc package: Logistic Regression Classification

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

2016-06-09

Methods for fitting and predicting logistic regression classifiers (LRC) with an arbitrary loss function using elastic net or best subsets. This package adds additional model fitting features to the existing glmnet and bestglm R packages. This package was created to perform the analyses described in Amidan BG, Orton DJ, LaMarche BL, et al. 2014. Signatures for Mass Spectrometry Data Quality. Journal of Proteome Research. 13(4), 2215-2222. It makes the model fitting available in the glmnet and bestglm packages more general by identifying optimal model parameters via cross validation with an customizable loss function. It also identifies the optimal threshold formore » binary classification.« less

Digital literacy of youth and young adults with intellectual disability predicted by support needs and social maturity.

PubMed

Seok, Soonhwa; DaCosta, Boaventura

2017-01-01

This study investigated relationships between digital propensity and support needs as well as predictors of digital propensity in the context of support intensity, age, gender, and social maturity. A total of 118 special education teachers rated the support intensity, digital propensity, and social maturity of 352 students with intellectual disability. Leveraging the Digital Propensity Index, Supports Intensity Scale, and the Social Maturity Scale, descriptive statistics, correlations, multiple regressions, and regression analyses were employed. The findings revealed significant relationships between digital propensity and support needs. In addition, significant predictors of digital propensity were found with regard to support intensity, age, gender, and social maturity.

Adjustment of regional regression equations for urban storm-runoff quality using at-site data

USGS Publications Warehouse

Barks, C.S.

1996-01-01

Regional regression equations have been developed to estimate urban storm-runoff loads and mean concentrations using a national data base. Four statistical methods using at-site data to adjust the regional equation predictions were developed to provide better local estimates. The four adjustment procedures are a single-factor adjustment, a regression of the observed data against the predicted values, a regression of the observed values against the predicted values and additional local independent variables, and a weighted combination of a local regression with the regional prediction. Data collected at five representative storm-runoff sites during 22 storms in Little Rock, Arkansas, were used to verify, and, when appropriate, adjust the regional regression equation predictions. Comparison of observed values of stormrunoff loads and mean concentrations to the predicted values from the regional regression equations for nine constituents (chemical oxygen demand, suspended solids, total nitrogen as N, total ammonia plus organic nitrogen as N, total phosphorus as P, dissolved phosphorus as P, total recoverable copper, total recoverable lead, and total recoverable zinc) showed large prediction errors ranging from 63 percent to more than several thousand percent. Prediction errors for 6 of the 18 regional regression equations were less than 100 percent and could be considered reasonable for water-quality prediction equations. The regression adjustment procedure was used to adjust five of the regional equation predictions to improve the predictive accuracy. For seven of the regional equations the observed and the predicted values are not significantly correlated. Thus neither the unadjusted regional equations nor any of the adjustments were appropriate. The mean of the observed values was used as a simple estimator when the regional equation predictions and adjusted predictions were not appropriate.

Deadlines at work and sleep quality. Cross-sectional and longitudinal findings among Danish knowledge workers.

PubMed

Rugulies, Reiner; Martin, Marie H T; Garde, Anne Helene; Persson, Roger; Albertsen, Karen

2012-03-01

Exposure to deadlines at work is increasing in several countries and may affect health. We aimed to investigate cross-sectional and longitudinal associations between frequency of difficult deadlines at work and sleep quality. Study participants were knowledge workers, drawn from a representative sample of Danish employees who responded to a baseline questionnaire in 2006 (n = 363) and a follow-up questionnaire in 2007 (n = 302). Frequency of difficult deadlines was measured by self-report and categorized into low, intermediate, and high. Sleep quality was measured with a Total Sleep Quality Score and two indexes (Awakening Index and Disturbed Sleep Index) derived from the Karolinska Sleep Questionnaire. Analyses on the association between frequency of deadlines and sleep quality scores were conducted with multiple linear regression models, adjusted for potential confounders. In addition, we used multiple logistic regression models to analyze whether frequency of deadlines at baseline predicted caseness of sleep problems at follow-up among participants free of sleep problems at baseline. Frequent deadlines were cross-sectionally and longitudinally associated with poorer sleep quality on all three sleep quality measures. Associations in the longitudinal analyses were greatly attenuated when we adjusted for baseline sleep quality. The logistic regression analyses showed that frequent deadlines at baseline were associated with elevated odds ratios for caseness of sleep problems at follow-up, however, confidence intervals were wide in these analyses. Frequent deadlines at work were associated with poorer sleep quality among Danish knowledge workers. We recommend investigating the relation between deadlines and health endpoints in large-scale epidemiologic studies. Copyright © 2011 Wiley Periodicals, Inc.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Predictive capacity of sperm quality parameters and sperm subpopulations on field fertility after artificial insemination in sheep.

PubMed

Santolaria, P; Vicente-Fiel, S; Palacín, I; Fantova, E; Blasco, M E; Silvestre, M A; Yániz, J L

2015-12-01

This study was designed to evaluate the relevance of several sperm quality parameters and sperm population structure on the reproductive performance after cervical artificial insemination (AI) in sheep. One hundred and thirty-nine ejaculates from 56 adult rams were collected using an artificial vagina, processed for sperm quality assessment and used to perform 1319 AI. Analyses of sperm motility by computer-assisted sperm analysis (CASA), sperm nuclear morphometry by computer-assisted sperm morphometry analysis (CASMA), membrane integrity by acridine orange-propidium iodide combination and sperm DNA fragmentation using the sperm chromatin dispersion test (SCD) were performed. Clustering procedures using the sperm kinematic and morphometric data resulted in the classification of spermatozoa into three kinematic and three morphometric sperm subpopulations. Logistic regression procedures were used, including fertility at AI as the dependent variable (measured by lambing, 0 or 1) and farm, year, month of AI, female parity, female lambing-treatment interval, ram, AI technician and sperm quality parameters (including sperm subpopulations) as independent factors. Sperm quality variables remaining in the logistic regression model were viability and VCL. Fertility increased for each one-unit increase in viability (by a factor of 1.01) and in VCL (by a factor of 1.02). Multiple linear regression analyses were also performed to analyze the factors possibly influencing ejaculate fertility (N=139). The analysis yielded a significant (P<0.05) relationship between sperm viability and ejaculate fertility. The discriminant ability of the different semen variables to predict field fertility was analyzed using receiver operating characteristic (ROC) curve analysis. Sperm viability and VCL showed significant, albeit limited, predictive capacity on field fertility (0.57 and 0.54 Area Under Curve, respectively). The distribution of spermatozoa in the different subpopulations was not related to fertility. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction of intrinsic motivation and sports performance using 2 x 2 achievement goal framework.

PubMed

Li, Chiung-Huang; Chi, Likang; Yeh, Suh-Ruu; Guo, Kwei-Bin; Ou, Cheng-Tsung; Kao, Chun-Chieh

2011-04-01

The purpose of this study was to examine the influence of 2 x 2 achievement goals on intrinsic motivation and performance in handball. Participants were 164 high school athletes. All completed the 2 x 2 Achievement Goals Questionnaire for Sport and the Intrinsic Motivation subscale of the Sport Motivation Scale; the coach for each team rated his athletes' overall sports performance. Using simultaneous-regression analyses, mastery-approach goals positively predicted both intrinsic motivation and performance in sports, whereas performance-avoidance goals negatively predicted sports performance. These results suggest that athletes who pursue task mastery and improvement of their competence perform well and enjoy their participation. In contrast, those who focus on avoiding normative incompetence perform poorly.

Prediction of Poor Ovarian response by Biochemical and Biophysical Markers: A Logistic Regression Model.

PubMed

Jaiswar, S P; Natu, S M; Sujata; Sankhwar, P L; Manjari, Gupta

2015-12-01

To study correlation between ovarian reserve with biophysical markers (antral follicle count and ovarian volume) and biochemical markers (S. FSH, S. Inhibin B, and S. AMH) and use these markers to predict poor ovarian response to ovarian induction. This is a prospective observational study. One hundred infertile women attending the Obst & Gynae Dept, KGMU were recruited. Blood samples were collected on day 2/day 3 for assessment of S. FSH, S. Inhibin B, and S. AMH and TVS were done for antral follicle count and ovarian volume. Clomephene citrate 100 mg 1OD was given from day 2 to 6, and patients were followed up with serial USG measurements. The numbers of dominant follicles (> or = 14 mm) at the time of hCG administration were counted. Patients with <3 follicles in the 1st cycle were subjected to the 2nd cycle of clomephene 100 mg 1OD from day 2 to day 6 with Inj HMG 150 IU given i.m. starting from day 8 and every alternate day until at least one leading follicle attained ≥18 mm. Development of <3 follicles at end of the 2nd cycle was considered as poor response. Univariate analyses showed that s. inhibin B presented the highest (ROCAUC = 0.862) discriminating potential for predicting poor ovarian response, In multivariate logistic regression model, the variables age, FSH, AMH, INHIBIN B, and AFC remained significant, and the resulting model showed a predicted accuracy of 84.4 %. A derived multimarker computation by a logistic regression model for predicting poor ovarian response was obtained through this study. Thus, potential poor responders could be identified easily, and appropriate ovarian stimulation protocol could be devised for such pts.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities.

PubMed

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the "S&P 500 1987" bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

Combination of c-reactive protein and squamous cell carcinoma antigen in predicting postoperative prognosis for patients with squamous cell carcinoma of the esophagus.

PubMed

Feng, Ji-Feng; Chen, Sheng; Yang, Xun

2017-09-08

We initially proposed a useful and novel prognostic model, named CCS [Combination of c-reactive protein (CRP) and squamous cell carcinoma antigen (SCC)], for predicting the postoperative survival in patients with esophageal squamous cell carcinoma (ESCC). Two hundred and fifty-two patients with resectable ESCC were included in this retrospective study. A logistic regression was performed and yielded a logistic equation. The CCS was calculated by the combined CRP and SCC. The optimal cut-off value for CCS was evaluated by X-tile program. Univariate and multivariate analyses were used to evaluate the predictive factors. In addition, a novel nomogram model was also performed to predict the prognosis for patients with ESCC. In the current study, CCS was calculated as CRP+6.33 SCC according to the logistic equation. The optimal cut-off value was 15.8 for CCS according to the X-tile program. Kaplan-Meier analyses demonstrated that high CCS group had a significantly poor 5-year cancer-specific survival (CSS) than low CCS group (10.3% vs. 47.3%, P <0.001). According to multivariate analyses, CCS ( P =0.004), but not CRP ( P =0.466) or SCC ( P =0.926), was an independent prognostic factor. A nomogram could be more accuracy for CSS (Harrell's c-index: 0.70). The CCS is a usefull and independent predictive factor in patients with ESCC.

Economic deprivation and racial segregation: comparing Superfund sites in Portland, Oregon and Detroit, Michigan.

PubMed

Smith, Chad L

2009-09-01

The research presented here weighs the ability of two major explanations of social inequality-Massey and Denton's racial segregation explanation and Wilson's emphasis on economic deprivation (concentrated poverty)-to predict environmental inequality. Two sets of logistic regression analyses are used to predict the location of Superfund sites in Portland, Oregon and Detroit, Michigan providing a conditional understanding of environmental inequality within a larger sociological context. The analysis includes a general examination of the two theories in all census tracts in both cities and a set of analyses focusing upon Black neighborhoods in Detroit. The findings indicate that there is support for explanations of environmental inequality that include both racial segregation and economic deprivation, but that the more powerful of the two is economic deprivation. The results suggest that even though African-American neighborhoods disproportionately house Superfund sites, these facilities are more likely to be located in Black neighborhoods that are economically deprived.

A chemometric approach to the characterisation of historical mortars

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

Rampazzi, L.; Pozzi, A.; Sansonetti, A.

2006-06-15

The compositional knowledge of historical mortars is of great concern in case of provenance and dating investigations and of conservation works since the nature of the raw materials suggests the most compatible conservation products. The classic characterisation usually goes through various analytical determinations, while conservation laboratories call for simple and quick analyses able to enlighten the nature of mortars, usually in terms of the binder fraction. A chemometric approach to the matter is here undertaken. Specimens of mortars were prepared with calcitic and dolomitic binders and analysed by Atomic Spectroscopy. Principal Components Analysis (PCA) was used to investigate the featuresmore » of specimens and samples. A Partial Least Square (PLS1) regression was done in order to predict the binder/aggregate ratio. The model was applied to historical mortars from the churches of St. Lorenzo (Milan) and St. Abbondio (Como). The accordance between the predictive model and the real samples is discussed.« less

Predicting consumer preferences for mineral composition of bottled and tap water.

PubMed

Platikanov, Stefan; Hernández, Alejandra; González, Susana; Luis Cortina, Jose; Tauler, Roma; Devesa, Ricard

2017-01-01

The overall liking for taste of water was correlated with the mineral composition of selected bottled and tap waters. Sixty-nine untrained volunteers assessed and rated twenty-five different commercial bottled and tap waters from. Water samples were physicochemical characterised by analysing conductivity, pH, total dissolved solids (TDS) and major anions and cations: HCO 3 - , SO 4 2- , Cl - , NO 3 - , Ca 2+ , Mg 2+ , Na + , and K + . Residual chlorine levels were also analysed in the tap water samples. Globally, volunteers preferred waters rich in calcium bicarbonate and sulfate, rather than in sodium chloride. This study also demonstrated that it was possible to accurately predict the overall liking by a Partial Least Squares regression using either all measured physicochemical parameters or a reduced number of them. These results were in agreement with previously published results using trained panellists. Copyright © 2016 Elsevier B.V. All rights reserved.

Multiple Losses: The Psychological and Economic Well-Being of Survivors of Intimate Partner Violence.

PubMed

Sauber, Elizabeth W; O'Brien, Karen M

2017-05-01

This study advanced knowledge regarding the mechanisms through which intimate partner violence relates to psychological and financial distress with a sample of diverse low-income women. Data were collected from 147 female domestic violence survivors who were abused by a male partner within the past 6 months. Three hierarchical regression analyses revealed that psychological, physical, and economic abuse were predictive of posttraumatic stress, depression, and economic self-sufficiency among survivors. Guided by the Conservation of Resources Theory, the loss of financial, work, and interpersonal resources also predicted these three outcomes, above and beyond abuse experiences (i.e., economically controlling behaviors, economic sabotage, and interpersonal resource loss were unique predictors). In addition, bootstrap mediation analyses showed that interpersonal resource loss partially mediated the relationship between psychological abuse and mental health outcomes. Together, these findings can be used to inform future interventions to promote the financial and psychological well-being of survivors.

Genome-wide regression and prediction with the BGLR statistical package.

PubMed

Pérez, Paulino; de los Campos, Gustavo

2014-10-01

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis. Copyright © 2014 by the Genetics Society of America.

The relationships between empathy, stress and social support among medical students

PubMed Central

Kim, Dong-hee; Kim, Seok Kyoung; Yi, Young Hoon; Jeong, Jae Hoon; Chae, Jiun; Hwang, Jiyeon; Roh, HyeRin

2015-01-01

Objectives To examine the relationship between stress, social support, and empathy among medical students. Methods We evaluated the relationships between stress and empathy, and social support and empathy among medical students. The respondents completed a question-naire including demographic information, the Jefferson Scale of Empathy, the Perceived Stress Scale, and the Multidimensional Scale of Perceived Social Support. Corre-lation and linear regression analyses were conducted, along with sub-analyses according to gender, admission system, and study year. Results In total, 2,692 questionnaires were analysed. Empathy and social support positively correlated, and empathy and stress negatively correlated. Similar correla-tion patterns were detected in the sub-analyses; the correla-tion between empathy and stress among female students was negligible. In the regression model, stress and social support predicted empathy among all the samples. In the sub-analysis, stress was not a significant predictor among female and first-year students. Conclusions Stress and social support were significant predictors of empathy among all the students. Medical educators should provide means to foster resilience against stress or stress alleviation, and to ameliorate social support, so as to increase or maintain empathy in the long term. Furthermore, stress management should be emphasised, particularly among female and first-year students. PMID:26342190

The role of lifetime anxiety history in the course of bipolar spectrum disorders.

PubMed

Titone, Madison K; Freed, Rachel D; O'Garro-Moore, Jared K; Gepty, Andrew; Ng, Tommy H; Stange, Jonathan P; Abramson, Lyn Y; Alloy, Lauren B

2018-06-01

Individuals with bipolar spectrum disorder (BSD) frequently meet criteria for comorbid anxiety disorders, and anxiety may be an important factor in the etiology and course of BSDs. The current study examined the association of lifetime anxiety disorders with prospective manic/hypomanic versus major depressive episodes. Participants were 244 young adults (aged 17-26) with milder forms of BSDs (i.e., bipolar-II, cyclothymia, BD-NOS). First, bivariate analyses assessed differences in baseline clinical characteristics between participants with and without DSM-IV anxiety diagnoses. Second, negative binomial regression analyses tested whether lifetime anxiety predicted number of manic/hypomanic or major depressive episodes developed during the study. Third, survival analyses evaluated whether lifetime anxiety predicted time to onset of manic/hypomanic and major depressive episodes. Results indicated that anxiety history was associated with greater illness severity at baseline. Over follow-up, anxiety history predicted fewer manic/hypomanic episodes, but did not predict number of major depressive episodes. Anxiety history also was associated with longer time to onset of manic/hypomanic episodes, but shorter time to onset of depressive episodes. Findings corroborate past studies implicating anxiety disorders as salient influences on the course of BSDs. Moreover, results extend prior research by indicating that anxiety disorders may be linked with reduced manic/hypomanic phases of illness. Copyright © 2018 Elsevier B.V. All rights reserved.

Authoritative school climate and high school dropout rates.

PubMed

Jia, Yuane; Konold, Timothy R; Cornell, Dewey

2016-06-01

This study tested the association between school-wide measures of an authoritative school climate and high school dropout rates in a statewide sample of 315 high schools. Regression models at the school level of analysis used teacher and student measures of disciplinary structure, student support, and academic expectations to predict overall high school dropout rates. Analyses controlled for school demographics of school enrollment size, percentage of low-income students, percentage of minority students, and urbanicity. Consistent with authoritative school climate theory, moderation analyses found that when students perceive their teachers as supportive, high academic expectations are associated with lower dropout rates. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Identifying dyslexia in adults: an iterative method using the predictive value of item scores and self-report questions.

PubMed

Tamboer, Peter; Vorst, Harrie C M; Oort, Frans J

2014-04-01

Methods for identifying dyslexia in adults vary widely between studies. Researchers have to decide how many tests to use, which tests are considered to be the most reliable, and how to determine cut-off scores. The aim of this study was to develop an objective and powerful method for diagnosing dyslexia. We took various methodological measures, most of which are new compared to previous methods. We used a large sample of Dutch first-year psychology students, we considered several options for exclusion and inclusion criteria, we collected as many cognitive tests as possible, we used six independent sources of biographical information for a criterion of dyslexia, we compared the predictive power of discriminant analyses and logistic regression analyses, we used both sum scores and item scores as predictor variables, we used self-report questions as predictor variables, and we retested the reliability of predictions with repeated prediction analyses using an adjusted criterion. We were able to identify 74 dyslexic and 369 non-dyslexic students. For 37 students, various predictions were too inconsistent for a final classification. The most reliable predictions were acquired with item scores and self-report questions. The main conclusion is that it is possible to identify dyslexia with a high reliability, although the exact nature of dyslexia is still unknown. We therefore believe that this study yielded valuable information for future methods of identifying dyslexia in Dutch as well as in other languages, and that this would be beneficial for comparing studies across countries.

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.

Predictability Effects on Durations of Content and Function Words in Conversational English

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

Bell, Alan; Brenier, Jason; Gregory, Michelle L.

Content and function word duration are affected differently by their frequency and predictability. Regression analyses of conversational speech show that content words are shorter when they are more frequent, but function words are not. Repeated content words are shorter, but function words are not. Furthermore, function words have shorter pronunciations, after controlling for frequency and predictability. both content and function words are strongly affected by predictability from the word following them, and only very frequent function words show sensitivity to predictability from the preceding word. The results support the view that content and function words are accessed by different productionmore » mechanisms. We argue that words’ form differences due to frequency or repetition stem from their faster or slower lexical access, mediated by a general mechanism that coordinates the pace of higher-level planning and the execution of the articulatory plan.« less

The Predictive Effects of Protection Motivation Theory on Intention and Behaviour of Physical Activity in Patients with Type 2 Diabetes

PubMed Central

Ali Morowatisharifabad, Mohammad; Abdolkarimi, Mahdi; Asadpour, Mohammad; Fathollahi, Mahmood Sheikh; Balaee, Parisa

2018-01-01

INTRODUCTION: Theory-based education tailored to target behaviour and group can be effective in promoting physical activity. AIM: The purpose of this study was to examine the predictive power of Protection Motivation Theory on intent and behaviour of Physical Activity in Patients with Type 2 Diabetes. METHODS: This descriptive study was conducted on 250 patients in Rafsanjan, Iran. To examine the scores of protection motivation theory structures, a researcher-made questionnaire was used. Its validity and reliability were confirmed. The level of physical activity was also measured by the International Short - form Physical Activity Inventory. Its validity and reliability were also approved. Data were analysed by statistical tests including correlation coefficient, chi-square, logistic regression and linear regression. RESULTS: The results revealed that there was a significant correlation between all the protection motivation theory constructs and the intention to do physical activity. The results showed that the Theory structures were able to predict 60% of the variance of physical activity intention. The results of logistic regression demonstrated that increase in the score of physical activity intent and self - efficacy increased the chance of higher level of physical activity by 3.4 and 1.5 times, respectively OR = (3.39, 1.54). CONCLUSION: Considering the ability of protection motivation theory structures to explain the physical activity behaviour, interventional designs are suggested based on the structures of this theory, especially to improve self -efficacy as the most powerful factor in predicting physical activity intention and behaviour. PMID:29731945

Predictive and Feedback Performance Errors are Signaled in the Simple Spike Discharge of Individual Purkinje Cells

PubMed Central

Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.

2012-01-01

The cerebellum has been implicated in processing motor errors required for online control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudo-randomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV–VI is significantly correlated with position, distance and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R2 temporal profiles resulting from regressing firing with individual errors exhibit two peak R2 values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:23115173

Using heart rate to predict energy expenditure in large domestic dogs.

PubMed

Gerth, N; Ruoß, C; Dobenecker, B; Reese, S; Starck, J M

2016-06-01

The aim of this study was to establish heart rate as a measure of energy expenditure in large active kennel dogs (28 ± 3 kg bw). Therefore, the heart rate (HR)-oxygen consumption (V˙O2) relationship was analysed in Foxhound-Boxer-Ingelheim-Labrador cross-breds (FBI dogs) at rest and graded levels of exercise on a treadmill up to 60-65% of maximal aerobic capacity. To test for effects of training, HR and V˙O2 were measured in female dogs, before and after a training period, and after an adjacent training pause to test for reversibility of potential effects. Least squares regression was applied to describe the relationship between HR and V˙O2. The applied training had no statistically significant effect on the HR-V˙O2 regression. A general regression line from all data collected was prepared to establish a general predictive equation for energy expenditure from HR in FBI dogs. The regression equation established in this study enables fast estimation of energy requirement for running activity. The equation is valid for large dogs weighing around 30 kg that run at ground level up to 15 km/h with a heart rate maximum of 190 bpm irrespective of the training level. Journal of Animal Physiology and Animal Nutrition © 2015 Blackwell Verlag GmbH.

The role of narcissism in health-risk and health-protective behaviors.

PubMed

Hill, Erin M

2016-09-01

This study examined the role of narcissism in health-risk and health-protective behaviors in a sample of 365 undergraduate students. Regression analyses were used to test the influence of narcissism on health behaviors. Narcissism was positively predictive of alcohol use, marijuana use, and risky driving behaviors, and it was associated with an increased likelihood of consistently having a healthy eating pattern. Narcissism was also positively predictive of physical activity. Results are discussed with reference to the potential short-term and long-term health implications and the need for future research on the factors involved in the relationship between narcissism and health behaviors. © The Author(s) 2015.

Does trust of patients in their physician predict loyalty to the health care insurer? The Israeli case study.

PubMed

Gabay, Gillie

2016-01-01

This pioneer study tests the relationship between patients' trust in their physicians and patients' loyalty to their health care insurers. This is a cross-sectional study using a representative sample of patients from all health care insurers with identical health care plans. Regression analyses and Baron and Kenny's model were used to test the study model. Patient trust in the physician did not predict loyalty to the insurer. Loyalty to the physician did not mediate the relationship between trust in the physician and loyalty to the insurer. Satisfaction with the physician was the only predictor of loyalty to the insurer.

Regimen Difficulty and Medication Non-Adherence and the Interaction Effects of Gender and Age.

PubMed

Dalvi, Vidya; Mekoth, Nandakumar

2017-12-08

Medication non-adherence is a global health issue. Numerous factors predict it. This study is aimed to identify the association between regimen difficulty and medication non-adherence among patients with chronic conditions and testing the interaction effects of gender and age on the same. It was a cross-sectional study conducted among 479 outpatients from India. Convenience sampling method was used. Multiple regression analyses were performed to find the predictors of non-adherence and to test interaction effects. Regimen difficulty predicted medication non-adherence. The patient's gender and age have interaction effects on the relationship between regimen difficulty and medication non-adherence.

Extension of the Job Demands-Resources model in the prediction of burnout and engagement among teachers over time.

PubMed

Lorente Prieto, Laura; Salanova Soria, Marisa; Martínez Martínez, Isabel; Schaufeli, Wilmar

2008-08-01

Our purpose was to extend the Job Demand-Resources Model (Schaufeli & Bakker, 2004) by including personal resources, job demands and job resources to predict burnout (exhaustion, cynicism, depersonalization) and work engagement (vigour and dedication). The sample comprised 274 teachers from 23 secondary schools of the Valencian Community (Spain). Hierarchical multiple regression analyses have revealed: (1) the predictor effect of quantitative overload on exhaustion and dedication at T2, (2) role conflict on cynicism and (3) role ambiguity on dedication. Lastly, the mediating role of burnout and engagement at T2. Practical implications and directions of future research are discussed.

Suicidal ideation and its correlates: testing the interpersonal theory of suicide in Chinese students.

PubMed

Zhang, Jie; Lester, David; Zhao, Sibo; Zhou, Chengchao

2013-01-01

The present study explored the validity of Joiner's interpersonal theory of suicide in a sample of 439 Chinese university students 17 to 24 years of age. The results indicated that the three elements of the theory (thwarted belongingness, perceived burdensomeness, and acquired capability for self-harm) were associated with current suicidal ideation in the total sample of students. For men, only thwarted belongingness and perceived burdensomeness predicted suicidal ideation, whereas all three elements of the theory predicted suicidal ideation for women. Multiple regression analyses, controlling for other variables, supported the role of burdensomeness and acquired capability for suicide, but not thwarted belongingness.

Predicting nurse burnout from demands and resources in three acute care hospitals under different forms of ownership: a cross-sectional questionnaire survey.

PubMed

Hansen, Niklas; Sverke, Magnus; Näswall, Katharina

2009-01-01

Health care organizations have changed dramatically over the last decades, with hospitals undergoing restructurings and privatizations. The aim of this study is to enhance the understanding of the origin and prevalence of burnout in health care by investigating factors in the psychosocial work environment and comparing three Swedish emergency hospitals with different types of ownership. A cross-sectional design was used. We selected a total sample of 1800 registered nurses from three acute care hospitals, one private for-profit, one private non-profit and one publicly administered. A total of 1102 questionnaires were included in the analyses. The examined ownership types were a private for-profit, a private non-profit and a traditional publicly administered hospital. All were situated in the Stockholm region, Sweden. Data were collected by questionnaires using validated instruments, in accordance with the Job Demands-Resources Model and Maslach's Burnout Inventory. Descriptive statistics, correlation analyses, multivariate covariance analyses and multiple regression analyses were conducted. The results showed that the burnout levels were the highest at the private for-profit hospital and lowest at the publicly administered hospital. However, in contrast to expectations the demands were not higher overall at the for-profit organization or lowest at the public administration unit, and overall, resources were not better in the private for-profit or worse at the publicly administered hospital. Multiple regression analyses showed that several of the demands included were related to higher burnout levels. Job resources were linked to lower burnout levels, but not for all variables. Profit orientation in health care seems to result in higher burnout levels for registered nurses compared to a publicly administered hospital. In general, demands were more predictive of burnout than resources, and there were only marginal differences in the pattern of predictors across hospitals.

The Role of Parental Perceptions of Tic Frequency and Intensity in Predicting Tic-Related Functional Impairment in Youth with Chronic Tic Disorders

PubMed Central

Espil, Flint M.; Capriotti, Matthew R.; Conelea, Christine A.; Woods, Douglas W.

2014-01-01

Tic severity is composed of several dimensions. Tic frequency and intensity are two such dimensions, but little empirical data exist regarding their relative contributions to functional impairment in those with Chronic Tic Disorders (CTD). The present study examined the relative contributions of these dimensions in predicting tic-related impairment across several psychosocial domains. Using data collected from parents of youth with CTD, multivariate regression analyses revealed that both tic frequency and intensity predicted tic-related impairment in several areas; including family and peer relationships, school interference, and social endeavors, even when controlling for the presence of comorbid anxiety symptoms and Attention Deficit Hyperactivity Disorder diagnostic status. Results showed that tic intensity predicted more variance across more domains than tic frequency. PMID:24395287

Insufficient sleep predicts clinical burnout.

PubMed

Söderström, Marie; Jeding, Kerstin; Ekstedt, Mirjam; Perski, Aleksander; Akerstedt, Torbjörn

2012-04-01

The present prospective study aimed to identify risk factors for subsequent clinical burnout. Three hundred eighty-eight working individuals completed a baseline questionnaire regarding work stress, sleep, mood, health, and so forth. During a 2-year period, 15 subjects (7 women and 8 men) of the total sample were identified as "burnout cases," as they were assessed and referred to treatment for clinical burnout. Questionnaire data from the baseline measurement were used as independent variables in a series of logistic regression analyses to predict clinical burnout. The results identified "too little sleep (< 6 h)" as the main risk factor for burnout development, with adjustment for "work demands," "thoughts of work during leisure time," and "sleep quality." The first two factors were significant predictors in earlier steps of the multivariate regression. The results indicate that insufficient sleep, preoccupation with thoughts of work during leisure time, and high work demands are risk factors for subsequent burnout. The results suggest a chain of causation. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Contextual predictive factors of child sexual abuse: the role of parent-child interaction.

PubMed

Ramírez, Clemencia; Pinzón-Rondón, Angela María; Botero, Juan Carlos

2011-12-01

To determine the prevalence of child sexual abuse in the Colombian coasts, as well as to assess the role of parent-child interactions on its occurrence and to identify factors from different environmental levels that predict it. This cross-sectional study explores the results of 1,089 household interviews responded by mothers. Descriptive analyses and multivariate logistic regressions were conducted, with child sexual abuse regressed on parent-child interactions, children's characteristics, maternal characteristics, family characteristics, and community characteristics. 1.2% of the mothers reported that their children had been sexually abused. Families that communicated with their children were less likely to report child sexual abuse, each additional standard deviation of communication reduced child sexual abuse 3.5 times. Affection and negative treatment to the children were not associated with child sexual abuse. Families who experienced intimate partner violence and violent communities were more likely to experience child sexual abuse. Interventions are needed to address the problem of child sexual abuse. Copyright © 2011 Elsevier Ltd. All rights reserved.

Lower back pain in nurses working in home care: linked to work-family conflict, emotional dissonance, and appreciation?

PubMed

Elfering, Achim; Häfliger, Evelyne; Celik, Zehra; Grebner, Simone

2018-07-01

In industrial countries home care services for elderly people living in the community are growing rapidly. Home care nursing is intensive and the nurses often suffer from musculoskeletal pain. Time pressure and job control are job-related factors linked to the risk of experiencing lower back pain (LBP) and LBP-related work impairment. This survey investigated whether work-family conflict (WFC), emotional dissonance and being appreciated at work have incremental predictive value. Responses were obtained from 125 home care nurses (63% response rate). Multiple linear regression showed that emotional dissonance and being appreciated at work predicted LBP intensity and LBP-related disability independently of time pressure and job control. WFC was not a predictor of LBP-related disability in multiple regression analyses despite a zero-order correlation with it. Redesigning the working pattern of home care nurses to reduce the emotional demands and improve appreciation of their work might reduce the incidence of LBP in this group.

Longitudinal cognitive biomarkers predicting symptom onset in presymptomatic frontotemporal dementia.

PubMed

Jiskoot, Lize C; Panman, Jessica L; van Asseldonk, Lauren; Franzen, Sanne; Meeter, Lieke H H; Donker Kaat, Laura; van der Ende, Emma L; Dopper, Elise G P; Timman, Reinier; van Minkelen, Rick; van Swieten, John C; van den Berg, Esther; Papma, Janne M

2018-06-01

We performed 4-year follow-up neuropsychological assessment to investigate cognitive decline and the prognostic abilities from presymptomatic to symptomatic familial frontotemporal dementia (FTD). Presymptomatic MAPT (n = 15) and GRN mutation carriers (n = 31), and healthy controls (n = 39) underwent neuropsychological assessment every 2 years. Eight mutation carriers (5 MAPT, 3 GRN) became symptomatic. We investigated cognitive decline with multilevel regression modeling; the prognostic performance was assessed with ROC analyses and stepwise logistic regression. MAPT converters declined on language, attention, executive function, social cognition, and memory, and GRN converters declined on attention and executive function (p < 0.05). Cognitive decline in ScreeLing phonology (p = 0.046) and letter fluency (p = 0.046) were predictive for conversion to non-fluent variant PPA, and decline on categorical fluency (p = 0.025) for an underlying MAPT mutation. Using longitudinal neuropsychological assessment, we detected a mutation-specific pattern of cognitive decline, potentially suggesting prognostic value of neuropsychological trajectories in conversion to symptomatic FTD.

Positive predictors of quality of life for postpartum mothers with a history of childhood maltreatment.

PubMed

Irwin, Jessica L; Beeghly, Marjorie; Rosenblum, Katherine L; Muzik, Maria

2016-12-01

The postpartum period brings a host of biopsychosocial, familial, and economic changes, which may be challenging for new mothers, especially those with trauma histories. Trauma-exposed women are at heightened risk for psychiatric symptomatology and reduced quality of life. The current study sought to evaluate whether a set of hypothesized promotive factors assessed during the first 18 months postpartum (positive parenting, family cohesion, and maternal resilience) are associated with life satisfaction in this population, after controlling for income and postpartum psychiatric symptoms. Analyses were based on data collected for 266 mother-infant dyads from a longitudinal cohort study, Maternal Anxiety during the Childbearing Years (MACY), of women oversampled for childhood maltreatment history. Hierarchical linear regression was used to evaluate the study hypotheses. Consistent with prior work, greater postpartum psychiatric symptoms and less income predicted poor perceptions of life quality. In hierarchical regressions controlling for income and psychiatric symptoms, positive parenting and family cohesion predicted unique variance in mothers' positive perceptions of life quality, and resilience was predictive beyond all other factors. Factors from multiple levels of analysis (maternal, dyadic, and familial) may serve as promotive factors predicting positive perceptions of life quality among women with childhood trauma histories, even those struggling with high levels of psychiatric or economic distress.

The predicting roles of reasons for living and social support on depression, anxiety and stress among young people in Malaysia.

PubMed

Amit, N; Ibrahim, N; Aga Mohd Jaladin, R; Che Din, N

2017-10-01

This research examined the predicting roles of reasons for living and social support on depression, anxiety and stress in Malaysia. This research was carried out on a sample of 263 participants (age range 12-24 years old), from Klang Valley, Selangor. The survey package comprises demographic information, a measure of reasons for living, social support, depression, anxiety and stress. To analyse the data, correlation analysis and a series of linear multiple regression analysis were carried out. Findings showed that there were low negative relationships between all subdomains and the total score of reasons for living and depression. There were also low negative relationships between domain-specific of social support (family and friends) and total social support and depression. In terms of the family alliance, self-acceptance and total score of reasons for living, they were negatively associated with anxiety, whereas family social support was negatively associated with stress. The linear regression analysis showed that only future optimism and family social support found to be the significant predictors for depression. Family alliance and total reasons for living were significant in predicting anxiety, whereas family social support was significant in predicting stress. These findings have the potential to promote awareness related to depression, anxiety, and stress among youth in Malaysia.

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

PubMed

Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V

2014-01-01

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Minority stress and sexual problems among African-American gay and bisexual men.

PubMed

Zamboni, Brian D; Crawford, Isiaah

2007-08-01

Minority stress, such as racism and gay bashing, may be associated with sexual problems, but this notion has not been examined in the literature. African-American gay/bisexual men face a unique challenge in managing a double minority status, putting them at high risk for stress and sexual problems. This investigation examined ten predictors of sexual problems among 174 African-American gay/bisexual men. Covarying for age, a forward multiple regression analysis showed that the measures of self-esteem, male gender role stress, HIV prevention self-efficacy, and lifetime experiences with racial discrimination significantly added to the prediction of sexual problems. Gay bashing, psychiatric symptoms, low life satisfaction, and low social support were significantly correlated with sexual problems, but did not add to the prediction of sexual problems in the regression analysis. Mediation analyses showed that stress predicted psychiatric symptoms, which then predicted sexual problems. Sexual problems were not significantly related to HIV status, racial/ethnic identity, or gay identity. The findings from this study showed a relationship between experiences with racial and sexual discrimination and sexual problems while also providing support for mediation to illustrate how stress might cause sexual problems. Addressing minority stress in therapy may help minimize and treat sexual difficulties among minority gay/bisexual men.

Multi-modal imaging predicts memory performance in normal aging and cognitive decline.

PubMed

Walhovd, K B; Fjell, A M; Dale, A M; McEvoy, L K; Brewer, J; Karow, D S; Salmon, D P; Fennema-Notestine, C

2010-07-01

This study (n=161) related morphometric MR imaging, FDG-PET and APOE genotype to memory scores in normal controls (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). Stepwise regression analyses focused on morphometric and metabolic characteristics of the episodic memory network: hippocampus, entorhinal, parahippocampal, retrosplenial, posterior cingulate, precuneus, inferior parietal, and lateral orbitofrontal cortices. In NC, hippocampal metabolism predicted learning; entorhinal metabolism predicted recognition; and hippocampal metabolism predicted recall. In MCI, thickness of the entorhinal and precuneus cortices predicted learning, while parahippocampal metabolism predicted recognition. In AD, posterior cingulate cortical thickness predicted learning, while APOE genotype predicted recognition. In the total sample, hippocampal volume and metabolism, cortical thickness of the precuneus, and inferior parietal metabolism predicted learning; hippocampal volume and metabolism, parahippocampal thickness and APOE genotype predicted recognition. Imaging methods appear complementary and differentially sensitive to memory in health and disease. Medial temporal and parietal metabolism and morphometry best explained memory variance. Medial temporal characteristics were related to learning, recall and recognition, while parietal structures only predicted learning. Copyright 2008. Published by Elsevier Inc.

Spatially Explicit Estimates of Suspended Sediment and Bedload Transport Rates for Western Oregon and Northwestern California

NASA Astrophysics Data System (ADS)

O'Connor, J. E.; Wise, D. R.; Mangano, J.; Jones, K.

2015-12-01

Empirical analyses of suspended sediment and bedload transport gives estimates of sediment flux for western Oregon and northwestern California. The estimates of both bedload and suspended load are from regression models relating measured annual sediment yield to geologic, physiographic, and climatic properties of contributing basins. The best models include generalized geology and either slope or precipitation. The best-fit suspended-sediment model is based on basin geology, precipitation, and area of recent wildfire. It explains 65% of the variance for 68 suspended sediment measurement sites within the model area. Predicted suspended sediment yields range from no yield from the High Cascades geologic province to 200 tonnes/ km2-yr in the northern Oregon Coast Range and 1000 tonnes/km2-yr in recently burned areas of the northern Klamath terrain. Bed-material yield is similarly estimated from a regression model based on 22 sites of measured bed-material transport, mostly from reservoir accumulation analyses but also from several bedload measurement programs. The resulting best-fit regression is based on basin slope and the presence/absence of the Klamath geologic terrane. For the Klamath terrane, bed-material yield is twice that of the other geologic provinces. This model explains more than 80% of the variance of the better-quality measurements. Predicted bed-material yields range up to 350 tonnes/ km2-yr in steep areas of the Klamath terrane. Applying these regressions to small individual watersheds (mean size; 66 km2 for bed-material; 3 km2 for suspended sediment) and cumulating totals down the hydrologic network (but also decreasing the bed-material flux by experimentally determined attrition rates) gives spatially explicit estimates of both bed-material and suspended sediment flux. This enables assessment of several management issues, including the effects of dams on bedload transport, instream gravel mining, habitat formation processes, and water-quality. The combined fluxes can also be compared to long-term rock uplift and cosmogenically determined landscape erosion rates.

Predictors of transitions from single to multiple job holding: Results of a longitudinal study among employees aged 45-64 in the Netherlands.

PubMed

Bouwhuis, Stef; Geuskens, Goedele A; Boot, Cécile R L; Bongers, Paulien M; van der Beek, Allard J

2017-08-01

To construct prediction models for transitions to combination multiple job holding (MJH) (multiple jobs as an employee) and hybrid MJH (being an employee and self-employed), among employees aged 45-64. A total of 5187 employees in the Netherlands completed online questionnaires annually between 2010 and 2013. We applied logistic regression analyses with a backward elimination strategy to construct prediction models. Transitions to combination MJH and hybrid MJH were best predicted by a combination of factors including: demographics, health and mastery, work characteristics, work history, skills and knowledge, social factors, and financial factors. Not having a permanent contract and a poor household financial situation predicted both transitions. Some predictors only predicted combination MJH, e.g., working part-time, or hybrid MJH, e.g., work-home interference. A wide variety of factors predict combination MJH and/or hybrid MJH. The prediction model approach allowed for the identification of predictors that have not been previously studied. © 2017 Wiley Periodicals, Inc.

Predicting psychopharmacological drug effects on actual driving performance (SDLP) from psychometric tests measuring driving-related skills.

PubMed

Verster, Joris C; Roth, Thomas

2012-03-01

There are various methods to examine driving ability. Comparisons between these methods and their relationship with actual on-road driving is often not determined. The objective of this study was to determine whether laboratory tests measuring driving-related skills could adequately predict on-the-road driving performance during normal traffic. Ninety-six healthy volunteers performed a standardized on-the-road driving test. Subjects were instructed to drive with a constant speed and steady lateral position within the right traffic lane. Standard deviation of lateral position (SDLP), i.e., the weaving of the car, was determined. The subjects also performed a psychometric test battery including the DSST, Sternberg memory scanning test, a tracking test, and a divided attention test. Difference scores from placebo for parameters of the psychometric tests and SDLP were computed and correlated with each other. A stepwise linear regression analysis determined the predictive validity of the laboratory test battery to SDLP. Stepwise regression analyses revealed that the combination of five parameters, hard tracking, tracking and reaction time of the divided attention test, and reaction time and percentage of errors of the Sternberg memory scanning test, together had a predictive validity of 33.4%. The psychometric tests in this test battery showed insufficient predictive validity to replace the on-the-road driving test during normal traffic.

Beyond Access and Exposure: Implications of Sneaky Media Use for Preschoolers' Sleep Behavior.

PubMed

Moorman, Jessica D; Harrison, Kristen

2018-01-09

Greater consumption of and access to screen media are known correlates of unhealthy sleep behavior in preschoolers. What remains unknown, however, is the role a child's media use plays in this association. Parents and guardians of U.S. preschoolers (N = 278, average child age 56 months) provided information about their child's nightly duration of sleep, daily duration of nap, quantity of screen media use, sneaky media use, and the presence of a screen media device in the bedroom. We assessed four media: television, DVD/VCRs, video games, and computer/Internet. Based on rationales of sleep displacement, the forbidden fruit hypothesis, and social cognitive theory, we predicted that increased consumption of and access to media, along with sneaky media use, would predict a shorter duration of nightly sleep and longer duration of daily nap across the four screen media. In correlational analyses, a clear pattern emerged with quantity of media use, screen media in the bedroom, and sneaky media use associated with shorter nightly duration of sleep and longer duration of daily nap. In regression analyses, only weekday evening television viewing and sneaky media use predicted shorter nightly sleep duration; weekend morning and evening DVD use predicted longer naps.

Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.

PubMed

D'Archivio, Angelo Antonio; Incani, Angela; Ruggieri, Fabrizio

2011-01-01

In this paper, we use a quantitative structure-retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GC×GC). We analyse the GC×GC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column ((1) t (R) and (2) t (R), respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for (1) t (R) and (2) t (R) from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling (1) t (R) and (2) t (R), respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model (1) t (R) and (2) t (R) without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for (1) t (R) and (2) t (R) in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

An introduction to using Bayesian linear regression with clinical data.

PubMed

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development and validation of prognostic models in metastatic breast cancer: a GOCS study.

PubMed

Rabinovich, M; Vallejo, C; Bianco, A; Perez, J; Machiavelli, M; Leone, B; Romero, A; Rodriguez, R; Cuevas, M; Dansky, C

1992-01-01

The significance of several prognostic factors and the magnitude of their influence on response rate and survival were assessed by means of uni- and multivariate analyses in 362 patients with stage IV (UICC) breast carcinoma receiving combination chemotherapy as first systemic treatment over an 8-year period. Univariate analyses identified performance status and prior adjuvant radiotherapy as predictors of objective regression (OR), whereas the performance status, prior chemotherapy and radiotherapy (adjuvants), white blood cells count, SGOT and SGPT levels, and metastatic pattern were significantly correlated to survival. In multivariate analyses favorable characteristics associated to OR were prior adjuvant radiotherapy, no prior chemotherapy and postmenopausal status. Regarding survival, the performance status and visceral involvement were selected by the Cox model. The predictive accuracy of the logistic and the proportional hazards models was retrospectively tested in the training sample, and prospectively in a new population of 126 patients also receiving combined chemotherapy as first treatment for metastatic breast cancer. A certain overfitting to data in the training sample was observed with the regression model for response. However, the discriminative ability of the Cox model for survival was clearly confirmed.

Examining the relationship between adolescent sexual risk-taking and perceptions of monitoring, communication, and parenting styles.

PubMed

Huebner, Angela J; Howell, Laurie W

2003-08-01

To examine the relationship between adolescent sexual risk-taking and perception of parental monitoring, frequency of parent-adolescent communication, and parenting style. The influences of gender, age, and ethnicity are also of interest. Data were collected from 7th-12th grade students in six rural, ethnically diverse school located in adjacent counties in a Southeastern state. A 174-item instrument assessed adolescent perceptions, behaviors and attitudes. Youth who had engaged in sexual intercourse (n = 1160) were included in the analyses. Logistic regression analyses were conducted to identify parenting practices that predicted high versus low-risk sex (defined by number of partners and use of condoms). Variables included parental monitoring, parent-adolescent communication, parenting style, parenting process interaction effects and interaction effects among these three parenting processes and gender, age and ethnicity. Analyses included frequencies, cross-tabulations and logistic regression. Parental monitoring, parental monitoring by parent-adolescent communication and parenting style by ethnicity were significant predictors of sexual risk-taking. No gender or age interactions were noted. Parental monitoring, parent-adolescent communication and parenting style are all important variables to consider when examining sexual risk-taking among adolescents.

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T

2016-12-20

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Radiographic absence of the posterior communicating arteries and the prediction of cognitive dysfunction after carotid endarterectomy.

PubMed

Sussman, Eric S; Kellner, Christopher P; Mergeche, Joanna L; Bruce, Samuel S; McDowell, Michael M; Heyer, Eric J; Connolly, E Sander

2014-09-01

Approximately 25% of patients exhibit cognitive dysfunction 24 hours after carotid endarterectomy (CEA). One of the purported mechanisms of early cognitive dysfunction (eCD) is hypoperfusion due to inadequate collateral circulation during cross-clamping of the carotid artery. The authors assessed whether poor collateral circulation within the circle of Willis, as determined by preoperative CT angiography (CTA) or MR angiography (MRA), could predict eCD. Patients who underwent CEA after preoperative MRA or CTA imaging and full neuropsychometric evaluation were included in this study (n = 42); 4 patients were excluded due to intraoperative electroencephalographic changes and subsequent shunt placement. Thirty-eight patients were included in the statistical analyses. Patients were stratified according to posterior communicating artery (PCoA) status (radiographic visualization of at least 1 PCoA vs of no PCoAs). Variables with p < 0.20 in univariate analyses were included in a stepwise multivariate logistic regression model to identify predictors of eCD after CEA. Overall, 23.7% of patients exhibited eCD. In the final multivariate logistic regression model, radiographic absence of both PCoAs was the only independent predictor of eCD (OR 9.64, 95% CI 1.43-64.92, p = 0.02). The absence of both PCoAs on preoperative radiographic imaging is predictive of eCD after CEA. This finding supports the evidence for an underlying ischemic etiology of eCD. Larger studies are justified to verify the findings of this study. Clinical trial registration no.: NCT00597883 ( http://www.clinicaltrials.gov ).

Predicting Dural Tear in Compound Depressed Skull Fractures: A Prospective Multicenter Correlational Study.

PubMed

Salia, Shemsedin Musefa; Mersha, Hagos Biluts; Aklilu, Abenezer Tirsit; Baleh, Abat Sahlu; Lund-Johansen, Morten

2018-06-01

Compound depressed skull fracture (DSF) is a neurosurgical emergency. Preoperative knowledge of dural status is indispensable for treatment decision making. This study aimed to determine predictors of dural tear from clinical and imaging characteristics in patients with compound DSF. This prospective, multicenter correlational study in neurosurgical hospitals in Addis Ababa, Ethiopia, included 128 patients operated on from January 1, 2016, to October 31, 2016. Clinical, imaging, and intraoperative findings were evaluated. Univariate and multivariate analyses were used to establish predictors of dural tear. A logistic regression model was developed to predict probability of dural tear. Model validation was done using the receiver operating characteristic curve. Dural tear was seen in 55.5% of 128 patients. Demographics, injury mechanism, clinical presentation, and site of DSF had no significant correlation with dural tear. In univariate and multivariate analyses, depth of fracture depression (odds ratio 1.3, P < 0.001), pneumocephalus (odds ratio 2.8, P = 0.005), and brain contusions/intracerebral hematoma (odds ratio 5.5, P < 0.001) were significantly correlated with dural tear. We developed a logistic regression model (diagnostic test) to calculate probability of dural tear. Using the receiver operating characteristic curve, we determined the cutoff value for a positive test giving the highest accuracy to be 30% with a corresponding sensitivity of 93.0% and specificity of 43.9%. Dural tear in compound DSF can be predicted with 93.0% sensitivity using preoperative findings and may guide treatment decision making in resource-limited settings where risk of extensive cranial surgery outweighs the benefit. Copyright © 2018 Elsevier Inc. All rights reserved.

Dysfunctional attitudes and poor problem solving skills predict hopelessness in major depression.

PubMed

Cannon, B; Mulroy, R; Otto, M W; Rosenbaum, J F; Fava, M; Nierenberg, A A

1999-09-01

Hopelessness is a significant predictor of suicidality, but not all depressed patients feel hopeless. If clinicians can predict hopelessness, they may be able to identify those patients at risk of suicide and focus interventions on factors associated with hopelessness. In this study, we examined potential predictors of hopelessness in a sample of depressed outpatients. In this study, we examined potential demographic, diagnostic, and symptom predictors of hopelessness in a sample of 138 medication-free outpatients (73 women and 65 men) with a primary diagnosis of major depression. The significance of predictors was evaluated in both simple and multiple regression analyses. Consistent with previous studies, we found no significant associations between demographic and diagnostic variables and greater hopelessness. Hopelessness was significantly associated with greater depression severity, poor problem solving abilities as assessed by the Problem Solving Inventory, and each of two measures of dysfunctional cognitions (the Dysfunctional Attitudes Scale and the Cognitions Questionnaire). In a stepwise multiple regression equation, however, only dysfunctional cognitions and poor problem solving offered non-redundant prediction of hopelessness scores, and accounted for 20% of the variance in these scores. This study is based on depressed patients entering into an outpatient treatment protocol. All analyses were correlational in nature, and no causal links can be concluded. Our findings, identifying clinical correlates of hopelessness, provide clinicians with potential additional targets for assessment and treatment of suicidal risk. In particular, clinical attention to dysfunctional attitudes and problem solving skills may be important for further reduction of hopelessness and perhaps suicidal risk.

Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

NASA Astrophysics Data System (ADS)

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

2016-06-01

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

The predictive value of the antioxidative function of HDL for cardiovascular disease and graft failure in renal transplant recipients.

PubMed

Leberkühne, Lynn J; Ebtehaj, Sanam; Dimova, Lidiya G; Dikkers, Arne; Dullaart, Robin P F; Bakker, Stephan J L; Tietge, Uwe J F

2016-06-01

Protection of low-density lipoproteins (LDL) against oxidative modification is a key anti-atherosclerotic property of high-density lipoproteins (HDL). This study evaluated the predictive value of the HDL antioxidative function for cardiovascular mortality, all-cause mortality and chronic graft failure in renal transplant recipients (RTR). The capacity of HDL to inhibit native LDL oxidation was determined in vitro in a prospective cohort of renal transplant recipients (RTR, n = 495, median follow-up 7.0 years). The HDL antioxidative functionality was significantly higher in patients experiencing graft failure (57.4 ± 9.7%) than in those without (54.2 ± 11.3%; P = 0.039), while there were no differences for cardiovascular and all-cause mortality. Specifically glomerular filtration rate (P = 0.001) and C-reactive protein levels (P = 0.006) associated independently with antioxidative functionality in multivariate linear regression analyses. Cox regression analysis demonstrated a significant relationship between antioxidative functionality of HDL and graft failure in age-adjusted analyses, but significance was lost following adjustment for baseline kidney function and inflammatory load. No significant association was found between HDL antioxidative functionality and cardiovascular and all-cause mortality. This study demonstrates that the antioxidative function of HDL (i) does not predict cardiovascular or all-cause mortality in RTR, but (ii) conceivably contributes to the development of graft failure, however, not independent of baseline kidney function and inflammatory load. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Diversity of leisure-time sport activities in adolescence as a predictor of leisure-time physical activity in adulthood.

PubMed

Mäkelä, S; Aaltonen, S; Korhonen, T; Rose, R J; Kaprio, J

2017-12-01

Because sustained physical activity is important for a healthy life, this paper examined whether a greater diversity of sport activities during adolescence predicts higher levels of leisure-time physical activity (LTPA) in adulthood. From sport activity participation reported by 17-year-old twins, we formed five groups: 1, 2, 3, 4, and 5+ different sport activities. At follow-up in their mid-thirties, twins were divided into four activity classes based on LTPA, including active commuting. Multinomial regression analyses, adjusted for several confounders, were conducted separately for male (N=1288) and female (N=1770) participants. Further, conditional logistic regression analysis included 23 twin pairs discordant for both diversity of sport activities in adolescence and LTPA in adulthood. The diversity of leisure-time sport activities in adolescence had a significant positive association with adulthood LTPA among females. Membership in the most active adult quartile, compared to the least active quartile, was predicted by participation in 2, 3, 4, and 5+ sport activities in adolescence with odds ratios: 1.52 (P=.11), 1.86 (P=.02), 1.29 (P=.39), and 3.12 (P=5.4e-05), respectively. Within-pair analyses, limited by the small sample of twins discordant for both adolescent activities and adult outcomes, did not replicate the association. A greater diversity of leisure-time sport activities in adolescence predicts higher levels of LTPA in adulthood in females, but the causal nature of this association remains unresolved. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A stream-gaging network analysis for the 7-day, 10-year annual low flow in New Hampshire streams

USGS Publications Warehouse

Flynn, Robert H.

2003-01-01

The 7-day, 10-year (7Q10) low-flow-frequency statistic is a widely used measure of surface-water availability in New Hampshire. Regression equations and basin-characteristic digital data sets were developed to help water-resource managers determine surface-water resources during periods of low flow in New Hampshire streams. These regression equations and data sets were developed to estimate streamflow statistics for the annual and seasonal low-flow-frequency, and period-of-record and seasonal period-of-record flow durations. generalized-least-squares (GLS) regression methods were used to develop the annual 7Q10 low-flow-frequency regression equation from 60 continuous-record stream-gaging stations in New Hampshire and in neighboring States. In the regression equation, the dependent variables were the annual 7Q10 flows at the 60 stream-gaging stations. The independent (or predictor) variables were objectively selected characteristics of the drainage basins that contribute flow to those stations. In contrast to ordinary-least-squares (OLS) regression analysis, GLS-developed estimating equations account for differences in length of record and spatial correlations among the flow-frequency statistics at the various stations.A total of 93 measurable drainage-basin characteristics were candidate independent variables. On the basis of several statistical parameters that were used to evaluate which combination of basin characteristics contribute the most to the predictive power of the equations, three drainage-basin characteristics were determined to be statistically significant predictors of the annual 7Q10: (1) total drainage area, (2) mean summer stream-gaging station precipitation from 1961 to 90, and (3) average mean annual basinwide temperature from 1961 to 1990.To evaluate the effectiveness of the stream-gaging network in providing regional streamflow data for the annual 7Q10, the computer program GLSNET (generalized-least-squares NETwork) was used to analyze the network by application of GLS regression between streamflow and the climatic and basin characteristics of the drainage basin upstream from each stream-gaging station. Improvement to the predictive ability of the regression equations developed for the network analyses is measured by the reduction in the average sampling-error variance, and can be achieved by collecting additional streamflow data at existing stations. The predictive ability of the regression equations is enhanced even further with the addition of new stations to the network. Continued data collection at unregulated stream-gaging stations with less than 14 years of record resulted in the greatest cost-weighted reduction to the average sampling-error variance of the annual 7Q10 regional regression equation. The addition of new stations in basins with underrepresented values for the independent variables of the total drainage area, average mean annual basinwide temperature, or mean summer stream-gaging station precipitation in the annual 7Q10 regression equation yielded a much greater cost-weighted reduction to the average sampling-error variance than when more data were collected at existing unregulated stations. To maximize the regional information obtained from the stream-gaging network for the annual 7Q10, ranking of the streamflow data can be used to determine whether an active station should be continued or if a new or discontinued station should be activated for streamflow data collection. Thus, this network analysis can help determine the costs and benefits of continuing the operation of a particular station or activating a new station at another location to predict the 7Q10 at ungaged stream reaches. The decision to discontinue an existing station or activate a new station, however, must also consider its contribution to other water-resource analyses such as flood management, water quality, or trends in land use or climatic change.

Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative.

PubMed

Gomar, Jesus J; Bobes-Bascaran, Maria T; Conejero-Goldberg, Concepcion; Davies, Peter; Goldberg, Terry E

2011-09-01

Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors. To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease. Longitudinal study. We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses. The Alzheimer's Disease Neuroimaging Initiative public database. Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes. In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease. Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic trajectory of the disease than by a sharp decline in functional ability and, to a lesser extent, by declines in executive function.

Social determinants of mental health service utilization in Switzerland.

PubMed

Dey, Michelle; Jorm, Anthony Francis

2017-01-01

To investigate whether mental health services utilization in Switzerland is equitably distributed (i.e., predicted only by the need of a person). Data on 17,789 participants of the Swiss Health Survey 2012 (≥15 years) was analysed. Logistic regression analyses were conducted to predict: having been in treatment for a psychological problem; having used psychotropic medication; having had medical treatment for depression; and having visited a psychologist or psychotherapist. Need (depression severity and risky alcohol consumption) and socio-demographic variables were used as independent variables. Depression severity was the strongest predictor for using mental health services. In contrast, risky alcohol consumption was not associated with an increased likelihood of using mental health services. After adjusting for need, the following groups were less likely to use (some of) the mental health services: males, young people, participants who (almost) work full-time, single/unmarried, non-Swiss people and those living in rural areas. Education and income were not significantly associated with the outcomes in the adjusted analyses. Some socio-demographic subgroups are less likely to use mental health services despite having the same need.

Biological and behavioral factors modify urinary arsenic metabolic profiles in a U.S. population.

PubMed

Hudgens, Edward E; Drobna, Zuzana; He, Bin; Le, X C; Styblo, Miroslav; Rogers, John; Thomas, David J

2016-05-26

Because some adverse health effects associated with chronic arsenic exposure may be mediated by methylated arsenicals, interindividual variation in capacity to convert inorganic arsenic into mono- and di-methylated metabolites may be an important determinant of risk associated with exposure to this metalloid. Hence, identifying biological and behavioral factors that modify an individual's capacity to methylate inorganic arsenic could provide insights into critical dose-response relations underlying adverse health effects. A total of 904 older adults (≥45 years old) in Churchill County, Nevada, who chronically used home tap water supplies containing up to 1850 μg of arsenic per liter provided urine and toenail samples for determination of total and speciated arsenic levels. Effects of biological factors (gender, age, body mass index) and behavioral factors (smoking, recent fish or shellfish consumption) on patterns of arsenicals in urine were evaluated with bivariate analyses and multivariate regression models. Relative contributions of inorganic, mono-, and di-methylated arsenic to total speciated arsenic in urine were unchanged over the range of concentrations of arsenic in home tap water supplies used by study participants. Gender predicted both absolute and relative amounts of arsenicals in urine. Age predicted levels of inorganic arsenic in urine and body mass index predicted relative levels of mono- and di-methylated arsenic in urine. Smoking predicted both absolute and relative levels of arsenicals in urine. Multivariate regression models were developed for both absolute and relative levels of arsenicals in urine. Concentration of arsenic in home tap water and estimated water consumption were strongly predictive of levels of arsenicals in urine as were smoking, body mass index, and gender. Relative contributions of arsenicals to urinary arsenic were not consistently predicted by concentrations of arsenic in drinking water supplies but were more consistently predicted by gender, body mass index, age, and smoking. These findings suggest that analyses of dose-response relations in arsenic-exposed populations should account for biological and behavioral factors that modify levels of inorganic and methylated arsenicals in urine. Evidence of significant effects of these factors on arsenic metabolism may also support mode of action studies in appropriate experimental models.

Investigating the relative importance of individual differences on the work-family interface and the moderating role of boundary preference for segmentation.

PubMed

Michel, Jesse S; Clark, Malissa A

2013-10-01

This study examines the relative importance of individual differences in relation to perceptions of work-family conflict and facilitation, as well as the moderating role of boundary preference for segmentation on these relationships. Relative importance analyses, based on a diverse sample of 380 employees from the USA, revealed that individual differences were consistently predictive of self-reported work-family conflict and facilitation. Conscientiousness, neuroticism, negative affect and core self-evaluations were consistently related to both directions of work-family conflict, whereas agreeableness predicted significant variance in family-to-work conflict only. Positive affect and core self-evaluations were consistently related to both directions of work-family facilitation, whereas agreeableness and neuroticism predicted significant variance in family-to-work facilitation only. Collectively, individual differences explained 25-28% of the variance in work-family conflict (primarily predicted by neuroticism and negative affect) and 11-18% of the variance in work-family facilitation (primarily predicted by positive affect and core self-evaluations). Moderated regression analyses showed that boundary preference for segmentation strengthened many of the relationships between individual differences and work-family conflict and facilitation. Implications for addressing the nature of work and family are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Estimating the concrete compressive strength using hard clustering and fuzzy clustering based regression techniques.

PubMed

Nagwani, Naresh Kumar; Deo, Shirish V

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

PubMed Central

Nagwani, Naresh Kumar; Deo, Shirish V.

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

PubMed Central

Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

The Influence of Subjective Social Status on Vulnerability to Postpartum Smoking Among Young Pregnant Women

PubMed Central

Reitzel, Lorraine R.; Vidrine, Jennifer I.; Li, Yisheng; Mullen, Patricia D.; Velasquez, Mary M.; Cinciripini, Paul M.; Cofta-Woerpel, Ludmila; Greisinger, Anthony; Wetter, David W.

2007-01-01

Objectives. Associations between subjective social status, a subjective measure of socioeconomic status, and predictors of risk for postpartum smoking were examined among 123 pregnant women (aged 18–24 years) who stopped smoking because of pregnancy. The goal was to identify how subjective social status might influence the risk for postpartum smoking and to elucidate targets for intervention. Methods. We used multiple regression equations to examine the predictive relations between subjective social status and tobacco dependence, self-rated likelihood of postpartum smoking, confidence, temptations, positive and negative affect, depression, stress, and social support. Adjusted analyses were also conducted with control for race/ethnicity, education, income, and whether participant had a partner or not (partner status). Results. In unadjusted and adjusted analyses, subjective social status predicted tobacco dependence, likelihood of postpartum smoking, confidence, temptations, positive affect, negative affect, and social support. Adjusted analyses predicting depression and stress approached significance. Conclusions. Among young pregnant women who quit smoking because of pregnancy, low subjective social status was associated with a constellation of characteristics indicative of increased vulnerability to postpartum smoking. Subjective social status provided unique information on risk for postpartum smoking over and above the effects of race/ethnicity, objective socioeconomic status, and partner status. PMID:17600249

Peer Influence Predicts Speeding Prevalence Among Teenage Drivers

PubMed Central

Ouimet, Marie Claude; Chen, Rusan; Klauer, Sheila G.; Lee, Suzanne E.; Wang, Jing; Dingus, Thomas A.

2012-01-01

Objective This research examined the psychosocial and personality predictors of observed speeding among young drivers. Method. Survey and driving data were collected from 42 newly-licensed teenage drivers during the first 18 months of licensure. Speeding (i.e., driving 10 mph over the speed limit; about 16 km/h) was assessed by comparing speed data collected with recording systems installed in participants’ vehicles with posted speed limits. Questionnaire data collected at baseline were used to predict speeding rates using random effects regression analyses. For mediation analysis, data collected at baseline and at 6, 12, and 18 months after licensure were used. Results. Speeding was correlated with elevated g-force event rates, including hard braking and turning (r = 0.335, p < 0.05), but not with crashes and near crashes (r = 0.227; ns). Speeding prevalence increased over time. In univariate analyses speeding was predicted by day vs. night trips, higher sensation seeking, substance use, tolerance of deviance, susceptibility to peer pressure, and number of risky friends. In multivariate analyses the number of risky friends was the only significant predictor of speeding. Perceived risk was a significant mediator of the association between speeding and risky friends. Conclusion. The findings support the contention that social norms may influence teenage speeding behavior and this relationship may operate through perceived risk. PMID:23206513

The Preoperative CT-Scan Can Help to Predict Postoperative Complications after Pancreatoduodenectomy

PubMed Central

Schröder, Femke F.; de Graaff, Feike; Bouman, Donald E.; Brusse-Keizer, Marjolein; Slump, Kees H.; Klaase, Joost M.

2015-01-01

After pancreatoduodenectomy, complication rates are up to 40%. To predict the risk of developing postoperative pancreatic fistula or severe complications, various factors were evaluated. 110 consecutive patients undergoing pancreatoduodenectomy at our institute between January 2012 and September 2014 with complete CT scan were retrospectively identified. Pre-, per-, and postoperative patients and pathological information were gathered. The CT-scans were analysed for the diameter of the pancreatic duct, attenuation of the pancreas, and the visceral fat area. All data was statistically analysed for predicting POPF and severe complications by univariate and multivariate logistic regression analyses. The POPF rate was 18%. The VFA measured at umbilicus (OR 1.01; 95% CI = 1.00–1.02; P = 0.011) was an independent predictor for POPF. The severe complications rate was 33%. Independent predictors were BMI (OR 1.24; 95% CI = 1.10–1.42; P = 0.001), ASA class III (OR 17.10; 95% CI = 1.60–182.88; P = 0.019), and mean HU (OR 0.98; 95% CI = 0.96–1.00; P = 0.024). In conclusion, VFA measured at the umbilicus seems to be the best predictor for POPF. BMI, ASA III, and the mean HU of the pancreatic body are independent predictors for severe complications following PD. PMID:26605340

Does affective organizational commitment and experience of meaning at work predict long-term sickness absence? An analysis of register-based outcomes using pooled data on 61,302 observations in four occupational groups.

PubMed

Clausen, Thomas; Burr, Hermann; Borg, Vilhelm

2014-02-01

To investigate whether experience of low meaning at work (MAW) and low affective organizational commitment (AOC) predicts long-term sickness absence (LTSA) for more than 3 consecutive weeks and whether this association is dependent on the occupational group. Survey data pooling 61,302 observations were fitted to the DREAM register containing information on payments of sickness absence compensation. Using multiadjusted Cox regression, observations were followed for an 18-month follow-up period to assess the risk for LTSA. Low levels of MAW and AOC significantly increased the risk for LTSA during follow-up. Subgroup analyses showed that associations were statistically significant for employees working with clients and office workers but not for employees working with customers and manual workers. Further analyses showed that associations between MAW and LTSA varied significantly across the four occupational groups. Meaning at work and affective organizational commitment significantly predict LTSA. Promoting MAW and AOC may contribute toward reducing LTSA in contemporary workplaces.

The Relationship Between Continuous Identity Disturbances, Negative Mood, and Suicidal Ideation.

PubMed

Sokol, Yosef; Eisenheim, Edouard

To examine the relationship between continuous identity and a measure of depression, anxiety, and stress as well as suicidal ideation using 2 validated measures of continuous identity. A total of 246 subjects recruited from the Amazon Mechanical Turk subject pool who completed a full survey in November 2014 were included in the analyses. Stress, anxiety, and depression severity were measured using the Depression, Anxiety, and Stress Scale. Continuous identity was measured with the Venn continuous identity task and the me/not me continuous identity task. Multiple regression analyses revealed continuous identity disturbances were significantly associated with depressed mood (R (2) = 0.37, P < .01). Continuous identity also predicted suicide severity, even after controlling for demographic factors, negative life events, and depressed mood. Additionally, predictive discriminant analysis revealed continuous identity, depression severity, and negative life events correctly classified 74.1% of participants into high and low suicide risk groups. Lack of continuous identity predicted both depression and suicidality severity. Integration of perceived identities may be a worthwhile goal for behavioral interventions aimed at reducing depressed mood and suicidality.

Academic and emotional functioning in early adolescence: longitudinal relations, patterns, and prediction by experience in middle school.

PubMed

Roeser, R W; Eccles, J S; Sameroff, A J

1998-01-01

Adopting a motivational perspective on adolescent development, these two companion studies examined the longitudinal relations between early adolescents' school motivation (competence beliefs and values), achievement, emotional functioning (depressive symptoms and anger), and middle school perceptions using both variable- and person-centered analytic techniques. Data were collected from 1041 adolescents and their parents at the beginning of seventh and the end of eight grade in middle school. Controlling for demographic factors, regression analyses in Study 1 showed reciprocal relations between school motivation and positive emotional functioning over time. Furthermore, adolescents' perceptions of the middle school learning environment (support for competence and autonomy, quality of relationships with teachers) predicted their eighth grade motivation, achievement, and emotional functioning after accounting for demographic and prior adjustment measures. Cluster analyses in Study 2 revealed several different patterns of school functioning and emotional functioning during seventh grade that were stable over 2 years and that were predictably related to adolescents' reports of their middle school environment. Discussion focuses on the developmental significance of schooling for multiple adjustment outcomes during adolescence.

Overgeneral autobiographical memory at baseline predicts depressive symptoms at follow-up in patients with first-episode depression.

PubMed

Liu, Yansong; Zhang, Fuquan; Wang, Zhiqiang; Cao, Leiming; Wang, Jun; Na, Aiguo; Sun, Yujun; Zhao, Xudong

2016-09-30

Previous studies have shown that overgeneral autobiographical memory (OGM) is a characteristic of depression. However, there are no studies to explore the association between baseline OGM and depressive symptoms at follow-up in patients with first-episode depression (FE). This study investigated whether baseline OGM predicts depressive symptoms at follow-up in patients with FE. We recruited 125 patients with FE. The participants were divided into remitted group and non-remitted group according to the severity of their depression at 12 months follow-up. The measures consisted of the 17-item Hamilton Depression Rating Scale, Ruminative Response Scale, and Autobiographical Memory Test. Hierarchical linear regression analyses and bootstrap mediation analyses were conducted. The results showed that non-remitted patients had more OGM at baseline. Baseline OGM predicted depressive symptoms at follow-up in patients with FE. Rumination mediated the relationship between baseline OGM and depressive symptoms at follow-up. Our findings highlight OGM as a vulnerability factor involved in the maintenance of depression in patients with FE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Respiratory morbidity of pattern and model makers exposed to wood, plastic, and metal products

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

Robins, T.G.; Haboubi, G.; Demers, R.Y.

Pattern and model makers are skilled tradespersons who may be exposed to hardwoods, softwoods, phenol-formaldehyde resin-impregnated woods, epoxy and polyester/styrene resin systems, and welding and metal-casting fumes. The relationship of respiratory symptoms (wheezing, chronic bronchitis, dyspnea) and pulmonary function (FVC% predicted, FEV1% predicted, FEV1/FVC% predicted) with interview-derived cumulative exposure estimates to specific workplace agents and to all work with wood, plastic, or metal products was investigated in 751 pattern and model makers in southeast Michigan. In stratified analyses and age- and smoking-adjusted linear and logistic regression models, measures of cumulative wood exposures were associated with decrements in pulmonary function andmore » dyspnea, but not with other symptoms. In similar analyses, measures of cumulative plastic exposures were associated with wheezing, chronic bronchitis, and dyspnea, but not with decrements in pulmonary function. Prior studies of exposure levels among pattern and model makers and of respiratory health effects of specific agents among other occupational groups support the plausibility of wood-related effects more strongly than that of plastic-related effects.« less

A model for predicting thermal properties of asphalt mixtures from their constituents

NASA Astrophysics Data System (ADS)

Keller, Merlin; Roche, Alexis; Lavielle, Marc

Numerous theoretical and experimental approaches have been developed to predict the effective thermal conductivity of composite materials such as polymers, foams, epoxies, soils and concrete. None of such models have been applied to asphalt concrete. This study attempts to develop a model to predict the thermal conductivity of asphalt concrete from its constituents that will contribute to the asphalt industry by reducing costs and saving time on laboratory testing. The necessity to do the laboratory testing would be no longer required when a mix for the pavement is created with desired thermal properties at the design stage by selecting correct constituents. This thesis investigated six existing predictive models for applicability to asphalt mixtures, and four standard mathematical techniques were used to develop a regression model to predict the effective thermal conductivity. The effective thermal conductivities of 81 asphalt specimens were used as the response variables, and the thermal conductivities and volume fractions of their constituents were used as the predictors. The conducted statistical analyses showed that the measured values of thermal conductivities of the mixtures are affected by the bitumen and aggregate content, but not by the air content. Contrarily, the predicted data for some investigated models are highly sensitive to air voids, but not to bitumen and/or aggregate content. Additionally, the comparison of the experimental with analytical data showed that none of the existing models gave satisfactory results; on the other hand, two regression models (Exponential 1* and Linear 3*) are promising for asphalt concrete.

The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses.

PubMed

Xie, Heping; Wang, Fuxing; Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan

2017-01-01

Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = -0.11, 95% CI = [-0.19, -0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = -0.70, 95% CI = [-1.02, -0.38], p < 0.001), as well as dtransfer for cueing (β = -0.60, 95% CI = [-0.92, -0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning.

The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses

PubMed Central

Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan

2017-01-01

Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = −0.11, 95% CI = [−0.19, −0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = −0.70, 95% CI = [−1.02, −0.38], p < 0.001), as well as dtransfer for cueing (β = −0.60, 95% CI = [−0.92, −0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning. PMID:28854205

Evaluation of risk factors for perforated peptic ulcer.

PubMed

Yamamoto, Kazuki; Takahashi, Osamu; Arioka, Hiroko; Kobayashi, Daiki

2018-02-15

The aim of this study was to evaluate the prediction factors for perforated peptic ulcer (PPU). At St. Luke's International Hospital in Tokyo, Japan, a case control study was performed between August 2004 and March 2016. All patients diagnosed with PPU were included. As control subjects, patients with age, sex and date of CT scan corresponding to those of the PPU subjects were included in the study at a proportion of 2 controls for every PPU subject. All data such as past medical histories, physical findings, and laboratory data were collected through chart reviews. Univariate analyses and multivariate analyses with logistic regression were conducted, and receiver operating characteristic curves (ROCs) were calculated to show validity. Sensitivity analyses were performed to confirm results using a stepwise method and conditional logistic regression. A total of 408 patients were included in this study; 136 were a group of patients with PPU, and 272 were a control group. Univariate analysis showed statistical significance in many categories. Four different models of multivariate analyses were conducted, and significant differences were found for muscular defense and a history of peptic ulcer disease (PUD) in all models. The conditional forced-entry analysis of muscular defense showed an odds ratio (OR) of 23.8 (95% confidence interval [CI]: 5.70-100.0), and the analysis of PUD history showed an OR of 6.40 (95% CI: 1.13-36.2). The sensitivity analysis showed consistent results, with an OR of 23.8-366.2 for muscular defense and an OR of 3.67-7.81 for PUD history. The area under the curve (AUC) of all models was high enough to confirm the results. However, anticoagulants, known risk factors for PUD, did not increase the risk for PPU in our study. The conditional forced-entry analysis of anticoagulant use showed an OR of 0.85 (95% CI: 0.03-22.3). The evaluation of prediction factors and development of a prediction rule for PPU may help our decision making in performing a CT scan for patients with acute abdominal pain.

A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Bridge, Thomas M.; Amaya, Max A.

2014-01-01

A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper.

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

PubMed Central

Gravlee, Clarence C.; Mulligan, Connie J.

2012-01-01

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

Birth weight and neonatal adiposity prediction using fractional limb volume obtained with 3D ultrasound.

PubMed

O'Connor, Clare; O'Higgins, Amy; Doolan, Anne; Segurado, Ricardo; Stuart, Bernard; Turner, Michael J; Kennelly, Máireád M

2014-01-01

The objective of this investigation was to study fetal thigh volume throughout gestation and explore its correlation with birth weight and neonatal body composition. This novel technique may improve birth weight prediction and lead to improved detection rates for fetal growth restriction. Fractional thigh volume (TVol) using 3D ultrasound, fetal biometry and soft tissue thickness were studied longitudinally in 42 mother-infant pairs. The percentages of neonatal body fat, fat mass and fat-free mass were determined using air displacement plethysmography. Correlation and linear regression analyses were performed. Linear regression analysis showed an association between TVol and birth weight. TVol at 33 weeks was also associated with neonatal fat-free mass. There was no correlation between TVol and neonatal fat mass. Abdominal circumference, estimated fetal weight (EFW) and EFW centile showed consistent correlations with birth weight. Thigh volume demonstrated an additional independent contribution to birth weight prediction when added to the EFW centile from the 38-week scan (p = 0.03). Fractional TVol performed at 33 weeks gestation is correlated with birth weight and neonatal lean body mass. This screening test may highlight those at risk of fetal growth restriction or macrosomia.

Three-dimensional prediction of the human eyeball and canthi for craniofacial reconstruction using cone-beam computed tomography.

PubMed

Kim, Sang-Rok; Lee, Kyung-Min; Cho, Jin-Hyoung; Hwang, Hyeon-Shik

2016-04-01

An anatomical relationship between the hard and soft tissues of the face is mandatory for facial reconstruction. The purpose of this study was to investigate the positions of the eyeball and canthi three-dimensionally from the relationships between the facial hard and soft tissues using cone-beam computed tomography (CBCT). CBCT scan data of 100 living subjects were used to obtain the measurements of facial hard and soft tissues. Stepwise multiple regression analyses were carried out using the hard tissue measurements in the orbit, nasal bone, nasal cavity and maxillary canine to predict the most probable positions of the eyeball and canthi within the orbit. Orbital width, orbital height, and orbital depth were strong predictors of the eyeball and canthi position. Intercanine width was also a predictor of the mediolateral position of the eyeball. Statistically significant regression models for the positions of the eyeball and canthi could be derived from the measurements of orbit and maxillary canine. These results suggest that CBCT data can be useful in predicting the positions of the eyeball and canthi three-dimensionally. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

2012-08-01

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

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.

Predicting Homelessness among Emerging Adults Aging Out of Foster Care.

PubMed

Shah, Melissa Ford; Liu, Qinghua; Mark Eddy, J; Barkan, Susan; Marshall, David; Mancuso, David; Lucenko, Barbara; Huber, Alice

2017-09-01

This study examines risk and protective factors associated with experiencing homelessness in the year after "aging out" of foster care. Using a state-level integrated administrative database, we identified 1,202 emerging adults in Washington State who exited foster care between July 2010 and June 2012. Initial bivariate analyses were conducted to assess the association between candidate predictive factors and an indicator of homelessness in a 12-month follow-up period. After deploying a stepwise regression process, the final logistic regression model included 15 predictive factors. Youth who were parents, who had recently experienced housing instability, or who were African American had approximately twice the odds of experiencing homelessness in the year after exiting foster care. In addition, youth who had experienced disrupted adoptions, had multiple foster care placements (especially in congregate care settings), or had been involved with the juvenile justice system were more likely to become homeless. In contrast, youth were less likely to experience homelessness if they had ever been placed with a relative while in foster care or had a high cumulative grade point average relative to their peers. © Society for Community Research and Action 2016.

Empirical and semi-analytical models for predicting peak outflows caused by embankment dam failures

NASA Astrophysics Data System (ADS)

Wang, Bo; Chen, Yunliang; Wu, Chao; Peng, Yong; Song, Jiajun; Liu, Wenjun; Liu, Xin

2018-07-01

Prediction of peak discharge of floods has attracted great attention for researchers and engineers. In present study, nine typical nonlinear mathematical models are established based on database of 40 historical dam failures. The first eight models that were developed with a series of regression analyses are purely empirical, while the last one is a semi-analytical approach that was derived from an analytical solution of dam-break floods in a trapezoidal channel. Water depth above breach invert (Hw), volume of water stored above breach invert (Vw), embankment length (El), and average embankment width (Ew) are used as independent variables to develop empirical formulas of estimating the peak outflow from breached embankment dams. It is indicated from the multiple regression analysis that a function using the former two variables (i.e., Hw and Vw) produce considerably more accurate results than that using latter two variables (i.e., El and Ew). It is shown that the semi-analytical approach works best in terms of both prediction accuracy and uncertainty, and the established empirical models produce considerably reasonable results except the model only using El. Moreover, present models have been compared with other models available in literature for estimating peak discharge.

Influence of systemic bone mineral density on atlantoaxial subluxation in patients with rheumatoid arthritis.

PubMed

Han, M H; Ryu, J I; Kim, C H; Kim, J M; Cheong, J H; Bak, K H; Chun, H J

2017-06-01

Osteopenia and osteoporosis were independent predictive factors for higher atlantoaxial subluxation occurrence in patients with lower body mass index. Our findings suggest that patients with rheumatoid arthritis with osteopenia or osteoporosis, particularly those with lower body mass index (BMI), should be screened regularly to determine the status of their cervical spines. Cervical spine involvement in rheumatoid arthritis (RA) patients may cause serious adverse effects on quality of life and overall health. This study aimed to evaluate the association between atlantodental interval (ADI), atlantoaxial subluxation (AAS), and systemic bone mineral density (BMD) based on BMI variations among established patients with RA. The ADI was transformed to the natural log scale to normalize distributions for all analyses. Multivariable linear regression analyses were used to identify independent predictive factors for ADI based on each BMD classification. Multivariate Cox regression analyses were also performed to identify independent predictive factors for the risk of AAS, which were classified by tertile groups of BMI. A total of 1220 patients with RA who had undergone at least one or more cervical radiography and BMD assessments were identified and enrolled. We found that the association between BMD and ADI (β, -0.029; 95% CI, -0.059 to 0.002; p = 0.070) fell short of achieving statistical significance. However, the ADI showed a 3.6% decrease per 1 BMI increase in the osteoporosis group (β, -0.036; 95% CI, -0.061 to -0.011; p = 0.004). The osteopenia and osteoporosis groups showed about a 1.5-fold and a 1.8-fold increased risk of AAS occurrence among the first tertile of the BMI group. Our study showed a possible association between lower BMD and AAS occurrence in patients with RA with lower BMI. Further studies are needed to confirm our findings.

Intraoperative factors associated with delayed recovery of liver function after hepatectomy: analysis of 1969 living donors.

PubMed

Choi, S-S; Cho, S-S; Ha, T-Y; Hwang, S; Lee, S-G; Kim, Y-K

2016-02-01

The safety of healthy living donors who are undergoing hepatic resection is a primary concern. We aimed to identify intraoperative anaesthetic and surgical factors associated with delayed recovery of liver function after hepatectomy in living donors. We retrospectively analysed 1969 living donors who underwent hepatectomy for living donor liver transplantation. Delayed recovery of hepatic function was defined by increases in international normalised ratio of prothrombin time and concomitant hyperbilirubinaemia on or after post-operative day 5. Univariate and multivariate logistic regression analyses were performed to determine the factors associated with delayed recovery of hepatic function after living donor hepatectomy. Delayed recovery of liver function after donor hepatectomy was observed in 213 (10.8%) donors. Univariate logistic regression analysis showed that sevoflurane anaesthesia, synthetic colloid, donor age, body mass index, fatty change and remnant liver volume were significant factors for prediction of delayed recovery of hepatic function. Multivariate logistic regression analysis showed that independent factors significantly associated with delayed recovery of liver function after donor hepatectomy were sevoflurane anaesthesia (odds ratio = 3.514, P < 0.001), synthetic colloid (odds ratio = 1.045, P = 0.033), donor age (odds ratio = 0.970, P = 0.003), female gender (odds ratio = 1.512, P = 0.014) and remnant liver volume (odds ratio = 0.963, P < 0.001). Anaesthesia with sevoflurane was an independent factor in predicting delayed recovery of hepatic function after donor hepatectomy. Although synthetic colloid may be associated with delayed recovery of hepatic function after donor hepatectomy, further study is required. These results can provide useful information on perioperative management of living liver donors. © 2015 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Ventral Striatum Functional Connectivity as a Predictor of Adolescent Depressive Disorder in a Longitudinal Community-Based Sample.

PubMed

Pan, Pedro Mario; Sato, João R; Salum, Giovanni A; Rohde, Luis A; Gadelha, Ary; Zugman, Andre; Mari, Jair; Jackowski, Andrea; Picon, Felipe; Miguel, Eurípedes C; Pine, Daniel S; Leibenluft, Ellen; Bressan, Rodrigo A; Stringaris, Argyris

2017-11-01

Previous studies have implicated aberrant reward processing in the pathogenesis of adolescent depression. However, no study has used functional connectivity within a distributed reward network, assessed using resting-state functional MRI (fMRI), to predict the onset of depression in adolescents. This study used reward network-based functional connectivity at baseline to predict depressive disorder at follow-up in a community sample of adolescents. A total of 637 children 6-12 years old underwent resting-state fMRI. Discovery and replication analyses tested intrinsic functional connectivity (iFC) among nodes of a putative reward network. Logistic regression tested whether striatal node strength, a measure of reward-related iFC, predicted onset of a depressive disorder at 3-year follow-up. Further analyses investigated the specificity of this prediction. Increased left ventral striatum node strength predicted increased risk for future depressive disorder (odds ratio=1.54, 95% CI=1.09-2.18), even after excluding participants who had depressive disorders at baseline (odds ratio=1.52, 95% CI=1.05-2.20). Among 11 reward-network nodes, only the left ventral striatum significantly predicted depression. Striatal node strength did not predict other common adolescent psychopathology, such as anxiety, attention deficit hyperactivity disorder, and substance use. Aberrant ventral striatum functional connectivity specifically predicts future risk for depressive disorder. This finding further emphasizes the need to understand how brain reward networks contribute to youth depression.

Personal and lifestyle characteristics predictive of the consumption of fast foods in Australia.

PubMed

Mohr, Philip; Wilson, Carlene; Dunn, Kirsten; Brindal, Emily; Wittert, Gary

2007-12-01

To identify key predictors of fast-food consumption from a range of demographic, attitudinal, personality and lifestyle variables. We analysed data from a nationwide survey (n = 20 527) conducted in Australia by Nielsen Media Research. Items assessing frequency of fast-food consumption at (1) eat in and (2) take away were regressed onto 12 demographic, seven media consumption, and 23 psychological and lifestyle variables, the latter derived from factor analysis of responses to 107 attitudinal and behavioural items. Stepwise multiple regression analyses explained 29.6% of the variance for frequency of take-away and 9.6% of the variance for frequency of eat-in consumption of fast foods. Predictors of more frequent consumption of fast food at take away (and, to a lesser extent, eat in) included lower age - especially under 45 years, relative indifference to health consequences of behaviour, greater household income, more exposure to advertising, greater receptiveness to advertising, lesser allocation of time for eating, and greater allocation of time to home entertainment. There were no effects for occupational status or education level. The effects for age suggest that fast-food take-away consumption is associated with a general cultural shift in eating practices; individual differences in attitudinal and lifestyle characteristics constitute additional, cumulative, predictive factors. The role of advertising and the reasons for the lesser explanatory value of the eat-in models are important targets for further research.

Using the simplified case mix tool (sCMT) to identify cost in special care dental services to support commissioning.

PubMed

Duane, B G; Freeman, R; Richards, D; Crosbie, S; Patel, P; White, S; Humphris, G

2017-03-01

To commission dental services for vulnerable (special care) patient groups effectively, consistently and fairly an evidence base is needed of the costs involved. The simplified Case Mixed Tool (sCMT) can assess treatment mode complexity for these patient groups. To determine if the sCMT can be used to identify costs of service provision. Patients (n=495) attending the Sussex Community NHS Trust Special Care Dental Service for care were assessed using the sCMT. sCMT score and costs (staffing, laboratory fees, etc.) besides patient age, whether a new patient and use of general anaesthetic/intravenous sedation. Statistical analysis (adjusted linear regression modelling) compared sCMT score and costs then sensitivity analyses of the costings to age, being a new patient and sedation use were undertaken. Regression tables were produced to present estimates of service costs. Costs increased with sCMT total scale and single item values in a predictable manner in all analyses except for 'cooperation'. Costs increased with the use of IV sedation; with each rising level of the sCMT, and with complexity in every sCMT category, except cooperation. Costs increased with increase in complexity of treatment mode as measured by sCMT scores. Measures such as the sCMT can provide predictions of the resource allocations required when commissioning special care dental services. Copyright© 2017 Dennis Barber Ltd.

Adjuvant radiotherapy after breast conserving surgery - a comparative effectiveness research study.

PubMed

Corradini, Stefanie; Niyazi, Maximilian; Niemoeller, Olivier M; Li, Minglun; Roeder, Falk; Eckel, Renate; Schubert-Fritschle, Gabriele; Scheithauer, Heike R; Harbeck, Nadia; Engel, Jutta; Belka, Claus

2015-01-01

The purpose of this retrospective outcome study was to validate the effectiveness of postoperative radiotherapy in breast conserving therapy (BCT) and to evaluate possible causes for omission of radiotherapy after breast conserving surgery (BCS) in a non-trial population. Data were provided by the population-based Munich Cancer Registry. The study included epidemiological data of 30.811 patients diagnosed with breast cancer from 1998 to 2012. The effect of omitting radiotherapy was analysed using Kaplan-Meier-estimates and Cox proportional hazard regression. Variables predicting omission of radiotherapy were analysed using multivariate logistic regression. Use of postoperative radiotherapy after BCS was associated with significant improvements in local control and survival. 10-year loco-regional recurrence-free-survival was 90.8% with postoperative radiotherapy vs. 77.6% with surgery alone (p<0.001). 10-year overall survival rates were 55.2% with surgery alone vs. 82.2% following postoperative radiotherapy (p<0.001). Variables predicting omission of postoperative radiotherapy included advanced age (women ⩾80 years; OR: 0.082; 95% CI: 0.071-0.094, p<0.001). This study shows a decrease in local control and a survival disadvantage if postoperative radiotherapy after breast conserving surgery is omitted in an unselected cohort of primary breast cancer patients. Due to its epidemiological nature, it cannot answer the question in whom postoperative radiotherapy can be safely omitted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The influence of smoking imagery on the smoking intentions of young people: testing a media interpretation model.

PubMed

McCool, Judith P; Cameron, Linda D; Petrie, Keith J

2005-06-01

To assess a theoretical model of adolescents' exposure to films, perceptions of smoking imagery in film, and smoking intentions. A structured questionnaire was completed by 3041 Year 8 (aged 12 years) and Year 12 (aged 16 years) students from 25 schools in Auckland, New Zealand. The survey assessed the relationships among exposure to films, attitudes about smoking imagery, perceptions of smoking prevalence and its acceptability, and expectations of smoking in the future. Measures included exposure to films, perceived pervasiveness of, and nonchalant attitudes about smoking imagery, identification of positive smoker stereotypes in films, perceived smoking prevalence, judgment of smoking acceptability, and smoking expectations. Path analytic techniques, using multiple regression analyses, were used to test the pattern of associations identified by the media interpretation model. Hierarchical regression analyses revealed that film exposure predicted higher levels of perceived smoking prevalence, perceived imagery pervasiveness, and nonchalant attitudes about smoking imagery. Nonchalant attitudes, identification of positive smoker stereotypes, and perceived smoking prevalence predicted judgments of smoking acceptability. Acceptability judgments, identification of positive stereotypes, and perceived smoking prevalence were all positively associated with smoking expectations. The media interpretation model accounted for 24% of the variance in smoking expectations within the total sample. Smoking imagery in film may play a role in the development of smoking intentions through inflating the perception of smoking prevalence and presenting socially attractive images.

Measures of health, fitness, and functional movement among firefighter recruits.

PubMed

Cornell, David J; Gnacinski, Stacy L; Zamzow, Aaron; Mims, Jason; Ebersole, Kyle T

2017-06-01

The purpose of this study was to examine the associations between various health and fitness measures and Functional Movement Screen™ (FMS™) scores among 78 firefighter recruits. Relationships between FMS™ scores and age, body mass index (BMI), sit and reach (S&R) distance, estimated maximal aerobic capacity (V˙ O2max ), estimated one-repetition maximum squat (1RM-Squat max ), and plank endurance (%Plank max ) were examined. Total FMS™ scores were significantly correlated with BMI (r = -0.231, p = 0.042), estimated 1RM-Squat max (r = 0.302, p = 0.007), and %Plank max (r = 0.320, p = 0.004). Multiple regression analyses indicated that this combination of predictors significantly predicted (F(3, 74) = 5.043, p = 0.003) Total FMS™ score outcomes and accounted for 17% of the total variance (R 2  = 0.170). In addition, logistic regression analyses indicated that estimated 1RM-Squat max also significantly predicted (χ 2  = 6.662, df = 1, p = 0.010) FMS™ group membership (≤14 or ≥15). These results suggest that the health and fitness measures of obesity (BMI), bilateral lower extremity strength (estimated 1RM-Squat max ), and core muscular endurance (%Plank max ) are significantly associated with functional movement patterns among firefighter recruits. Consequently, injury prevention programs implemented among firefighter recruits should target these aspects of health and fitness.

Severity of specific language impairment predicts delayed development in number skills

PubMed Central

Durkin, Kevin; Mok, Pearl L. H.; Conti-Ramsden, Gina

2013-01-01

The extent to which mathematical development is dependent upon language is controversial. This longitudinal study investigates the role of language ability in children's development of number skills. Participants were 229 children with specific language impairment (SLI) who were assessed initially at age 7 and again 1 year later. All participants completed measures of psycholinguistic development (expressive and receptive), performance IQ, and the Basic Number Skills subtest of the British Ability Scales. Number skills data for this sample were compared with normative population data. Consistent with predictions that language impairment would impact on numerical development, average standard scores were more than 1 SD below the population mean at both ages. Although the children showed improvements in raw scores at the second wave of the study, the discrepancy between their scores and the population data nonetheless increased over time. Regression analyses showed that, after controlling for the effect of PIQ, language skills explained an additional 19 and 17% of the variance in number skills for ages 7 and 8, respectively. Furthermore, logistic regression analyses revealed that less improvement in the child's language ability over the course of the year was associated with a greater odds of a drop in performance in basic number skills from 7 to 8 years. The results are discussed in relation to the interaction of linguistic and cognitive factors in numerical development and the implications for mathematical education. PMID:24027548

Association between childhood and adult attention deficit hyperactivity disorder symptoms in Korean young adults with Internet addiction.

PubMed

Kim, DongIll; Lee, Deokjong; Lee, Junghan; Namkoong, Kee; Jung, Young-Chul

2017-09-01

Background and aims Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric comorbidities of Internet addiction (IA); however, the possible mechanisms that contribute to this high comorbidity are still under debate. This study aims to analyze these possible mechanisms by comparing the effect of IA severity and childhood ADHD on inattention, hyperactivity, and impulsivity in young adults with IA. We hypothesized that IA might have associations with ADHD-like cognitive and behavior symptoms aside from childhood ADHD. Methods Study participants consisted of 61 young male adults. Participants were administered a structured interview. The severity of IA, childhood and current ADHD symptoms, and psychiatry comorbid symptoms were assessed through self-rating scales. The associations between the severity of IA and ADHD symptoms were examined through hierarchical regression analyses. Results Hierarchical regression analyses showed that the severity of IA significantly predicted most dimensions of ADHD symptoms. By contrast, childhood ADHD predicted only one dimension. Discussion The high comorbidity of inattention and hyperactivity symptoms in IA should not solely be accounted by an independent ADHD disorder but should consider the possibility of cognitive symptoms related to IA. Functional and structural brain abnormalities associated with excessive and pathologic Internet usage might be related to these ADHD-like symptoms. Conclusion Inattention and hyperactivity in young adults with IA are more significantly associated with the severity of IA than that of childhood ADHD.

Techniques for estimating monthly mean streamflow at gaged sites and monthly streamflow duration characteristics at ungaged sites in central Nevada

USGS Publications Warehouse

Hess, G.W.; Bohman, L.R.

1996-01-01

Techniques for estimating monthly mean streamflow at gaged sites and monthly streamflow duration characteristics at ungaged sites in central Nevada were developed using streamflow records at six gaged sites and basin physical and climatic characteristics. Streamflow data at gaged sites were related by regression techniques to concurrent flows at nearby gaging stations so that monthly mean streamflows for periods of missing or no record can be estimated for gaged sites in central Nevada. The standard error of estimate for relations at these sites ranged from 12 to 196 percent. Also, monthly streamflow data for selected percent exceedence levels were used in regression analyses with basin and climatic variables to determine relations for ungaged basins for annual and monthly percent exceedence levels. Analyses indicate that the drainage area and percent of drainage area at altitudes greater than 10,000 feet are the most significant variables. For the annual percent exceedence, the standard error of estimate of the relations for ungaged sites ranged from 51 to 96 percent and standard error of prediction for ungaged sites ranged from 96 to 249 percent. For the monthly percent exceedence values, the standard error of estimate of the relations ranged from 31 to 168 percent, and the standard error of prediction ranged from 115 to 3,124 percent. Reliability and limitations of the estimating methods are described.

Sleep problems and suicide attempts among adolescents: a case-control study.

PubMed

Koyawala, Neel; Stevens, Jack; McBee-Strayer, Sandra M; Cannon, Elizabeth A; Bridge, Jeffrey A

2015-01-01

This study used a case-control design to compare sleep disturbances in 40 adolescents who attempted suicide with 40 never-suicidal adolescents. Using hierarchical logistic regression analyses, we found that self-reported nighttime awakenings were significantly associated with attempted suicide, after controlling for antidepressant use, antipsychotic use, affective problems, and being bullied. In a separate regression analysis, the parent-reported total sleep problems score also predicted suicide attempt status, controlling for key covariates. No associations were found between suicide attempts and other distinct sleep problems, including falling asleep at bedtime, sleeping a lot during the day, trouble waking up in the morning, sleep duration, and parent-reported nightmares. Clinicians should be aware of sleep problems as potential risk factors for suicide attempts for adolescents.

The relationship between severity of violence in the home and dating violence.

PubMed

Sims, Eva Nowakowski; Dodd, Virginia J Noland; Tejeda, Manuel J

2008-01-01

This study used propositions from the social learning theory to explore the effects of the combined influences of child maltreatment, childhood witness to parental violence, sibling violence, and gender on dating violence perpetration using a modified version of the Conflict Tactics Scale 2 (CTS2). A weighted scoring method was utilized to determine how severity of violence in the home impacts dating violence perpetration. Bivariate correlations and linear regression models indicate significant associations between child maltreatment, sibling violence perpetration, childhood witness to parental violence, gender, and subsequent dating violence perpetration. Multiple regression analyses indicate that for men, history of severe violence victimization (i.e., child maltreatment and childhood witness to parental violence) and severe perpetration (sibling violence) significantly predict dating violence perpetration.

Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

PubMed

Hunter, A; Kennedy, L; Henry, J; Ferguson, I

2000-05-01

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.

Within-person variation in security of attachment: a self-determination theory perspective on attachment, need fulfillment, and well-being.

PubMed

La Guardia, J G; Ryan, R M; Couchman, C E; Deci, E L

2000-09-01

Attachment research has traditionally focused on individual differences in global patterns of attachment to important others. The current research instead focuses primarily on within-person variability in attachments across relational partners. It was predicted that within-person variability would be substantial, even among primary attachment figures of mother, father, romantic partner, and best friend. The prediction was supported in three studies. Furthermore, in line with self-determination theory, multilevel modeling and regression analyses showed that, at the relationship level, individuals' experience of fulfillment of the basic needs for autonomy, competence, and relatedness positively predicted overall attachment security, model of self, and model of other. Relations of both attachment and need satisfaction to well-being were also explored.

Relationship between boys' normative beliefs about aggression and their physical, verbal, and indirect aggressive behaviors.

PubMed

Lim, Si Huan; Ang, Rebecca P

2009-01-01

This study examined the contribution of general normative beliefs about aggression and specific normative beliefs about retaliatory aggression in predicting physical, verbal, and indirect aggressive behaviors. Two hundred and forty-nine Grade 4 and Grade 5 boys completed the Normative Beliefs about Aggression Scale (NOBAGS) and provided self-reports on the frequency of their physical, verbal, and indirect aggressive behaviors. A series of hierarchical multiple regression analyses revealed that general normative beliefs about aggression contributed significantly in predicting all three types of aggressive behaviors. When general normative beliefs about aggression were controlled for, specific normative beliefs about retaliatory aggression against males but not specific normative beliefs about retaliatory aggression against females, contributed significantly to predict physical, verbal, and indirect aggressive behaviors. Implications for intervention programs are discussed.

Brain Natriuretic Hormone Predicts Stress Induced Alterations in Diastolic Function

PubMed Central

Choksy, Pratik; Davis, Harry C.; Januzzi, James; Thayer, Julian; Harshfield, Gregory; Robinson, Vincent JB; Kapuku, Gaston K.

2015-01-01

Background Mental stress (MS) reduces diastolic function (DF) and may lead to congestive heart failure with preserved systolic function. Whether brain natriuretic hormone (BNP) mediates the relationship of MS with DF is unknown. Method and Results 160 individuals aged 30 to 50 years underwent 2 hour protocol of 40 minutes rest, videogame stressor and recovery. Hemodynamics, pro-BNP samples and DF indices were obtained throughout the protocol. Separate regression analyses were conducted using rest and stress E/A, E’ and E/E’ as dependent variables. Predictor variables were entered into the stepwise regression models in a hierarchical fashion. At the first level age, sex, race, height, BMI, pro-BNP, and LVM were permitted to enter the models. The second level consisted of SBP, DBP and HR. The final level contained cross-product terms of race by SBP, DBP and HR. E/A ratio was lower during stress compared to rest, and recovery (p<0.01). Resting E/A ratio was predicted by a regression model of age (−.31), pro-BNP (.16), HR (−.40) and DBP (−.23) with an R2 = .33. Stress E/A ratio was predicted by age (−.24), pro-BNP (.08), HR (−.38), and SBP (−.21), total R2 = .22. Resting E’ model consisted of age (−.22), pro-BNP (.26), DBP (−.27) and LVM (−.15) with an R2 = .29. Stress E’ was predicted by age (−.18), pro-BNP (.35) and LVM (−.18) with an R2 = .18. Resting E/E’ was predicted by race (.17, B>W) and DBP (.24) with an R2 = .10. Stress E/E’ consisted of pro-BNP (−.36), height (−.26) and HR (−.21) with R2 = .15. Conclusion pro-BNP predicts both resting and stress DF suggesting that lower BNP during MS may be a maker of diastolic dysfunction in apparently healthy individuals. PMID:24841419

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey.

PubMed

Erdogan, Saffet

2009-10-01

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey. Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P<0.05 with spatial autocorrelation analyses. Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P<0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R(2) values. Geographically weighted regression provided values of 0.89-0.99 adjusted R(2) for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions. Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.

Impulsivity facets’ predictive relations with DSM-5 PTSD symptom clusters

PubMed Central

Roley, Michelle E.; Contractor, Ateka A.; Weiss, Nicole H.; Armour, Cherie; Elhai, Jon D.

2017-01-01

Objective Posttraumatic Stress Disorder (PTSD) has a well-established theoretical and empirical relation with impulsivity. Prior research has not used a multidimensional approach for measuring both PTSD and impulsivity constructs when assessing their relationship. Method The current study assessed the unique relationship of impulsivity facets on PTSD symptom clusters among a non-clinical sample of 412 trauma-exposed adults. Results Linear regression analyses revealed that impulsivity facets best accounted for PTSD’s arousal symptoms. The negative urgency facet of impulsivity was most predictive, as it was associated with all of PTSD’s symptom clusters. Sensation seeking did not predict PTSD’s intrusion symptoms, but did predict the other symptom clusters of PTSD. Lack of perseverance only predicted intrusion symptoms, while lack of premeditation only predicted PTSD’s mood/cognition symptoms. Conclusions Results extend theoretical and empirical research on the impulsivity-PTSD relationship, suggesting that impulsivity facets may serve as both risk and protective factors for PTSD symptoms. PMID:27243571

Impulsivity facets' predictive relations with DSM-5 PTSD symptom clusters.

PubMed

Roley, Michelle E; Contractor, Ateka A; Weiss, Nicole H; Armour, Cherie; Elhai, Jon D

2017-01-01

Posttraumatic stress disorder (PTSD) has a well-established theoretical and empirical relation with impulsivity. Prior research has not used a multidimensional approach for measuring both PTSD and impulsivity constructs when assessing their relationship. The current study assessed the unique relationship of impulsivity facets on PTSD symptom clusters among a nonclinical sample of 412 trauma-exposed adults. Linear regression analyses revealed that impulsivity facets best accounted for PTSD's arousal symptoms. The negative urgency facet of impulsivity was most predictive, because it was associated with all of PTSD's symptom clusters. Sensation seeking did not predict PTSD's intrusion symptoms, but did predict the other symptom clusters of PTSD. Lack of perseverance only predicted intrusion symptoms, while lack of premeditation only predicted PTSD's mood/cognition symptoms. Results extend theoretical and empirical research on the impulsivity-PTSD relationship, suggesting that impulsivity facets may serve as both risk and protective factors for PTSD symptoms. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Genetic improvement of mastitis resistance: validation of somatic cell score and clinical mastitis as selection criteria.

PubMed

Odegård, J; Klemetsdal, G; Heringstad, B

2003-12-01

Mean daughter deviations for clinical mastitis among second-crop daughters were regressed on predicted transmitting abilities for clinical mastitis and lactation mean somatic cell score in first-crop daughters to validate the predictive ability of these traits as selection criteria for reduced incidence of clinical mastitis. A total of 321 sires had 684,897 second-crop daughters, while predicted transmitting abilities were calculated for 2159 sires, based on 495,681 records of first-crop daughters. Predictive ability, as a measure of efficiency of selection, was 23 to 43% higher for clinical mastitis than for lactation mean somatic cell score. Compared to single-trait selection, predictive ability improved 8 to 13% from utilizing information on both traits. The relative weight that should be assigned to standardized predicted transmitting abilities from univariate genetic analyses were 60 to 67% for clinical mastitis and 33 to 40% for lactation mean somatic cell score. No significant nonlinear genetic relationship between the two traits was found.

Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.

PubMed

Lien, Tonje G; Borgan, Ørnulf; Reppe, Sjur; Gautvik, Kaare; Glad, Ingrid Kristine

2018-03-07

Using high-dimensional penalized regression we studied genome-wide DNA-methylation in bone biopsies of 80 postmenopausal women in relation to their bone mineral density (BMD). The women showed BMD varying from severely osteoporotic to normal. Global gene expression data from the same individuals was available, and since DNA-methylation often affects gene expression, the overall aim of this paper was to include both of these omics data sets into an integrated analysis. The classical penalized regression uses one penalty, but we incorporated individual penalties for each of the DNA-methylation sites. These individual penalties were guided by the strength of association between DNA-methylations and gene transcript levels. DNA-methylations that were highly associated to one or more transcripts got lower penalties and were therefore favored compared to DNA-methylations showing less association to expression. Because of the complex pathways and interactions among genes, we investigated both the association between DNA-methylations and their corresponding cis gene, as well as the association between DNA-methylations and trans-located genes. Two integrating penalized methods were used: first, an adaptive group-regularized ridge regression, and secondly, variable selection was performed through a modified version of the weighted lasso. When information from gene expressions was integrated, predictive performance was considerably improved, in terms of predictive mean square error, compared to classical penalized regression without data integration. We found a 14.7% improvement in the ridge regression case and a 17% improvement for the lasso case. Our version of the weighted lasso with data integration found a list of 22 interesting methylation sites. Several corresponded to genes that are known to be important in bone formation. Using BMD as response and these 22 methylation sites as covariates, least square regression analyses resulted in R 2 =0.726, comparable to an average R 2 =0.438 for 10000 randomly selected groups of DNA-methylations with group size 22. Two recent types of penalized regression methods were adapted to integrate DNA-methylation and their association to gene expression in the analysis of bone mineral density. In both cases predictions clearly benefit from including the additional information on gene expressions.

Effects of Psychological and Social Work Factors on Self-Reported Sleep Disturbance and Difficulties Initiating Sleep.

PubMed

Vleeshouwers, Jolien; Knardahl, Stein; Christensen, Jan Olav

2016-04-01

This prospective cohort study examined previously underexplored relations between psychological/social work factors and troubled sleep in order to provide practical information about specific, modifiable factors at work. A comprehensive evaluation of a range of psychological/social work factors was obtained by several designs; i.e., cross-sectional analyses at baseline and follow-up, prospective analyses with baseline predictors (T1), prospective analyses with average exposure across waves as predictor ([T1 + T2] / 2), and prospective analyses with change in exposure from baseline to follow-up as predictor. Participants consisted of a sample of Norwegian employees from a broad spectrum of occupations, who completed a questionnaire at two points in time, approximately two years apart. Cross-sectional analyses at T1 comprised 7,459 participants, cross-sectional analyses at T2 included 6,688 participants. Prospective analyses comprised a sample 5,070 of participants who responded at both T1 and T2. Univariable and multivariable ordinal logistic regressions were performed. Thirteen psychological/social work factors and two aspects of troubled sleep, namely difficulties initiating sleep and disturbed sleep, were studied. Ordinal logistic regressions revealed statistically significant associations for all psychological and social work factors in at least one of the analyses. Psychological and social work factors predicted sleep problems in the short term as well as the long term. All work factors investigated showed statistically significant associations with both sleep items, however quantitative job demands, decision control, role conflict, and support from superior were the most robust predictors and may therefore be suitable targets of interventions aimed at improving employee sleep. © 2016 Associated Professional Sleep Societies, LLC.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Donating blood and organs: using an extended theory of planned behavior perspective to identify similarities and differences in individual motivations to donate.

PubMed

Hyde, Melissa K; Knowles, Simon R; White, Katherine M

2013-12-01

Due to the critical shortage and continued need of blood and organ donations (ODs), research exploring similarities and differences in the motivational determinants of these behaviors is needed. In a sample of 258 university students, we used a cross-sectional design to test the utility of an extended theory of planned behavior (TPB) including moral norm, self-identity and in-group altruism (family/close friends and ethnic group), to predict people's blood and OD intentions. Overall, the extended TPB explained 77.0% and 74.6% of variance in blood and OD intentions, respectively. In regression analyses, common contributors to intentions across donation contexts were attitude, self-efficacy and self-identity. Normative influences varied with subjective norm as a significant predictor related to OD intentions but not blood donation intentions at the final step of regression analyses. Moral norm did not contribute significantly to blood or OD intentions. In-group altruism (family/close friends) was significantly related to OD intentions only in regressions. Future donation strategies should increase confidence to donate, foster a perception of self as the type of person who donates blood and/or organs, and address preferences to donate organs to in-group members only.

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.

Fish habitat regression under water scarcity scenarios in the Douro River basin

NASA Astrophysics Data System (ADS)

Segurado, Pedro; Jauch, Eduardo; Neves, Ramiro; Ferreira, Teresa

2015-04-01

Climate change will predictably alter hydrological patterns and processes at the catchment scale, with impacts on habitat conditions for fish. The main goals of this study are to identify the stream reaches that will undergo more pronounced flow reduction under different climate change scenarios and to assess which fish species will be more affected by the consequent regression of suitable habitats. The interplay between changes in flow and temperature and the presence of transversal artificial obstacles (dams and weirs) is analysed. The results will contribute to river management and impact mitigation actions under climate change. This study was carried out in the Tâmega catchment of the Douro basin. A set of 29 Hydrological, climatic, and hydrogeomorphological variables were modelled using a water modelling system (MOHID), based on meteorological data recorded monthly between 2008 and 2014. The same variables were modelled considering future climate change scenarios. The resulting variables were used in empirical habitat models of a set of key species (brown trout Salmo trutta fario, barbell Barbus bocagei, and nase Pseudochondrostoma duriense) using boosted regression trees. The stream segments between tributaries were used as spatial sampling units. Models were developed for the whole Douro basin using 401 fish sampling sites, although the modelled probabilities of species occurrence for each stream segment were predicted only for the Tâmega catchment. These probabilities of occurrence were used to classify stream segments into suitable and unsuitable habitat for each fish species, considering the future climate change scenario. The stream reaches that were predicted to undergo longer flow interruptions were identified and crossed with the resulting predictive maps of habitat suitability to compute the total area of habitat loss per species. Among the target species, the brown trout was predicted to be the most sensitive to habitat regression due to the interplay of flow reduction, increase of temperature and transversal barriers. This species is therefore a good indicator of climate change impacts in rivers and therefore we recommend using this species as a target of monitoring programs to be implemented in the context of climate change adaptation strategies.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Configural approaches to temperament assessment: implications for predicting risk of unintentional injury in children.

PubMed

Berry, Jack W; Schwebel, David C

2009-10-01

This study used two configural approaches to understand how temperament factors (surgency/extraversion, negative affect, and effortful control) might predict child injury risk. In the first approach, clustering procedures were applied to trait dimensions to identify discrete personality prototypes. In the second approach, two- and three-way trait interactions were considered dimensionally in regression models predicting injury outcomes. Injury risk was assessed through four measures: lifetime prevalence of injuries requiring professional medical attention, scores on the Injury Behavior Checklist, and frequency and severity of injuries reported in a 2-week injury diary. In the prototype analysis, three temperament clusters were obtained, which resembled resilient, overcontrolled, and undercontrolled types found in previous research. Undercontrolled children had greater risk of injury than children in the other groups. In the dimensional interaction analyses, an interaction between surgency/extraversion and negative affect tended to predict injury, especially when children lacked capacity for effortful control.

Which Preschool Mathematics Competencies Are Most Predictive of Fifth Grade Achievement?

PubMed

Nguyen, Tutrang; Watts, Tyler W; Duncan, Greg J; Clements, Douglas H; Sarama, Julie S; Wolfe, Christopher; Spitler, Mary Elaine

In an effort to promote best practices regarding mathematics teaching and learning at the preschool level, national advisory panels and organizations have emphasized the importance of children's emergent counting and related competencies, such as the ability to verbally count, maintain one-to-one correspondence, count with cardinality, subitize, and count forward or backward from a given number. However, little research has investigated whether the kind of mathematical knowledge promoted by the various standards documents actually predict later mathematics achievement. The present study uses longitudinal data from a primarily low-income and minority sample of children to examine the extent to which preschool mathematical competencies, specifically basic and advanced counting, predict fifth grade mathematics achievement. Using regression analyses, we find early numeracy abilities to be the strongest predictors of later mathematics achievement, with advanced counting competencies more predictive than basic counting competencies. Our results highlight the significance of preschool mathematics knowledge for future academic achievement.

T56. AN EXPLORATORY ANALYSIS CONVERTING SCORES BETWEEN THE PANSS AND BNSS

PubMed Central

Kott, Alan; Daniel, David

2018-01-01

Abstract Background The Brief Negative Symptom Scale is a relatively new instrument designed specifically to measure the negative symptoms in schizophrenia. Recently more clinical trials include the BNSS scale as a secondary or exploratory outcome, typically along with the PANSS. In the current analysis we aimed at establishing the equations that would allow conversion between the BNSS scale total score and the PANSS negative subscale and PANSS negative factors score as well as conversion equations between the expressive deficits and avolition/apathy factors of the scales. (Kirkpatrick, 2011; Strauss, 2012) Methods Data from 518 schizophrenia clinical trials subjects with both PANSS and BNSS data available were used. Regression analyses predicting the BNSS total score with the PANSS negative subscale score, and the BNSS total score with the PANSS Negative factor (NFS) score were performed on data from all subjects. Regression analyses predicting the BNSS avolition/apathy factor (items 1, 2, 3, 5, 6, 7, and 8) with the PANSS avolition/apathy factor (items N2, N4 and G16) and the BNSS expressive deficits factor (items 4, 9, 10, 11, 12, and 13)with the expressive deficits factor (items N1, N3, N6, G5, G7, and G13)of the PANSS were performed on a sample of 318 subjects with individual BNSS item scores available. In addition to estimating the equations we as well calculated the Pearson’s correlations between the scales. Results The PANSS and BNSS avolition/apathy factors were highly correlated (r=0.70) as were the expressive deficit factors r=0.83). The following equations predicting the BNSS total score were obtained from regression analyses performed on 2,560 data points: BNSS_total = -11.64 + 2.10*PANSS_negative_subscale BNSS_total = -9.26 + 2.11*PANSS_NFS The following equations predicting the BNSS factor scores from the PANSS factor scores were obtained from regression analyses performed on 1,634 data points: BNSS_avolition/apathy = -2.40 + 2.38 * PANSS_avolition/apathy BNSS_expressive_deficit_factor = -4.21 + 1.27 * PANSS_expressive_deficit_factor Discussion The BNSS differs from the PANSS negative factor because it addresses all five currently recognized domains of negative symptoms including anhedonia and attempts to differentiate anticipatory from consummatory states. In our analysis we have replicated the strong correlation between the BNSS total score and PANSS negative subscale and newly identified strong correlations between the BNSS total score and NFS as well as strong correlations between the avolotion/apathy and expressive deficit factors of the BNSS and the PANSS scales. (Kirkpatrick, 2011)The provided equations offer a useful tool allowing researchers and clinicians to easily convert the data between the instruments for reasons such as pooling data from multiple trials using one of the instruments, to allow interpretation of results within the context of previously conducted research, etc. but as well offer a framework for risk based monitoring to identify data deviating from the expected relationship and allow for a targeted exploration of the causes for such a disagreement. The data used for analysis included not only subjects with predominantly negative symptoms but as well acutely psychotic subjects as well as subjects in stable conditions allowing therefore to generalize the results across the majority of schizophrenic subjects. This post-hoc analysis is exploratory. We plan to further explore the potential utility of equations addressing the relationships among schizophrenia measures of symptom severity in an iterative manner with larger datasets.

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

PubMed

Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

2018-06-11

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Spiritual stress and coping model of divorce: a longitudinal study.

PubMed

Krumrei, Elizabeth J; Mahoney, Annette; Pargament, Kenneth I

2011-12-01

This study represents the first longitudinal effort to use a spiritual stress and coping model to predict adults' psychosocial adjustment following divorce. A community sample of 89 participants completed measures at the time of their divorce and 1 year later. Though the sample endorsed slightly lower levels of religiosity than the general U.S. population, most reported spiritual appraisals and positive and negative religious coping tied to divorce. Hierarchical regression analyses controlling general religiousness and nonreligious forms of coping indicated that (a) appraising divorce as a sacred loss or desecration at the time it occurred predicted more depressive symptoms and dysfunctional conflict tactics with the ex-spouse 1 year later; (b) positive religious coping reported about the year following divorce predicted greater posttraumatic growth 1 year after divorce; and (c) negative religious coping reported about the year following divorce predicted more depressive symptoms 1 year after the divorce. Bootstrapping mediation analyses indicated that negative religious coping fully mediated links between appraising the divorce as a sacred loss or desecration at the time it occurred and depressive symptoms 1 year later. In addition, moderation analyses revealed that negative religious coping is more strongly associated with depressive symptoms among those who form high versus low appraisals of their divorce as a sacred loss or desecration. These findings are relevant to divorce education and intervention provided by professionals in legal, family, mental health, and clerical roles. Implications are discussed for clinical and counseling psychology and religious communities.

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.

Salivary gland ultrasonography as a primary imaging tool for predicting efficacy of xerostomia treatment in patients with Sjögren's syndrome.

PubMed

Takagi, Yukinori; Sumi, Misa; Nakamura, Hideki; Sato, Shuntaro; Kawakami, Atsushi; Nakamura, Takashi

2016-02-01

To evaluate ultrasonography (US) grading of salivary gland disease as a predictor of treatment efficacy for impaired salivary function in xerostomia patients with or without Sjögren's syndrome (SS). We retrospectively analysed the prognostic importance of salivary US grading in 317 patients (168 with SS and 149 without SS). US images of the parotid and submandibular glands in each patient were individually categorized into grades 0-4 based on the extent of damage to the gland; and the sum total grade of the two gland types on either side was assigned a US score of 0-8 for each patient. The relative importance of US score and demographic and clinical variables was assessed using stepwise multiple regression analysis after various durations of xerostomia treatment. Multiple regression analysis indicated that the baseline US score before treatment was the most important factor [standardized regression coefficient (β) = -0.523, t-statistic (t) = -7.967, P < 0.001] in predicting negative outcomes in SS patients. Treatment duration (β = 0.277, t = 4.225, P < 0.001) was also a significant but less important positive variable. On the other hand, US grading did not effectively predict treatment outcomes in non-SS patients, with treatment duration (β = 0.199, t = 2.486, P = 0.014) and baseline salivary flow rate before treatment (β = -0.172, t = -2.159, P = 0.032) being significant but weak predictors of positive and negative outcome, respectively. Salivary gland US grading may help to predict outcomes of treatment for impaired salivary function in patients with SS. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Analysing Twitter and web queries for flu trend prediction.

PubMed

Santos, José Carlos; Matos, Sérgio

2014-05-07

Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p<0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.

Estimating verbal fluency and naming ability from the test of premorbid functioning and demographic variables: Regression equations derived from a regional UK sample.

PubMed

Jenkinson, Toni-Marie; Muncer, Steven; Wheeler, Miranda; Brechin, Don; Evans, Stephen

2018-06-01

Neuropsychological assessment requires accurate estimation of an individual's premorbid cognitive abilities. Oral word reading tests, such as the test of premorbid functioning (TOPF), and demographic variables, such as age, sex, and level of education, provide a reasonable indication of premorbid intelligence, but their ability to predict other related cognitive abilities is less well understood. This study aimed to develop regression equations, based on the TOPF and demographic variables, to predict scores on tests of verbal fluency and naming ability. A sample of 119 healthy adults provided demographic information and were tested using the TOPF, FAS, animal naming test (ANT), and graded naming test (GNT). Multiple regression analyses, using the TOPF and demographics as predictor variables, were used to estimate verbal fluency and naming ability test scores. Change scores and cases of significant impairment were calculated for two clinical samples with diagnosed neurological conditions (TBI and meningioma) using the method in Knight, McMahon, Green, and Skeaff (). Demographic variables provided a significant contribution to the prediction of all verbal fluency and naming ability test scores; however, adding TOPF score to the equation considerably improved prediction beyond that afforded by demographic variables alone. The percentage of variance accounted for by demographic variables and/or TOPF score varied from 19 per cent (FAS), 28 per cent (ANT), and 41 per cent (GNT). Change scores revealed significant differences in performance in the clinical groups, particularity the TBI group. Demographic variables, particularly education level, and scores on the TOPF should be taken into consideration when interpreting performance on tests of verbal fluency and naming ability. © 2017 The British Psychological Society.

Low thrombospondin 2 expression is predictive of low tumor regression after neoadjuvant chemoradiotherapy in rectal cancer.

PubMed

Lin, Cheng-Yi; Lin, Ching-Yih; Chang, I-Wei; Sheu, Ming-Jen; Li, Chien-Feng; Lee, Sung-Wei; Lin, Li-Ching; Lee, Ying-En; He, Hong-Lin

2015-01-01

Neoadjuvant concurrent chemoradiotherapy (CCRT) followed by surgery is the mainstay of treatment for locally advanced rectal cancer. Several heparin-binding associated proteins have been reported to play a critical role in cancer progression. However, the clinical relevancies of such proteins and their associations with CCRT response in rectal cancer have not yet to be fully elucidated. The analysis of a public transcriptome of rectal cancer indicated that thrombospondin 2 (THBS2) is a predictive factor for CCRT response. Immunohistochemical analyses were conducted to evaluate the expression of THBS2 in pretreatment biopsy specimens from rectal cancer patients without distant metastasis. Furthermore, the relationships between THBS2 expression and various clinicopathological factors or survival were analyzed. Low expression of THBS2 was significantly associated with advanced pretreatment tumor (P<0.001) and nodal status (P=0.004), post-treatment tumor (P<0.001) and nodal status (P<0.001), increased vascular invasion (P=0.003), increased perineural invasion (P=0.023) and inferior tumor regression grade (P=0.015). In univariate analysis, low THBS2 expression predicted worse outcomes for disease-free survival, local recurrence-free survival and metastasis-free survival (all P<0.001). In multivariate analysis, low expression of THBS2 still served as a negative prognostic factor for disease-free survival (Hazard ratio=3.057, P=0.002) and metastasis-free survival (Hazard ratio=3.362, P=0.012). Low THBS2 expression was correlated with advanced disease status and low tumor regression after preoperative CCRT and that it acted as an independent negative prognostic factor in rectal cancer. THBS2 may represent a predictive biomarker for CCRT response in rectal cancer.

Gender differences in health-related quality of life of adolescents with cystic fibrosis

PubMed Central

Arrington-Sanders, Renata; Yi, Michael S; Tsevat, Joel; Wilmott, Robert W; Mrus, Joseph M; Britto, Maria T

2006-01-01

Background Female patients with cystic fibrosis (CF) have consistently poorer survival rates than males across all ages. To determine if gender differences exist in health-related quality of life (HRQOL) of adolescent patients with CF, we performed a cross-section analysis of CF patients recruited from 2 medical centers in 2 cities during 1997–2001. Methods We used the 87-item child self-report form of the Child Health Questionnaire to measure 12 health domains. Data was also collected on age and forced expiratory volume in 1 second (FEV1). We analyzed data from 98 subjects and performed univariate analyses and linear regression or ordinal logistic regression for multivariable analyses. Results The mean (SD) age was 14.6 (2.5) years; 50 (51.0%) were female; and mean FEV1 was 71.6% (25.6%) of predicted. There were no statistically significant gender differences in age or FEV1. In univariate analyses, females reported significantly poorer HRQOL in 5 of the 12 domains. In multivariable analyses controlling for FEV1 and age, we found that female gender was associated with significantly lower global health (p < 0.05), mental health (p < 0.01), and general health perceptions (p < 0.05) scores. Conclusion Further research will need to focus on the causes of these differences in HRQOL and on potential interventions to improve HRQOL of adolescent patients with CF. PMID:16433917

The theory of planned behavior applied to young people's use of social networking Web sites.

PubMed

Pelling, Emma L; White, Katherine M

2009-12-01

Despite the increasing popularity of social networking Web sites (SNWs), very little is known about the psychosocial variables that predict people's use of these Web sites. The present study used an extended model of the theory of planned behavior (TPB), including the additional variables of self-identity and belongingness, to predict high-level SNW use intentions and behavior in a sample of young people ages 17 to 24 years. Additional analyses examined the impact of self-identity and belongingness on young people's addictive tendencies toward SNWs. University students (N = 233) completed measures of the standard TPB constructs (attitude, subjective norm, and perceived behavioral control), the additional predictor variables (self-identity and belongingness), demographic variables (age, gender, and past behavior), and addictive tendencies. One week later, they reported their engagement in high-level SNW use during the previous week. Regression analyses partially supported the TPB: attitude and subjective norm significantly predicted intentions to engage in high-level SNW use with intention significantly predicting behavior. Self-identity, but not belongingness, significantly contributed to the prediction of intention and, unexpectedly, behavior. Past behavior also significantly predicted intention and behavior. Self-identity and belongingness significantly predicted addictive tendencies toward SNWs. Overall, the present study revealed that high-level SNW use is influenced by attitudinal, normative, and self-identity factors, findings that can be used to inform strategies that aim to modify young people's high levels of use or addictive tendencies for SNWs.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Factor Analysis of Therapist-Identified Treatment Targets in Community-Based Children's Mental Health.

PubMed

Love, Allison R; Okado, Izumi; Orimoto, Trina E; Mueller, Charles W

2018-01-01

The present study used exploratory and confirmatory factor analyses to identify underlying latent factors affecting variation in community therapists' endorsement of treatment targets. As part of a statewide practice management program, therapist completed monthly reports of treatment targets (up to 10 per month) for a sample of youth (n = 790) receiving intensive in-home therapy. Nearly 75 % of youth were diagnosed with multiple co-occurring disorders. Five factors emerged: Disinhibition, Societal Rules Evasion, Social Engagement Deficits, Emotional Distress, and Management of Biodevelopmental Outcomes. Using logistic regression, primary diagnosis predicted therapist selection of Disinhibition and Emotional Distress targets. Client age predicted endorsement of Societal Rules Evasion targets. Practice-to-research implications are discussed.

Boredom proneness and emotion regulation predict emotional eating.

PubMed

Crockett, Amanda C; Myhre, Samantha K; Rokke, Paul D

2015-05-01

Emotional eating is considered a risk factor for eating disorders and an important contributor to obesity and its associated health problems. It has been suggested that boredom may be an important contributor to overeating, but has received relatively little attention. A sample of 552 college students was surveyed. Linear regression analyses found that proneness to boredom and difficulties in emotion regulation simultaneously predicted inappropriate eating behavior, including eating in response to boredom, other negative emotions, and external cues. The unique contributions of these variables to emotional eating were discussed. These findings help to further identify which individuals could be at risk for emotional eating and potentially for unhealthy weight gain. © The Author(s) 2015.

Dietary and exercise change following acute cardiac syndrome onset: A latent class growth modelling analysis.

PubMed

Bennett, Paul; Gruszczynska, Ewa; Marke, Victoria

2016-10-01

The present study aim determine sub-group trajectories of change on measures of diet and exercise following acute coronary syndrome. 150 participants were assessed in hospital, 1 month and 6 months subsequently on measures including physical activity, diet, illness beliefs, coping and mood. Change trajectories were measured using latent class growth modelling. Multinomial logistic regression was used to predict class membership. These analyses revealed changes in exercise were confined to a sub-group of participants already reporting relatively high exercise levels; those eating less healthily evidenced modest dietary improvements. Coping, gender, depression and perceived control predicted group membership to a modest degree. © The Author(s) 2015.

Ambivalent Sexism in Close Relationships: (Hostile) Power and (Benevolent) Romance Shape Relationship Ideals

PubMed Central

Lee, Tiane L.; Fiske, Susan T.; Glick, Peter; Chen, Zhixia

2013-01-01

Gender-based structural power and heterosexual dependency produce ambivalent gender ideologies, with hostility and benevolence separately shaping close-relationship ideals. The relative importance of romanticized benevolent versus more overtly power-based hostile sexism, however, may be culturally dependent. Testing this, northeast US (N=311) and central Chinese (N=290) undergraduates rated prescriptions and proscriptions (ideals) for partners and completed Ambivalent Sexism and Ambivalence toward Men Inventories (ideologies). Multiple regressions analyses conducted on group-specific relationship ideals revealed that benevolent ideologies predicted partner ideals, in both countries, especially for US culture’s romance-oriented relationships. Hostile attitudes predicted men’s ideals, both American and Chinese, suggesting both societies’ dominant-partner advantage. PMID:23914004

Predicting fruit consumption: the role of habits, previous behavior and mediation effects

PubMed Central

2014-01-01

Background This study assessed the role of habits and previous behavior in predicting fruit consumption as well as their additional predictive contribution besides socio-demographic and motivational factors. In the literature, habits are proposed as a stable construct that needs to be controlled for in longitudinal analyses that predict behavior. The aim of this study is to provide empirical evidence for the inclusion of either previous behavior or habits. Methods A random sample of 806 Dutch adults (>18 years) was invited by an online survey panel of a private research company to participate in an online study on fruit consumption. A longitudinal design (N = 574) was used with assessments at baseline and after one (T2) and two months (T3). Multivariate linear regression analysis was used to assess the differential value of habit and previous behavior in the prediction of fruit consumption. Results Eighty percent of habit strength could be explained by habit strength one month earlier, and 64% of fruit consumption could be explained by fruit consumption one month earlier. Regression analyses revealed that the model with motivational constructs explained 41% of the behavioral variance at T2 and 38% at T3. The addition of previous behavior and habit increased the explained variance up to 66% at T2 and to 59% at T3. Inclusion of these factors resulted in non-significant contributions of the motivational constructs. Furthermore, our findings showed that the effect of habit strength on future behavior was to a large extent mediated by previous behavior. Conclusions Both habit and previous behavior are important as predictors of future behavior, and as educational objectives for behavior change programs. Our results revealed less stability for the constructs over time than expected. Habit strength was to a large extent mediated by previous behavior and our results do not strongly suggest a need for the inclusion of both constructs. Future research needs to assess the conditions that determine direct influences of both previous behavior and habit, since these influences may differ per type of health behavior, per context stability in which the behavior is performed, and per time frame used for predicting future behavior. PMID:25037859

Predicting thermally stressful events in rivers with a strategy to evaluate management alternatives

USGS Publications Warehouse

Maloney, K.O.; Cole, J.C.; Schmid, M.

2016-01-01

Water temperature is an important factor in river ecology. Numerous models have been developed to predict river temperature. However, many were not designed to predict thermally stressful periods. Because such events are rare, traditionally applied analyses are inappropriate. Here, we developed two logistic regression models to predict thermally stressful events in the Delaware River at the US Geological Survey gage near Lordville, New York. One model predicted the probability of an event >20.0 °C, and a second predicted an event >22.2 °C. Both models were strong (independent test data sensitivity 0.94 and 1.00, specificity 0.96 and 0.96) predicting 63 of 67 events in the >20.0 °C model and all 15 events in the >22.2 °C model. Both showed negative relationships with released volume from the upstream Cannonsville Reservoir and positive relationships with difference between air temperature and previous day's water temperature at Lordville. We further predicted how increasing release volumes from Cannonsville Reservoir affected the probabilities of correctly predicted events. For the >20.0 °C model, an increase of 0.5 to a proportionally adjusted release (that accounts for other sources) resulted in 35.9% of events in the training data falling below cutoffs; increasing this adjustment by 1.0 resulted in 81.7% falling below cutoffs. For the >22.2 °C these adjustments resulted in 71.1% and 100.0% of events falling below cutoffs. Results from these analyses can help managers make informed decisions on alternative release scenarios.

A sampling study on rock properties affecting drilling rate index (DRI)

NASA Astrophysics Data System (ADS)

Yenice, Hayati; Özdoğan, Mehmet V.; Özfırat, M. Kemal

2018-05-01

Drilling rate index (DRI) developed in Norway is a very useful index in determining the drillability of rocks and even in performance prediction of hard rock TBMs and it requires special laboratory test equipment. Drillability is one of the most important subjects in rock excavation. However, determining drillability index from physical and mechanical properties of rocks is very important for practicing engineers such as underground excavation, drilling operations in open pit mining, underground mining and natural stone production. That is why many researchers have studied concerned with drillability to find the correlations between drilling rate index (DRI) and penetration rate, influence of geological properties on drillability prediction in tunneling, correlations between rock properties and drillability. In this study, the relationships between drilling rate index (DRI) and some physico-mechanical properties (Density, Shore hardness, uniaxial compressive strength (UCS, σc), Indirect tensile strength (ITS, σt)) of three different rock groups including magmatic, sedimentary and metamorphic were evaluated using both simple and multiple regression analysis. This study reveals the effects of rock properties on DRI according to different types of rocks. In simple regression, quite high correlations were found between DRI and uniaxial compressive strength (UCS) and also between DRI and indirect tensile strength (ITS) values. Multiple regression analyses revealed even higher correlations when compared to simple regression. Especially, UCS, ITS, Shore hardness (SH) and the interactions between them were found to be very effective on DRI values.

Separation in Logistic Regression: Causes, Consequences, and Control.

PubMed

Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg

2018-04-01

Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

PubMed Central

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs. PMID:27806093

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462

Methods for estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011

USGS Publications Warehouse

Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis

2014-01-01

Reliable estimates of the magnitude and frequency of floods are essential for the design of transportation and water-conveyance structures, flood-insurance studies, and flood-plain management. Such estimates are particularly important in densely populated urban areas. In order to increase the number of streamflow-gaging stations (streamgages) available for analysis, expand the geographical coverage that would allow for application of regional regression equations across State boundaries, and build on a previous flood-frequency investigation of rural U.S Geological Survey streamgages in the Southeast United States, a multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The at-site flood-frequency analysis of annual peak-flow data for urban and small, rural streams (through September 30, 2011) included 116 urban streamgages and 32 small, rural streamgages, defined in this report as basins draining less than 1 square mile. The regional regression analysis included annual peak-flow data from an additional 338 rural streamgages previously included in U.S. Geological Survey flood-frequency reports and 2 additional rural streamgages in North Carolina that were not included in the previous Southeast rural flood-frequency investigation for a total of 488 streamgages included in the urban and small, rural regression analysis. The at-site flood-frequency analyses for the urban and small, rural streamgages included the expected moments algorithm, which is a modification of the Bulletin 17B log-Pearson type III method for fitting the statistical distribution to the logarithms of the annual peak flows. Where applicable, the flood-frequency analysis also included low-outlier and historic information. Additionally, the application of a generalized Grubbs-Becks test allowed for the detection of multiple potentially influential low outliers. Streamgage basin characteristics were determined using geographical information system techniques. Initial ordinary least squares regression simulations reduced the number of basin characteristics on the basis of such factors as statistical significance, coefficient of determination, Mallow’s Cp statistic, and ease of measurement of the explanatory variable. Application of generalized least squares regression techniques produced final predictive (regression) equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions (HR1, Piedmont–Ridge and Valley; HR3, Sand Hills; and HR4, Coastal Plain), which previously had been defined from exploratory regression analysis in the Southeast rural flood-frequency investigation. Because of the limited availability of urban streamgages in the Coastal Plain of Georgia, South Carolina, and North Carolina, additional urban streamgages in Florida and New Jersey were used in the regression analysis for this region. Including the urban streamgages in New Jersey allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 3.5 to 53.5 square miles. Average standard error of prediction for the predictive equations, which is a measure of the average accuracy of the regression equations when predicting flood estimates for ungaged sites, range from 25.0 percent for the 10-percent annual exceedance probability regression equation for the Piedmont–Ridge and Valley region to 73.3 percent for the 0.2-percent annual exceedance probability regression equation for the Sand Hills region.

The osmotic tolerance of boar spermatozoa and its usefulness as sperm quality parameter.

PubMed

Yeste, Marc; Briz, Mailo; Pinart, Elisabeth; Sancho, Sílvia; Bussalleu, Eva; Bonet, Sergi

2010-06-01

Predicting the fertility outcome of ejaculates is very important in the field of porcine reproduction. The aims of this study were to determine the effects of different osmotic treatments on boar spermatozoa and to correlate them with fertility and prolificacy, assessed as non-return rates within 60 days (NRR(60d)) of the first inseminations, and litter size (LS), respectively. Sperm samples (n=100) from one hundred healthy Piétrain boars were used to assess 48 treatments combining different osmolalities (ranged between 100 and 4000 mOsm kg(-1)), different compounds used to prepare anisotonic solutions, and two different modalities: return and non-return to isotonic conditions. Sperm quality was evaluated before and after applying the treatments on the basis of analyses of sperm viability, motility, morphology and percentages of acrosome-intact spermatozoa. Statistical analyses were performed using a one-way ANOVA and post hoc Tukey's test, linear regression analyses (Pearson correlation and multiple regression) and Jackknife cross-validation. Although three conventional parameters: sperm viability, sperm morphology and the percentages of acrosome-intact spermatozoa were significantly correlated with NRR(60d) and with LS, their respective osmotic tolerance parameters (defined for each parameter and treatment regarding with negative control) presented a higher Pearson coefficient with both fertility and prolificacy in three treatments (150 mOsm kg(-1) with non-return to isotonic conditions, 200 mOsm kg(-1) with return and 500 mOsm kg(-1) using sodium citrate and non-return to isotonic conditions). We conclude that osmotic resistance in sperm viability, sperm morphology and acrosome-intactness in the treatments mentioned above could be assessed along with classical parameters to better predict the fertilising ability of a given ejaculate. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder.

PubMed

Shen, Yuedi; Yao, Jiashu; Jiang, Xueyan; Zhang, Lei; Xu, Luoyi; Feng, Rui; Cai, Liqiang; Liu, Jing; Wang, Jinhui; Chen, Wei

2015-08-01

Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression. © 2015 Wiley Periodicals, Inc.

Predicting the reading skill of Japanese children.

PubMed

Ogino, Tatsuya; Hanafusa, Kaoru; Morooka, Teruko; Takeuchi, Akihito; Oka, Makio; Ohtsuka, Yoko

2017-02-01

To clarify cognitive processes underlining the development of reading in children speaking Japanese as their first language, we examined relationships between performances of cognitive tasks in the preschool period and later reading abilities. Ninety-one normally developing preschoolers (41 girls and 50 boys; 5years 4months to 6years 4months, mean 5years 10months) participated as subjects. We conducted seven cognitive tasks including phonological awareness tasks, naming tasks, and working memory tasks in the preschool period. In terms of reading tasks, the hiragana naming task was administered in the preschool period; the reading times, which is a composite score of the monomoraic syllable reading task, the word and the non-word reading tasks, and the single sentence reading task, was evaluated in first and second grade; and the kanji reading task (naming task) was tested in second grade. Raven's colored progressive matrices and picture vocabulary test revised were also conducted in first grade. Correlation analyses between task scores and stepwise multiple regression analyses were implemented. Tasks tapping phonological awareness, lexical access, and verbal working memory showed significant correlations with reading tasks. In the multiple regression analyses the performances in the verbal working memory task played a key role in predicting character naming task scores (the hiragana naming task and the kanji reading task) while the digit naming task was an important predictor of reading times. Unexpectedly, the role of phonological (mora) awareness was modest among children speaking Japanese. Cognitive functions including phonological awareness, digit naming, and verbal working memory (especially the latter two) were involved in the development of reading skills of children speaking Japanese. Copyright © 2016 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

Predictors of early versus late smoking abstinence within a 24-month disease management program.

PubMed

Cox, Lisa Sanderson; Wick, Jo A; Nazir, Niaman; Cupertino, A Paula; Mussulman, Laura M; Ahluwalia, Jasjit S; Ellerbeck, Edward F

2011-03-01

Standard smoking cessation treatment studies have been limited to 6- to 12-month follow-up, and examination of predictors of abstinence has been restricted to this timeframe. The KanQuit study enrolled 750 rural smokers across all stages of readiness to stop smoking and provided pharmacotherapy management and/or disease management, including motivational interviewing (MI) counseling every 6 months over 2 years. This paper examines differences in predictors of abstinence following initial (6-month) and extended (24-month) intervention. Baseline variables were analyzed as potential predictors of self-reported smoking abstinence at Month 6 and at Month 24. Chi-square tests, 2-sample t tests, and multiple logistic regression analyses were used to identify predictors of abstinence among 592 participants who completed assessment at baseline and Months 6 and 24. Controlling for treatment group, the final regression models showed that male gender and lower baseline cigarettes per day predicted abstinence at both 6 and 24 months. While remaining significant, the relative advantage of being male decreased over time. Global motivation to stop smoking, controlled motivation, and self-efficacy predicted abstinence at 6 months but did not predict abstinence at Month 24. In contrast, stage of change was strongly predictive of 24-month smoking status. While the importance of some predictors of successful smoking cessation appeared to diminish over time, initial lack of interest in cessation and number of cigarettes per day strongly predicted continued smoking following a 2-year program.

Determinants of work ability and its predictive value for disability.

PubMed

Alavinia, S M; de Boer, A G E M; van Duivenbooden, J C; Frings-Dresen, M H W; Burdorf, A

2009-01-01

Maintaining the ability of workers to cope with physical and psychosocial demands at work becomes increasingly important in prolonging working life. To analyse the effects of work-related factors and individual characteristics on work ability and to determine the predictive value of work ability on receiving a work-related disability pension. A longitudinal study was conducted among 850 construction workers aged 40 years and older, with average follow-up period of 23 months. Disability was defined as receiving a disability pension, granted to workers unable to continue working in their regular job. Work ability was assessed using the work ability index (WAI). Associations between work-related factors and individual characteristics with work ability at baseline were evaluated using linear regression analysis, and Cox regression analysis was used to evaluate the predictive value of work ability for disability. Work-related factors were associated with a lower work ability at baseline, but had little prognostic value for disability during follow-up. The hazard ratios for disability among workers with a moderate and poor work ability at baseline were 8 and 32, respectively. All separate scales in the WAI had predictive power for future disability with the highest influence of current work ability in relation to job demands and lowest influence of diseases diagnosed by a physician. A moderate or poor work ability was highly predictive for receiving a disability pension. Preventive measures should facilitate a good balance between work performance and health in order to prevent quitting labour participation.

Clinical changes in terminally ill cancer patients and death within 48 h: when should we refer patients to a separate room?

PubMed

Hwang, In Cheol; Ahn, Hong Yup; Park, Sang Min; Shim, Jae Yong; Kim, Kyoung Kon

2013-03-01

There is scant research concerning the prediction of imminent death, and current studies simply list events "that have already occurred" around 48 h of the death. We sought to determine what events herald the onset of dying process using the length of time from "any change" to death. This is a prospective observational study with chart audit. Inclusion criteria were terminal cancer patients who passed away in a palliative care unit. The analysis was limited to 181 patients who had medical records for their final week. Commonly observed events in the terminally ill were determined and their significant changes were defined beforehand. We selected the statistically significant changes by multiple logistic regression analysis and evaluated their predictive values for "death within 48 h." The median age was 67 years and there were 103 male patients. After adjusting for age, sex, primary cancer site, metastatic site, and cancer treatment, multiple logistic regression analyses for association between the events and "death within 48 h" revealed some significant changes: confused mental state, decreased blood pressure, increased pulse pressure, low oxygen saturation, death rattle, and decreased conscious level. The events that had higher predictability for death within 48 h were decreased blood pressure and low oxygen saturation, and the positive and negative predictive values of their combination were 95.0 and 81.4%, respectively. The most reliable events to predict impending death were decreased blood pressure and low oxygen saturation.

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

PubMed

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

2013-10-01

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

Predictive factors for postoperative visual function of primary chronic rhegmatogenous retinal detachment after scleral buckling.

PubMed

Fang, Wei; Li, Jiu-Ke; Jin, Xiao-Hong; Dai, Yuan-Min; Li, Yu-Min

2016-01-01

To evaluate predictive factors for postoperative visual function of primary chronic rhegmatgenous retinal detachment (RRD) after sclera buckling (SB). Totally 48 patients (51 eyes) with primary chronic RRD were included in this prospective interventional clinical cases study, which underwent SB alone from June 2008 to December 2014. Age, sex, symptoms duration, detached extension, retinal hole position, size, type, fovea on/off, proliferative vitreoretinopathy (PVR), posterior vitreous detachment (PVD), baseline best corrected visual acuity (BCVA), operative duration, follow up duration, final BCVA were measured. Pearson correlation analysis, Spearman correlation analysis and multivariate linear stepwise regression were used to confirm predictive factors for better final visual acuity. Student's t-test, Wilcoxon two-sample test, Chi-square test and logistic stepwise regression were used to confirm predictive factors for better vision improvement. Baseline BCVA was 0.8313±0.6911 logMAR and final BCVA was 0.4761±0.4956 logMAR. Primary surgical success rate was 92.16% (47/51). Correlation analyses revealed shorter symptoms duration (r=0.3850, P=0.0053), less detached area (r=0.5489, P<0.0001), fovea (r=0.4605, P=0.0007), no PVR (r=0.3138, P=0.0250), better baseline BCVA (r=0.7291, P<0.0001), shorter operative duration (r=0.3233, P=0.0207) and longer follow up (r=-0.3358, P=0.0160) were related with better final BCVA, while independent predictive factors were better baseline BCVA [partial R-square (PR(2))=0.5316, P<0.0001], shorter symptoms duration (PR(2)=0.0609, P=0.0101), longer follow up duration (PR(2)=0.0278, P=0.0477) and shorter operative duration (PR(2)=0.0338, P=0.0350). Patients with vision improvement took up 49.02% (25/51). Univariate and multivariate analyses both revealed predictive factors for better vision improvement were better baseline vision [odds ratio (OR) =50.369, P=0.0041] and longer follow up duration (OR=1.144, P=0.0067). Independent predictive factors for better visual outcome of primary chronic RRD after SB are better baseline BCVA, shorter symptoms duration, shorter operative duration and longer follow up duration, while independent predictive factors for better vision improvement after operation are better baseline vision and longer follow up duration.

Magnitude and frequency of floods in Arkansas

USGS Publications Warehouse

Hodge, Scott A.; Tasker, Gary D.

1995-01-01

Methods are presented for estimating the magnitude and frequency of peak discharges of streams in Arkansas. Regression analyses were developed in which a stream's physical and flood characteristics were related. Four sets of regional regression equations were derived to predict peak discharges with selected recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years on streams draining less than 7,770 square kilometers. The regression analyses indicate that size of drainage area, main channel slope, mean basin elevation, and the basin shape factor were the most significant basin characteristics that affect magnitude and frequency of floods. The region of influence method is included in this report. This method is still being improved and is to be considered only as a second alternative to the standard method of producing regional regression equations. This method estimates unique regression equations for each recurrence interval for each ungaged site. The regression analyses indicate that size of drainage area, main channel slope, mean annual precipitation, mean basin elevation, and the basin shape factor were the most significant basin and climatic characteristics that affect magnitude and frequency of floods for this method. Certain recommendations on the use of this method are provided. A method is described for estimating the magnitude and frequency of peak discharges of streams for urban areas in Arkansas. The method is from a nationwide U.S. Geeological Survey flood frequency report which uses urban basin characteristics combined with rural discharges to estimate urban discharges. Annual peak discharges from 204 gaging stations, with drainage areas less than 7,770 square kilometers and at least 10 years of unregulated record, were used in the analysis. These data provide the basis for this analysis and are published in the Appendix of this report as supplemental data. Large rivers such as the Red, Arkansas, White, Black, St. Francis, Mississippi, and Ouachita Rivers have floodflow characteristics that differ from those of smaller tributary streams and were treated individually. Regional regression equations are not applicable to these large rivers. The magnitude and frequency of floods along these rivers are based on specific station data. This section is provided in the Appendix and has not been updated since the last Arkansas flood frequency report (1987b), but is included at the request of the cooperator.

Nomogram to Predict Graft Thickness in Descemet Stripping Automated Endothelial Keratoplasty: An Eye Bank Study.

PubMed

Bae, Steven S; Menninga, Isaac; Hoshino, Richard; Humphreys, Christine; Chan, Clara C

2018-06-01

The purpose of this study was to develop a nomogram to predict postcut thickness of corneal grafts prepared at an eye bank for Descemet stripping automated endothelial keratoplasty (DSAEK). Retrospective chart review was performed of DSAEK graft preparations by 3 experienced technicians from April 2012 to May 2017 at the Eye Bank of Canada-Ontario Division. Variables collected included the following: donor demographics, death-to-preservation time, death-to-processing time, precut tissue thickness, postcut tissue thickness, microkeratome head size, endothelial cell count, cut technician, and rate of perforation. Linear regression models were generated for each microkeratome head size (300 and 350 μm). A total of 780 grafts were processed during the study period. Twelve preparation attempts resulted in perforation (1.5%) and were excluded. Mean precut tissue thickness was 510 ± 49 μm (range: 363-670 μm). Mean postcut tissue thickness was 114 ± 22 μm (range: 57-193 μm). Seventy-nine percent (608/768) of grafts were ≤130 μm. The linear regression models included precut thickness and donor age, which were able to predict the thickness to within 25 μm 80% of the time. We report a nomogram to predict thickness of DSAEK corneal grafts prepared in an eye bank setting, which was accurate to within 25 μm 80% of the time. Other eye banks could consider performing similar analyses.

[Prediction of soil nutrients spatial distribution based on neural network model combined with goestatistics].

PubMed

Li, Qi-Quan; Wang, Chang-Quan; Zhang, Wen-Jiang; Yu, Yong; Li, Bing; Yang, Juan; Bai, Gen-Chuan; Cai, Yan

2013-02-01

In this study, a radial basis function neural network model combined with ordinary kriging (RBFNN_OK) was adopted to predict the spatial distribution of soil nutrients (organic matter and total N) in a typical hilly region of Sichuan Basin, Southwest China, and the performance of this method was compared with that of ordinary kriging (OK) and regression kriging (RK). All the three methods produced the similar soil nutrient maps. However, as compared with those obtained by multiple linear regression model, the correlation coefficients between the measured values and the predicted values of soil organic matter and total N obtained by neural network model increased by 12. 3% and 16. 5% , respectively, suggesting that neural network model could more accurately capture the complicated relationships between soil nutrients and quantitative environmental factors. The error analyses of the prediction values of 469 validation points indicated that the mean absolute error (MAE) , mean relative error (MRE), and root mean squared error (RMSE) of RBFNN_OK were 6.9%, 7.4%, and 5. 1% (for soil organic matter), and 4.9%, 6.1% , and 4.6% (for soil total N) smaller than those of OK (P<0.01), and 2.4%, 2.6% , and 1.8% (for soil organic matter), and 2.1%, 2.8%, and 2.2% (for soil total N) smaller than those of RK, respectively (P<0.05).

Impact of asylum interviews on the mental health of traumatized asylum seekers

PubMed Central

Schock, Katrin; Rosner, Rita; Knaevelsrud, Christine

2015-01-01

Background Asylum interviews within the asylum procedure are associated with psychological stress for traumatized asylum seekers. This study investigates the impact of asylum interviews on the mental health in a sample of 40 traumatized asylum seekers. The comparison group consisted of refugees (N=10) that had not been invited to an asylum interview. Additionally, the moderating effects of trial-related variables such as perceived justice of the trial, stress of giving testimony, and stress of waiting for the asylum interview were examined. Method Participants were assessed on average 10 days before (t1) and 16 days after (t2) the asylum interview. Chi-square tests for dichotomous and categorical variables were used to compare the descriptive statistics of the two groups. To investigate symptom changes from t1 to t2, paired t-tests were calculated. The magnitude of effects was measured by Cohen's effect size d within groups. Hierarchical regression analyses were conducted for demographic and trial variables predicting posttraumatic intrusions, avoidance, and hyperarousal. Results Data showed a significant increase in posttraumatic intrusions and a significant decrease in posttraumatic avoidance and hyperarousal symptoms from t1 to t2. No significant symptom changes in the posttraumatic stress disorder subscales were found in the comparison group. The results of hierarchical regression analyses revealed perceived justice of the interview to predict the increase of intrusions and the number of experienced traumata and testimony stress to predict posttraumatic avoidance. Conclusions The present findings underline the stressful impact of asylum interviews on traumatized refugees. They indicate that the asylum interview might decrease posttraumatic avoidance and trigger posttraumatic intrusions, thus highlight the importance of ensuring that the already vulnerable group of traumatized refugees needs to be treated with empathy during their asylum interview. PMID:26333540

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

PubMed

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

2016-08-01

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

Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors.

PubMed

Walthouwer, Michel Jean Louis; Oenema, Anke; Candel, Math; Lechner, Lilian; de Vries, Hein

2015-04-01

Eating in moderation, i.e. the attempt to monitor and limit the intake of energy-dense foods, is a promising strategy in the prevention of weight gain. The purpose of this study was to examine which psychosocial factors derived from the I-Change Model (ICM) were associated with eating in moderation, and whether these factors differed between adults with a correct (aware) or incorrect (unaware) perception of their dietary behaviour. This study used a longitudinal design with measurements at baseline (N = 483) and six-month follow-up (N = 379). Eating in moderation was defined as the average daily energy intake from energy-dense food products and was measured by a validated food frequency questionnaire. Linear regression analyses were used to assess the associations between the ICM factors and eating in moderation. The moderating role of awareness was examined by including interactions between awareness and the ICM factors in the regression analyses using the pick-a-point approach to further examine the associations for aware and unaware participants. Participants who were aware of their dietary behaviour had a significantly lower average daily energy intake compared to those who were unaware. Eating in moderation was predicted by awareness, risk perception, social influence and intention. Among the aware participants, eating in moderation was predicted by risk perception, attitude, social influence and intention. Among the unaware participants, only risk perception and self-efficacy were significantly associated with eating in moderation. Our findings show that psychosocial factors may only predict eating in moderation when people are aware of their risk behaviour. Therefore, interventions aimed at promoting complex behaviours, such as eating in moderation, should first focus on improving individuals' awareness of their risk behaviour before targeting motivational factors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mother-Child Discrepancy in Perceived Family Functioning and Adolescent Developmental Outcomes in Families Experiencing Economic Disadvantage in Hong Kong.

PubMed

Leung, Janet T Y; Shek, Daniel T L; Li, Lin

2016-10-01

Though growing attention has been devoted to examining informant discrepancies of family attributes in social science research, studies that examine how interactions between mother-reported and adolescent-reported family functioning predict adolescent developmental outcomes in underprivileged families are severely lacking. The current study investigated the difference between mothers and adolescents in their reports of family functioning, as well as the relationships between mother-reported and adolescent-reported family functioning and adolescent developmental outcomes in a sample of 432 Chinese single-mother families (mean age of adolescents = 13.7 years, 51.2 % girls, mean age of mothers = 43.5 years, 69.9 % divorced) experiencing economic disadvantage in Hong Kong. Polynomial regression analyses were conducted to assess whether discrepancy in family functioning between mother reports and adolescent reports predicted resilience, beliefs in the future, cognitive competence, self-efficacy and self-determination of adolescents. The results indicated that adolescents reported family functioning more negatively than did their mothers. Polynomial regression analyses showed that the interaction term between mothers' reports and adolescents' reports of family functioning predicted adolescent developmental outcomes in Chinese single-mother families living in poverty. Basically, under poor adolescent-reported family functioning, adolescent development would be relatively better if their mothers reported more positive family functioning. In contrast, under good adolescent-reported family functioning, adolescents expressed better developmental outcomes when mothers reported lower levels of family functioning than those mothers who reported higher levels of family functioning. The findings provide insights on how congruency and discrepancy between informant reports of family functioning would influence adolescent development. Theoretical and practical implications of the findings are discussed.

Advanced interatrial block predicts new-onset atrial fibrillation and ischemic stroke in patients with heart failure: The "Bayes' Syndrome-HF" study.

PubMed

Escobar-Robledo, Luis Alberto; Bayés-de-Luna, Antoni; Lupón, Josep; Baranchuk, Adrian; Moliner, Pedro; Martínez-Sellés, Manuel; Zamora, Elisabet; de Antonio, Marta; Domingo, Mar; Cediel, Germán; Núñez, Julio; Santiago-Vacas, Evelyn; Bayés-Genís, Antoni

2018-05-18

Advanced interatrial block (IAB) is characterized by a prolonged (≥120 ms) and bimodal P wave in the inferior leads. The association between advanced IAB and atrial fibrillation (AF) is known as "Bayes' Syndrome", and there is scarce information about it in heart failure (HF). We examined the prevalence of IAB and whether advanced IAB could predict new-onset AF and/or stroke in HF patients. The prospective observational "Bayes' Syndrome-HF" study included consecutive outpatients with chronic HF. The primary endpoints were new-onset AF, ischemic stroke, and the composite of both. A secondary endpoint included all-cause death alone or in combination with the primary endpoint. Comprehensive multivariable Cox regression analyses were performed. Among 1050 consecutive patients, 536 (51.0%) were in sinus rhythm, 464 with a measurable P wave are the focus of this study. Two-hundred and sixty patients (56.0%) had normal atrial conduction, 95 (20.5%) partial IAB, and 109 (23.5%) advanced IAB. During a mean follow-up of 4.5 ± 2.1 years, 235 patients experienced all-cause death, new-onset AF, or stroke. In multivariable comprehensive Cox regression analyses, advanced IAB was associated with new-onset AF (HR 2.71 [1.61-4.56], P < 0.001), ischemic stroke (HR 3.02 [1.07-8.53], P = 0.04), and the composite of both (HR 2.42 [1.41-4.15], P < 0.001). In patients with HF advanced IAB predicts new-onset AF and ischemic stroke. Future studies must assess whether anticoagulant treatment in Bayes' Syndrome leads to better outcomes in HF. Copyright © 2017 Elsevier B.V. All rights reserved.

An Examination of Postsecondary Faculty and the Extent of Critical Reading Taught in 100-Level Introductory Biology and American History Courses in Publicly Funded Two-Year and Four-Year Pennsylvania Institutions

NASA Astrophysics Data System (ADS)

Sand, Dianna

This research examined the responses of postsecondary faculty on a critical reading inventory. The research is quantitative, non-experimental, and incorporates a multiple regression model in the analyses. Three research questions guided this study: (1) By institution type: To what degree does institution type predict the extent to which postsecondary faculty teach critical reading as measured by the Reading Goals Inventory (Jones, 1996)? (2) By faculty status: To what degree does faculty status predict the extent to which postsecondary faculty teach critical reading as measured by the Reading Goals Inventory (Jones, 1996)? (3) By disciplinary area: To what degree does disciplinary area predict the extent to which postsecondary faculty teach critical reading as measured by the Reading Goals Inventory (Jones, 1996)? Faculty from 28 Pennsylvania postsecondary institutions participated in this study. Faculty respondents taught 100-level introductory biology or American history courses either part-time or full-time at Pennsylvania community colleges or Pennsylvania State System of Higher Education (PASSHE) universities. Fifty-four faculty respondents completed the Reading Goals Inventory (Jones, 1996). The researcher conducted multiple regression analyses using a hierarchical method. Predictor variables included Institution Type, Faculty Status, and Disciplinary Area; criterion or outcome variables included seven sub-scales of the critical reading inventory. In this study, Institution Type and Faculty Status were not significant predictors. Disciplinary Area was a consistent significant predictor of the amount of critical reading taught as measured in the Interpretation, Analysis, Evaluation, and Reflection sub-scales of the Reading Goals Inventory (Jones, 1996).

The role of early language abilities on math skills among Chinese children.

PubMed

Zhang, Juan; Fan, Xitao; Cheung, Sum Kwing; Meng, Yaxuan; Cai, Zhihui; Hu, Bi Ying

2017-01-01

The present study investigated the role of early language abilities in the development of math skills among Chinese K-3 students. About 2000 children in China, who were on average aged 6 years, were assessed for both informal math (e.g., basic number concepts such as counting objects) and formal math (calculations including addition and subtraction) skills, language abilities and nonverbal intelligence. Correlation analysis showed that language abilities were more strongly associated with informal than formal math skills, and regression analyses revealed that children's language abilities could uniquely predict both informal and formal math skills with age, gender, and nonverbal intelligence controlled. Mediation analyses demonstrated that the relationship between children's language abilities and formal math skills was partially mediated by informal math skills. The current findings indicate 1) Children's language abilities are of strong predictive values for both informal and formal math skills; 2) Language abilities impacts formal math skills partially through the mediation of informal math skills.

The role of early language abilities on math skills among Chinese children

PubMed Central

Fan, Xitao; Cheung, Sum Kwing; Cai, Zhihui; Hu, Bi Ying

2017-01-01

Background The present study investigated the role of early language abilities in the development of math skills among Chinese K-3 students. About 2000 children in China, who were on average aged 6 years, were assessed for both informal math (e.g., basic number concepts such as counting objects) and formal math (calculations including addition and subtraction) skills, language abilities and nonverbal intelligence. Methodology Correlation analysis showed that language abilities were more strongly associated with informal than formal math skills, and regression analyses revealed that children’s language abilities could uniquely predict both informal and formal math skills with age, gender, and nonverbal intelligence controlled. Mediation analyses demonstrated that the relationship between children’s language abilities and formal math skills was partially mediated by informal math skills. Results The current findings indicate 1) Children’s language abilities are of strong predictive values for both informal and formal math skills; 2) Language abilities impacts formal math skills partially through the mediation of informal math skills. PMID:28749950

Childhood socioeconomic status and childhood maltreatment: Distinct associations with brain structure

PubMed Central

Lawson, Gwendolyn M.; Camins, Joshua S.; Wisse, Laura; Wu, Jue; Duda, Jeffrey T.; Cook, Philip A.; Gee, James C.; Farah, Martha J.

2017-01-01

The present study examined the relationship between childhood socioeconomic status (SES), childhood maltreatment, and the volumes of the hippocampus and amygdala between the ages of 25 and 36 years. Previous work has linked both low SES and maltreatment with reduced hippocampal volume in childhood, an effect attributed to childhood stress. In 46 adult subjects, only childhood maltreatment, and not childhood SES, predicted hippocampal volume in regression analyses, with greater maltreatment associated with lower volume. Neither factor was related to amygdala volume. When current SES and recent interpersonal stressful events were also considered, recent interpersonal stressful events predicted smaller hippocampal volumes over and above childhood maltreatment. Finally, exploratory analyses revealed a significant sex by childhood SES interaction, with women’s childhood SES showing a significantly more positive relation (less negative) with hippocampus volume than men’s. The overall effect of childhood maltreatment but not SES, and the sex-specific effect of childhood SES, indicate that different forms of stressful childhood adversity affect brain development differently. PMID:28414755

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Doing the counter-regulation shuffle: The importance of flexibility and hunger for predicting food consumption following a preload.

PubMed

Broadbent, Jaclyn; Fuller-Tyszkiewicz, Matthew; Dennerstein, Michelle; Greenwood, Jesse; Hancock, Naomi; Thavapalan, Nithyyaa; White, Melissa

This study utilised the preload paradigm to evaluate whether trait-like dieting attitudes and behaviours (dietary restraint and flexibility in dieting rules) and context-specific factors (negative mood and hunger) predict food consumption among male and female participants. Following a high calorie preload, 79 participants aged 18-40 completed a deceptive taste test in which they were encouraged to eat as much of the taste test foods as desired, and this ad libitum intake was measured. Although each predictor (except negative mood) predicted consumption when tested individually, regression analyses revealed that dieting flexibility and current hunger were the strongest unique predictors of intake. Mood failed to directly predict food consumption, nor did it moderate the relationship between restraint and food intake. Collectively, findings suggest that emphasis on dietary restraint in preload studies may be misplaced, as other proximal and stable factors may better predict food consumption. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Predictors of Program Use and Child and Parent Outcomes of A Brief Online Parenting Intervention.

PubMed

Baker, Sabine; Sanders, Matthew R

2017-10-01

Web-based parenting interventions have the potential to increase the currently low reach of parenting programs, but few evidence-based online programs are available, and little is known about who benefits from this delivery format. This study investigated if improvements in child behavior and parenting, following participation in a brief online parenting program (Triple P Online Brief), can be predicted by family and program-related factors. Participants were 100 parents of 2-9-year-old children displaying disruptive behavior problems. Regression analyses showed that higher baseline levels of child behavior problems, older parental age and more intense conflict over parenting pre-intervention predicted greater improvement in child behavior at 9-month follow-up. Improvement in parenting was predicted by higher pre-intervention levels of ineffective parenting. Family demographics, parental adjustment and program related factors did not predict treatment outcomes. Younger child age and lower disagreement over parenting pre-intervention predicted completion of the recommended minimum dose of the program.

Work and women's well-being: religion and age as moderators.

PubMed

Noor, Noraini M

2008-12-01

Religion has been found to moderate the stress-strain relationship. This moderator role, however, may be dependent on age. The present study tested for the three-way interaction between work experience, age, and religiosity in the prediction of women's well-being, and predicted that work experience and religiosity will combine additively in older women, while in younger women religiosity is predicted to moderate the relationship between work experience and well-being. In a sample of 389 married Malay Muslim women, results of the regression analyses showed significant three-way interactions between work experience, age, and religiosity in the prediction of well-being (measured by distress symptoms and life satisfaction). While in younger women the results were in line with the predictions made, in the older women, both additive and moderator effects of religiosity were observed, depending on the well-being measures used. These results are discussed in relation to the literature on work and family, with specific reference to women's age, religion, as well as the issue of stress-strain specificity.

Trajectories of change in symptom distress in a clinical group of late adolescents: The role of maladaptive personality traits and relations with parents.

PubMed

Koster, Nagila; Laceulle, Odilia; van der Heijden, Paul; de Clercq, Barbara; van Aken, Marcel

2018-03-25

In this study, it was analysed whether trajectories of change in symptom distress could be identified in a clinical group of late adolescents with personality pathology. Furthermore, it was examined whether maladaptive personality traits and relations with parents were predictive of following one of these trajectories. Three latent classes emerged from growth mixture modelling with a symptom inventory (n = 911): a Stable High, a Strong Decreasing and a Moderate Decreasing trajectory. Subsequently, by using multinomial logistic regression analyses in a subsample of late-adolescents (n = 127), it was revealed that high levels of Negative Affectivity and Detachment were predictive of following the Strong Decreasing, and high levels of Detachment were predictive of following the Stable High trajectory. Support from or Negative Interactions with parents were not predictive of any of the trajectories. The current results contribute to the notion of individual trajectories of change in symptom distress and provide suggestions for screening patients on personality traits to gain insight in the course of this change. © 2018 The Authors Personality and Mental Health Published by John Wiley & Sons Ltd. © 2018 The Authors Personality and Mental Health Published by John Wiley & Sons Ltd.

Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

PubMed Central

Genders, Tessa S. S.; Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; van Mieghem, Carlos A. G.; de Feyter, Pim J.

2010-01-01

Objectives: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. PMID:20559838

High serum uric acid concentration predicts poor survival in patients with breast cancer.

PubMed

Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

2017-10-01

Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Tract-specific fractional anisotropy predicts cognitive outcome in a community sample of middle-aged participants with white matter lesions

PubMed Central

Soriano-Raya, Juan José; Miralbell, Júlia; López-Cancio, Elena; Bargalló, Núria; Arenillas, Juan Francisco; Barrios, Maite; Cáceres, Cynthia; Toran, Pere; Alzamora, Maite; Dávalos, Antoni; Mataró, Maria

2014-01-01

Cerebral white matter lesions (WMLs) have been consistently related to cognitive dysfunction but the role of white matter (WM) damage in cognitive impairment is not fully determined. Diffusion tensor imaging is a promising tool to explain impaired cognition related to WMLs. We investigated the separate association of high-grade periventricular hyperintensities (PVHs) and deep white matter hyperintensities (DWMHs) with fractional anisotropy (FA) in middle-aged individuals. We also assessed the predictive value to cognition of FA within specific WM tracts associated with high-grade WMLs. One hundred participants from the Barcelona-AsIA Neuropsychology Study were divided into groups based on low- and high-grade WMLs. Voxel-by-voxel FA were compared between groups, with separate analyses for high-grade PVHs and DWMHs. The mean FA within areas showing differences between groups was extracted in each tract for linear regression analyses. Participants with high-grade PVHs and participants with high-grade DWMHs showed lower FA in different areas of specific tracts. Areas showing decreased FA in high-grade DWMHs predicted lower cognition, whereas areas with decreased FA in high-grade PVHs did not. The predictive value to cognition of specific WM tracts supports the involvement of cortico-subcortical circuits in cognitive deficits only in DWMHs. PMID:24549185

Tract-specific fractional anisotropy predicts cognitive outcome in a community sample of middle-aged participants with white matter lesions.

PubMed

Soriano-Raya, Juan José; Miralbell, Júlia; López-Cancio, Elena; Bargalló, Núria; Arenillas, Juan Francisco; Barrios, Maite; Cáceres, Cynthia; Toran, Pere; Alzamora, Maite; Dávalos, Antoni; Mataró, Maria

2014-05-01

Cerebral white matter lesions (WMLs) have been consistently related to cognitive dysfunction but the role of white matter (WM) damage in cognitive impairment is not fully determined. Diffusion tensor imaging is a promising tool to explain impaired cognition related to WMLs. We investigated the separate association of high-grade periventricular hyperintensities (PVHs) and deep white matter hyperintensities (DWMHs) with fractional anisotropy (FA) in middle-aged individuals. We also assessed the predictive value to cognition of FA within specific WM tracts associated with high-grade WMLs. One hundred participants from the Barcelona-AsIA Neuropsychology Study were divided into groups based on low- and high-grade WMLs. Voxel-by-voxel FA were compared between groups, with separate analyses for high-grade PVHs and DWMHs. The mean FA within areas showing differences between groups was extracted in each tract for linear regression analyses. Participants with high-grade PVHs and participants with high-grade DWMHs showed lower FA in different areas of specific tracts. Areas showing decreased FA in high-grade DWMHs predicted lower cognition, whereas areas with decreased FA in high-grade PVHs did not. The predictive value to cognition of specific WM tracts supports the involvement of cortico-subcortical circuits in cognitive deficits only in DWMHs.

Prediction equations of forced oscillation technique: the insidious role of collinearity.

PubMed

Narchi, Hassib; AlBlooshi, Afaf

2018-03-27

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

Comparison of patient centeredness of visits to emergency departments, physicians, and dentists for dental problems and injuries.

PubMed

Cohen, Leonard A; Bonito, Arthur J; Eicheldinger, Celia; Manski, Richard J; Macek, Mark D; Edwards, Robert R; Khanna, Niharika

2010-01-01

Patient-centered care has a positive impact on patient health status. This report compares patient assessments of patient centeredness during treatment in hospital emergency departments (EDs) and physician and dentist offices for dental problems and injuries. Participants included low-income White, Black, and Hispanic adults who had experienced a dental problem or injury during the previous 12 months and who visited an emergency department, physician, or dentist for treatment. A stratified random sample of Maryland households participated in a cross-sectional telephone survey. Interviews were completed with 94.8% (401/423) of eligible individuals. Multivariable logistic regression analyses were performed. The measure of predictive power, the pseudo-R2s, calculated for the logistic regression models ranged from 12% to 18% for the analyses of responses to the measures of patient centeredness (satisfaction with treatment, careful listening, thorough explaining, spending enough time, and treated with courtesy and respect). EDs were less likely than dentists to treat patients with great courtesy and respect. Further research is needed to identify factors that support patient-centered care.

Triglyceride and glucose (TyG) index as a predictor of incident hypertension: a 9-year longitudinal population-based study.

PubMed

Zheng, Rongjiong; Mao, Yushan

2017-09-13

Hypertension and the triglyceride and glucose index both have been associated with insulin resistance; however, the longitudinal association remains unclear. This study was designed to investigate the longitudinal association between the triglyceride and glucose index and incident hypertension among the Chinese population. We studied 4686 subjects (3177 males and 1509 females) and followed up for 9 years. The subjects were divided into four groups based on the triglyceride and glucose index. Univariate and multivariate Cox regression models were used to analyse the risk factors of hypertension. After 9 years of follow-up, 2047 subjects developed hypertension. The overall 9-year cumulative incidence of hypertension was 43.7%, ranging from 28.5% in quartile 1 to 36.9% in quartile 2, 49.2% in quartile 3 and 59.8% in quartile 4 (p for trend < 0.001). Cox regression analyses indicated that higher triglyceride and glucose index was associated with an increased risk of subsequent incident hypertension. The triglyceride and glucose index can predict the incident hypertension among the Chinese population.

Demand analysis of flood insurance by using logistic regression model and genetic algorithm

NASA Astrophysics Data System (ADS)

Sidi, P.; Mamat, M. B.; Sukono; Supian, S.; Putra, A. S.

2018-03-01

Citarum River floods in the area of South Bandung Indonesia, often resulting damage to some buildings belonging to the people living in the vicinity. One effort to alleviate the risk of building damage is to have flood insurance. The main obstacle is not all people in the Citarum basin decide to buy flood insurance. In this paper, we intend to analyse the decision to buy flood insurance. It is assumed that there are eight variables that influence the decision of purchasing flood assurance, include: income level, education level, house distance with river, building election with road, flood frequency experience, flood prediction, perception on insurance company, and perception towards government effort in handling flood. The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. The results of the analysis shows that eight variables analysed significantly influence the demand of flood insurance. These results are expected to be considered for insurance companies, to influence the decision of the community to be willing to buy flood insurance.

An experimental and theoretical study to relate uncommon rock/fluid properties to oil recovery. Final report

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

Watson, R.

Waterflooding is the most commonly used secondary oil recovery technique. One of the requirements for understanding waterflood performance is a good knowledge of the basic properties of the reservoir rocks. This study is aimed at correlating rock-pore characteristics to oil recovery from various reservoir rock types and incorporating these properties into empirical models for Predicting oil recovery. For that reason, this report deals with the analyses and interpretation of experimental data collected from core floods and correlated against measurements of absolute permeability, porosity. wettability index, mercury porosimetry properties and irreducible water saturation. The results of the radial-core the radial-core andmore » linear-core flow investigations and the other associated experimental analyses are presented and incorporated into empirical models to improve the predictions of oil recovery resulting from waterflooding, for sandstone and limestone reservoirs. For the radial-core case, the standardized regression model selected, based on a subset of the variables, predicted oil recovery by waterflooding with a standard deviation of 7%. For the linear-core case, separate models are developed using common, uncommon and combination of both types of rock properties. It was observed that residual oil saturation and oil recovery are better predicted with the inclusion of both common and uncommon rock/fluid properties into the predictive models.« less

Using the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey to Predict the Occurrence of Short-Term Coronary Heart Disease Events in Women.

PubMed

McSweeney, Jean C; Cleves, Mario A; Fischer, Ellen P; Pettey, Christina M; Beasley, Brittany

Few instruments capture symptoms that predict cardiac events in the short-term. This study examines the ability of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey to predict acute cardiac events within 3 months of administration and to identify the prodromal symptoms most associated with short-term risk in women without known coronary heart disease. The McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey was administered to 1,097 women referred to a cardiologist for initial coronary heart disease evaluation. Logistic regression models were used to examine prodromal symptoms individually and in combination to identify the subset of symptoms most predictive of an event within 3 months. Fifty-one women had an early cardiac event. In bivariate analyses, 4 of 30 prodromal symptoms were significantly associated with event occurrence within 90 days. In adjusted analyses, women reporting arm pain or discomfort and unusual fatigue were more likely (OR, 4.67; 95% CI, 2.08-10.48) to have a cardiac event than women reporting neither. The McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey may assist in predicting short-term coronary heart disease events in women without known coronary heart disease. Copyright © 2017 Jacobs Institute of Women's Health. All rights reserved.

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

Measurement of DSM-5 section II personality disorder constructs using the MMPI-2-RF in clinical and forensic samples.

PubMed

Anderson, Jaime L; Sellbom, Martin; Pymont, Carly; Smid, Wineke; De Saeger, Hilde; Kamphuis, Jan H

2015-09-01

In the current study, we evaluated the associations between the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) scale scores and the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) Section II personality disorder (PD) criterion counts in inpatient and forensic psychiatric samples from The Netherlands using structured clinical interviews to operationalize PDs. The inpatient psychiatric sample included 190 male and female patients and the forensic sample included 162 male psychiatric patients. We conducted correlation and count regression analyses to evaluate the utility of relevant MMPI-2-RF scales in predicting PD criterion count scores. Generally, results from these analyses emerged as conceptually expected and provided evidence that MMPI-2-RF scales can be useful in assessing PDs. At the zero-order level, most hypothesized associations between Section II disorders and MMPI-2-RF scales were supported. Similarly, in the regression analyses, a unique set of predictors emerged for each PD that was generally in line with conceptual expectations. Additionally, the results provided general evidence that PDs can be captured by dimensional psychopathology constructs, which has implications for both DSM-5 Section III specifically and the personality psychopathology literature more broadly. (c) 2015 APA, all rights reserved.

Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data

PubMed Central

Carrara, Marta; Baselli, Giuseppe; Ferrario, Manuela

2015-01-01

We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients. PMID:26557154

Antecedents of eating disorders and muscle dysmorphia in a non-clinical sample.

PubMed

Lamanna, J; Grieve, F G; Derryberry, W Pitt; Hakman, M; McClure, A

2010-01-01

Muscle Dysmorphia (MD) has recently been conceptualized as the male form of Eating Disorders (ED); although, it is not currently classified as an ED. The current study compares etiological models of MD symptomatology and ED symptomatology. It was hypothesized that sociocultural influences on appearance (SIA) would predict body dissatisfaction (BD), and that this relationship would be mediated by self-esteem (SE) and perfectionism (P); that BD would predict negative affect (NA); and that NA would predict MD and ED symptomatology. Two-hundred-forty-seven female and 101 male college students at a midsouth university completed the study. All participants completed measures assessing each of the constructs, and multiple regression analyses were conducted to test each model's fit. In both models, most predictor paths were significant. These results suggest similarity in symptomatology and etiological models between ED and MD.

Coach/player relationships in tennis.

PubMed

Prapavessis, H; Gordon, S

1991-09-01

The present study examined the variables that predict coach/athlete compatibility. Compatibility among a sample of 52 elite tennis coach/player dyads was assessed using a sport adapted version of Schutz's (1966) Fundamental Interpersonal Relations Orientation-Behaviour (FIRO-B), a sport adapted version of Fiedler's (1967) Least Preferred Co-worker scale (LPC), and Chelladurai and Saleh's (1980) Leadership Scale for Sport (LSS). Self-ratings of the quality of the interaction were obtained from both coach and athlete. Multiple-regression analyses using self-rating scores as the dependent measure were carried out to determine which variables best predicted the degree of compatibility. The sole inventory that significantly predicted compatibility was the LSS. More specifically, the discrepancy between the athlete's preferences and perceptions on the autocratic dimension was the best predictor. Implications for tennis coaches and recommendations for future research in this area are discussed.

Strategies Used in Coping With a Cancer Diagnosis Predict Meaning in Life for Survivors

PubMed Central

Jim, Heather S.; Richardson, Susan A.; Golden-Kreutz, Deanna M.; Andersen, Barbara L.

2007-01-01

The search for meaning in life is part of the human experience. A negative life event may threaten perceptions about meaning in life, such as the benevolence of the world and one’s sense of harmony and peace. The authors examined the longitudinal relationship between women’s coping with a diagnosis of breast cancer and their self-reported meaning in life 2 years later. Multiple regression analyses revealed that positive strategies for coping predicted significant variance in the sense of meaning in life—feelings of inner peace, satisfaction with one’s current life and the future, and spirituality and faith—and the absence of such strategies predicted reports of loss of meaning and confusion (ps < .01). The importance and process of finding meaning in the context of a life stressor are discussed. PMID:17100503

Future-oriented emotions in the prediction of binge-drinking intention and expectation: the role of anticipated and anticipatory emotions.

PubMed

Carrera, Pilar; Caballero, Amparo; Muñoz, Dolores

2012-06-01

The Theory of Planned Behavior (TPB) offers a parsimonious explanation of purposive behavior, but in the study of healthy and risk behaviors its sufficiency may be questioned. Working with binge-drinking, a very common risk behavior in Spanish undergraduate students, we used two strategies for improving predictions from TPB: using behavioral intention (BI) and behavioral expectation (BE) as proximal antecedents of behaviors and adding as new predictors two future-oriented emotions (anticipated and anticipatory). Hierarchical regression analyses show that while anticipated emotions improved TPB explanations of BI, anticipatory emotions improved the explanations of BE. The present results show the influence of future emotions in the prediction of behavioral intention and behavioral expectation. © 2012 The Authors. Scandinavian Journal of Psychology © 2012 The Scandinavian Psychological Associations.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

Predicting the Risk of Breakthrough Urinary Tract Infections: Primary Vesicoureteral Reflux.

PubMed

Hidas, Guy; Billimek, John; Nam, Alexander; Soltani, Tandis; Kelly, Maryellen S; Selby, Blake; Dorgalli, Crystal; Wehbi, Elias; McAleer, Irene; McLorie, Gordon; Greenfield, Sheldon; Kaplan, Sherrie H; Khoury, Antoine E

2015-11-01

We constructed a risk prediction instrument stratifying patients with primary vesicoureteral reflux into groups according to their 2-year probability of breakthrough urinary tract infection. Demographic and clinical information was retrospectively collected in children diagnosed with primary vesicoureteral reflux and followed for 2 years. Bivariate and binary logistic regression analyses were performed to identify factors associated with breakthrough urinary tract infection. The final regression model was used to compute an estimation of the 2-year probability of breakthrough urinary tract infection for each subject. Accuracy of the binary classifier for breakthrough urinary tract infection was evaluated using receiver operator curve analysis. Three distinct risk groups were identified. The model was then validated in a prospective cohort. A total of 252 bivariate analyses showed that high grade (IV or V) vesicoureteral reflux (OR 9.4, 95% CI 3.8-23.5, p <0.001), presentation after urinary tract infection (OR 5.3, 95% CI 1.1-24.7, p = 0.034) and female gender (OR 2.6, 95% CI 0.097-7.11, p <0.054) were important risk factors for breakthrough urinary tract infection. Subgroup analysis revealed bladder and bowel dysfunction was a significant risk factor more pronounced in low grade (I to III) vesicoureteral reflux (OR 2.8, p = 0.018). The estimation model was applied for prospective validation, which demonstrated predicted vs actual 2-year breakthrough urinary tract infection rates of 19% vs 21%. Stratifying the patients into 3 risk groups based on parameters in the risk model showed 2-year risk for breakthrough urinary tract infection was 8.6%, 26.0% and 62.5% in the low, intermediate and high risk groups, respectively. This proposed risk stratification and probability model allows prediction of 2-year risk of patient breakthrough urinary tract infection to better inform parents of possible outcomes and treatment strategies. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Group differences in anterior hippocampal volume and in the retrieval of spatial and temporal context memory in healthy young versus older adults.

PubMed

Rajah, M Natasha; Kromas, Michelle; Han, Jung Eun; Pruessner, Jens C

2010-12-01

The ability to retrieve temporal and spatial context information from memory declines with healthy aging. The hippocampus (HC) has been shown to be associated with successful encoding and retrieval of spatio-temporal context, versus item recognition information (Davachi, Mitchell, & Wagner, 2003; Nadel, Samsonovich, Ryan, & Moscovitch, 2000; Ross & Slotnick, 2008). Aging has been linked to volume reduction in the HC (Bouchard, Malykhin, Martin, Hanstock, Emery, Fisher, & Camicioli, 2008; Malykhin, Bouchard, Camicioli, & Coupland, 2008; Raz et al., 2005). As such, age-associated reductions in anterior HC volume may contribute to the context memory deficits observed in older adults. In the current MRI study we investigated whether item recognition, spatial context and temporal context memory performance would be predicted by regional volumes in HC head (HH), body (HB) and tail (HT) volumes, using within group multiple regression analyses in a sample of 19 healthy young (mean age 24.3) and 20 older adults (mean age 67.7). We further examined between age-group differences in the volumes of the same HC sub-regions. Multiple regression analyses revealed that in younger adults both spatial and temporal context retrieval performance was predicted by anterior HC volume. Older age was associated with significant volume reductions in HH and HB, but not HT; and with reduced ability to retrieve spatial and temporal contextual details from episodic memory. However, HC volumes did not predict context retrieval performance in older adults. We conclude that individual differences in anterior, not posterior, HC volumes predict context memory performance in young adults. With age there may be a posterior-to-anterior shift from using HC-related processes, due to HC volume loss, to employing the prefrontal cortex to aid in the performance of cognitively demanding context memory tasks. However, due to concomitant changes in the prefrontal system with age, there are limits to compensation in the aging brain. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Derivation and validation of in-hospital mortality prediction models in ischaemic stroke patients using administrative data.

PubMed

Lee, Jason; Morishima, Toshitaka; Kunisawa, Susumu; Sasaki, Noriko; Otsubo, Tetsuya; Ikai, Hiroshi; Imanaka, Yuichi

2013-01-01

Stroke and other cerebrovascular diseases are a major cause of death and disability. Predicting in-hospital mortality in ischaemic stroke patients can help to identify high-risk patients and guide treatment approaches. Chart reviews provide important clinical information for mortality prediction, but are laborious and limiting in sample sizes. Administrative data allow for large-scale multi-institutional analyses but lack the necessary clinical information for outcome research. However, administrative claims data in Japan has seen the recent inclusion of patient consciousness and disability information, which may allow more accurate mortality prediction using administrative data alone. The aim of this study was to derive and validate models to predict in-hospital mortality in patients admitted for ischaemic stroke using administrative data. The sample consisted of 21,445 patients from 176 Japanese hospitals, who were randomly divided into derivation and validation subgroups. Multivariable logistic regression models were developed using 7- and 30-day and overall in-hospital mortality as dependent variables. Independent variables included patient age, sex, comorbidities upon admission, Japan Coma Scale (JCS) score, Barthel Index score, modified Rankin Scale (mRS) score, and admissions after hours and on weekends/public holidays. Models were developed in the derivation subgroup, and coefficients from these models were applied to the validation subgroup. Predictive ability was analysed using C-statistics; calibration was evaluated with Hosmer-Lemeshow χ(2) tests. All three models showed predictive abilities similar or surpassing that of chart review-based models. The C-statistics were highest in the 7-day in-hospital mortality prediction model, at 0.906 and 0.901 in the derivation and validation subgroups, respectively. For the 30-day in-hospital mortality prediction models, the C-statistics for the derivation and validation subgroups were 0.893 and 0.872, respectively; in overall in-hospital mortality prediction these values were 0.883 and 0.876. In this study, we have derived and validated in-hospital mortality prediction models for three different time spans using a large population of ischaemic stroke patients in a multi-institutional analysis. The recent inclusion of JCS, Barthel Index, and mRS scores in Japanese administrative data has allowed the prediction of in-hospital mortality with accuracy comparable to that of chart review analyses. The models developed using administrative data had consistently high predictive abilities for all models in both the derivation and validation subgroups. These results have implications in the role of administrative data in future mortality prediction analyses. Copyright © 2013 S. Karger AG, Basel.

Flexible Meta-Regression to Assess the Shape of the Benzene–Leukemia Exposure–Response Curve

PubMed Central

Vlaanderen, Jelle; Portengen, Lützen; Rothman, Nathaniel; Lan, Qing; Kromhout, Hans; Vermeulen, Roel

2010-01-01

Background Previous evaluations of the shape of the benzene–leukemia exposure–response curve (ERC) were based on a single set or on small sets of human occupational studies. Integrating evidence from all available studies that are of sufficient quality combined with flexible meta-regression models is likely to provide better insight into the functional relation between benzene exposure and risk of leukemia. Objectives We used natural splines in a flexible meta-regression method to assess the shape of the benzene–leukemia ERC. Methods We fitted meta-regression models to 30 aggregated risk estimates extracted from nine human observational studies and performed sensitivity analyses to assess the impact of a priori assessed study characteristics on the predicted ERC. Results The natural spline showed a supralinear shape at cumulative exposures less than 100 ppm-years, although this model fitted the data only marginally better than a linear model (p = 0.06). Stratification based on study design and jackknifing indicated that the cohort studies had a considerable impact on the shape of the ERC at high exposure levels (> 100 ppm-years) but that predicted risks for the low exposure range (< 50 ppm-years) were robust. Conclusions Although limited by the small number of studies and the large heterogeneity between studies, the inclusion of all studies of sufficient quality combined with a flexible meta-regression method provides the most comprehensive evaluation of the benzene–leukemia ERC to date. The natural spline based on all data indicates a significantly increased risk of leukemia [relative risk (RR) = 1.14; 95% confidence interval (CI), 1.04–1.26] at an exposure level as low as 10 ppm-years. PMID:20064779

Toward Malaria Risk Prediction in Afghanistan Using Remote Sensing

NASA Technical Reports Server (NTRS)

Safi, N.; Adimi, F.; Soebiyanto, R. P.; Kiang, R. K.

2010-01-01

Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the average provincial accuracy reaches 91%. The developed predictive and early warning capabilities support the Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan.

Development of prediction equations for estimating appendicular skeletal muscle mass in Japanese men and women.

PubMed

Furushima, Taishi; Miyachi, Motohiko; Iemitsu, Motoyuki; Murakami, Haruka; Kawano, Hiroshi; Gando, Yuko; Kawakami, Ryoko; Sanada, Kiyoshi

2017-08-29

This study aimed to develop and cross-validate prediction equations for estimating appendicular skeletal muscle mass (ASM) and to examine the relationship between sarcopenia defined by the prediction equations and risk factors for cardiovascular diseases (CVD) or osteoporosis in Japanese men and women. Subjects were healthy men and women aged 20-90 years, who were randomly allocated to the following two groups: the development group (D group; 257 men, 913 women) and the cross-validation group (V group; 119 men, 112 women). To develop prediction equations, stepwise multiple regression analyses were performed on data obtained from the D group, using ASM measured by dual-energy X-ray absorptiometry (DXA) as a dependent variable and five easily obtainable measures (age, height, weight, waist circumference, and handgrip strength) as independent variables. When the prediction equations for ASM estimation were applied to the V group, a significant correlation was found between DXA-measured ASM and predicted ASM in both men and women (R 2  = 0.81 and R 2  = 0.72). Our prediction equations had higher R 2 values compared to previously developed equations (R 2  = 0.75-0.59 and R 2  = 0.69-0.40) in both men and women. Moreover, sarcopenia defined by predicted ASM was related to risk factors for osteoporosis and CVD, as well as sarcopenia defined by DXA-measured ASM. In this study, novel prediction equations were developed and cross-validated in Japanese men and women. Our analyses validated the clinical significance of these prediction equations and showed that previously reported equations were not applicable in a Japanese population.

Predictors of regular cigarette smoking among adolescent females: Does body image matter?

PubMed Central

Kaufman, Annette R.; Augustson, Erik M.

2013-01-01

This study examined how factors associated with body image predict regular smoking in adolescent females. Data were from the National Longitudinal Study of Adolescent Health (Add Health), a study of health-related behaviors in a nationally representative sample of adolescents in grades 7 through 12. Females in Waves I and II (n=6,956) were used for this study. Using SUDAAN to adjust for the sampling frame, univariate and multivariate analyses were performed to investigate if baseline body image factors, including perceived weight, perceived physical development, trying to lose weight, and self-esteem, were predictive of regular smoking status 1 year later. In univariate analyses, perceived weight (p<.01), perceived physical development (p<.0001), trying to lose weight (p<.05), and self-esteem (p<.0001) significantly predicted regular smoking 1 year later. In the logistic regression model, perceived physical development (p<.05), and self-esteem (p<.001) significantly predicted regular smoking. The more developed a female reported being in comparison to other females her age, the more likely she was to be a regular smoker. Lower self-esteem was predictive of regular smoking. Perceived weight and trying to lose weight failed to reach statistical significance in the multivariate model. This current study highlights the importance of perceived physical development and self-esteem when predicting regular smoking in adolescent females. Efforts to promote positive self-esteem in young females may be an important strategy when creating interventions to reduce regular cigarette smoking. PMID:18686177

The arcsine is asinine: the analysis of proportions in ecology.

PubMed

Warton, David I; Hui, Francis K C

2011-01-01

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

A psycholinguistic database for traditional Chinese character naming.

PubMed

Chang, Ya-Ning; Hsu, Chun-Hsien; Tsai, Jie-Li; Chen, Chien-Liang; Lee, Chia-Ying

2016-03-01

In this study, we aimed to provide a large-scale set of psycholinguistic norms for 3,314 traditional Chinese characters, along with their naming reaction times (RTs), collected from 140 Chinese speakers. The lexical and semantic variables in the database include frequency, regularity, familiarity, consistency, number of strokes, homophone density, semantic ambiguity rating, phonetic combinability, semantic combinability, and the number of disyllabic compound words formed by a character. Multiple regression analyses were conducted to examine the predictive powers of these variables for the naming RTs. The results demonstrated that these variables could account for a significant portion of variance (55.8%) in the naming RTs. An additional multiple regression analysis was conducted to demonstrate the effects of consistency and character frequency. Overall, the regression results were consistent with the findings of previous studies on Chinese character naming. This database should be useful for research into Chinese language processing, Chinese education, or cross-linguistic comparisons. The database can be accessed via an online inquiry system (http://ball.ling.sinica.edu.tw/namingdatabase/index.html).

Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women.

PubMed

Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2016-03-01

Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007-2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman's r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen's kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements.

Predicting suicide attempts with the SAD PERSONS scale: a longitudinal analysis.

PubMed

Bolton, James M; Spiwak, Rae; Sareen, Jitender

2012-06-01

The SAD PERSONS scale is a widely used risk assessment tool for suicidal behavior despite a paucity of supporting data. The objective of this study was to examine the ability of the scale in predicting suicide attempts. Participants consisted of consecutive referrals (N=4,019) over 2 years (January 1, 2009 to December 31, 2010) to psychiatric services in the emergency departments of the 2 largest tertiary care hospitals in the province of Manitoba, Canada. SAD PERSONS and Modified SAD PERSONS (MSPS) scale scores were recorded for individuals at their index and all subsequent presentations. The 2 main outcome measures in the study included current suicide attempts (at index presentation) and future suicide attempts (within the next 6 months). The ability of the scales to predict suicide attempts was evaluated with logistic regression, sensitivity and specificity analyses, and receiver operating characteristic curves. 566 people presented with suicide attempts (14.1% of the sample). Both SAD PERSONS and MSPS showed poor predictive ability for future suicide attempts. Compared to low risk scores, high risk baseline scores had low sensitivity (19.6% and 40.0%, respectively) and low positive predictive value (5.3% and 7.4%, respectively). SAD PERSONS did not predict suicide attempts better than chance (area under the curve =0.572; 95% confidence interval [CI], 0.51-0.64; P value nonsignificant). Stepwise regression identified 5 original scale items that accounted for the greatest proportion of future suicide attempt variance. High risk scores using this model had high sensitivity (93.5%) and were associated with a 5-fold higher likelihood of future suicide attempt presentation (odds ratio =5.58; 95% CI, 2.24-13.86; P<.001). In their current form, SAD PERSONS and MSPS do not accurately predict future suicide attempts. © Copyright 2012 Physicians Postgraduate Press, Inc.

Late-Life Depressive Symptoms and Lifetime History of Major Depression: Cognitive Deficits are Largely Due to Incipient Dementia rather than Depression.

PubMed

Heser, Kathrin; Bleckwenn, Markus; Wiese, Birgitt; Mamone, Silke; Riedel-Heller, Steffi G; Stein, Janine; Lühmann, Dagmar; Posselt, Tina; Fuchs, Angela; Pentzek, Michael; Weyerer, Siegfried; Werle, Jochen; Weeg, Dagmar; Bickel, Horst; Brettschneider, Christian; König, Hans-Helmut; Maier, Wolfgang; Scherer, Martin; Wagner, Michael

2016-08-01

Late-life depression is frequently accompanied by cognitive impairments. Whether these impairments indicate a prodromal state of dementia, or are a symptomatic expression of depression per se is not well-studied. In a cohort of very old initially non-demented primary care patients (n = 2,709, mean age = 81.1 y), cognitive performance was compared between groups of participants with or without elevated depressive symptoms and with or without subsequent dementia using ANCOVA (adjusted for age, sex, and education). Logistic regression analyses were computed to predict subsequent dementia over up to six years of follow-up. The same analytical approach was performed for lifetime major depression. Participants with elevated depressive symptoms without subsequent dementia showed only small to medium cognitive deficits. In contrast, participants with depressive symptoms with subsequent dementia showed medium to very large cognitive deficits. In adjusted logistic regression models, learning and memory deficits predicted the risk for subsequent dementia in participants with depressive symptoms. Participants with a lifetime history of major depression without subsequent dementia showed no cognitive deficits. However, in adjusted logistic regression models, learning and orientation deficits predicted the risk for subsequent dementia also in participants with lifetime major depression. Marked cognitive impairments in old age depression should not be dismissed as "depressive pseudodementia", but require clinical attention as a possible sign of incipient dementia. Non-depressed elderly with a lifetime history of major depression, who remained free of dementia during follow-up, had largely normal cognitive performance.

The use of machine learning for the identification of peripheral artery disease and future mortality risk.

PubMed

Ross, Elsie Gyang; Shah, Nigam H; Dalman, Ronald L; Nead, Kevin T; Cooke, John P; Leeper, Nicholas J

2016-11-01

A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses. Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Association of hospitalizations for asthma with seasonal and pandemic influenza.

PubMed

Gerke, Alicia K; Yang, Ming; Tang, Fan; Foster, Eric D; Cavanaugh, Joseph E; Polgreen, Philip M

2014-01-01

Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000-2008) to help predict asthma admissions during the 2009 H1N1 influenza pandemic. We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998-2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P-value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Sexual sensation seeking and Internet sex-seeking of Middle Eastern men who have sex with men.

PubMed

Matarelli, Steven A

2013-10-01

Despite recent evidence of stabilization in many developed nations, new human immunodeficiency virus (HIV) infections remain a public health concern globally. Efforts remain fragile in a number of world regions due to incomplete or inconsistent social policies concerning HIV, criminalization of same-sex encounters, social stigma, and religious doctrine. Middle Eastern men who have sex with men (MSM) remain one of the most hidden and stigmatized of all HIV risk groups. High-risk sexual bridging networks from these men to low prevalence populations (e.g., to spouse to offspring) are emerging HIV transmission pathways throughout the region. This cross-sectional, exploratory study investigated Sexual Sensation Seeking Scale (SSSS) scores to predict numbers of recent MSM sexual activities and to predict any recent unprotected receptive anal intercourse (URAI) activities in 86 Middle Eastern MSM who resided in the Middle East and who used the Internet to sex-seek. In a multivariate hierarchical regression, higher SSSS scores predicted higher numbers of recent MSM sexual activities (p = .028) and URAI (p = .022). In a logistic regression, higher SSSS scores increased the likelihood of engaging in URAI activities threefold (OR 3.0, 95 % CI 1.15-7.85, p = .025). Age and drug/alcohol use during sexual activities served as covariates in the regression models and were not significant in any analyses. Despite numerous hurdles, adopting Internet-based, non-restricted HIV education and prevention public health programs in the Middle East could instrumentally enhance efforts toward reducing the likelihood of new HIV transmissions in MSM and their sexual partners, ultimately contributing to an improved quality of life.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

The mental well-being of Central American transmigrant men in Mexico.

PubMed

Altman, Claire E; Gorman, Bridget K; Chávez, Sergio; Ramos, Federico; Fernández, Isaac

2018-04-01

To understand the mental health status of Central American migrant men travelling through Mexico to the U.S., we analysed the association between migration-related circumstances/stressors and psychological disorders. In-person interviews and a psychiatric assessment were conducted in 2010 and 2014 with 360 primarily Honduran transmigrant young adult males. The interviews were conducted at three Casas del Migrante (or migrant safe houses) in the migration-corridor cities of Monterrey, and Guadalupe, Nuevo Leon; and Saltillo, Coahuila. The results indicated high levels of migration-related stressors including abuse and a high prevalence of major depressive episodes (MDEs), alcohol dependency, and alcohol abuse. Nested logistic regression models were used to separately predict MDEs, alcohol dependency, and alcohol abuse, assessing their association with migration experiences and socio-demographic characteristics. Logistic regression models showed that characteristics surrounding migration (experiencing abuse, migration duration, and attempts) are predictive of depression. Alcohol dependency and abuse were both associated with marital status and having family/friends in the intended U.S. destination, while the number of migration attempts also predicted alcohol dependency. The results provide needed information on the association between transit migration through Mexico to the U.S. among unauthorised Central American men and major depressive disorder and alcohol abuse and dependency.

Selective attention deficits in obsessive-compulsive disorder: the role of metacognitive processes.

PubMed

Koch, Julia; Exner, Cornelia

2015-02-28

While initial studies supported the hypothesis that cognitive characteristics that capture cognitive resources act as underlying mechanisms in memory deficits in obsessive-compulsive disorder (OCD), the influence of those characteristics on selective attention has not been studied, yet. In this study, we examined the influence of cognitive self-consciousness (CSC), rumination and worrying on performance in selective attention in OCD and compared the results to a depressive and a healthy control group. We found that 36 OCD and 36 depressive participants were impaired in selective attention in comparison to 36 healthy controls. In all groups, hierarchical regression analyses demonstrated that age, intelligence and years in school significantly predicted performance in selective attention. But only in OCD, the predictive power of the regression model was improved when CSC, rumination and worrying were implemented as predictor variables. In contrast, in none of the three groups the predictive power improved when indicators of severity of obsessive-compulsive (OC) and depressive symptoms and trait anxiety were introduced as predictor variables. Thus, our results support the assumption that mental characteristics that bind cognitive resources play an important role in the understanding of selective attention deficits in OCD and that this mechanism is especially relevant for OCD. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Modeling potential habitats for alien species Dreissena polymorpha in continental USA

USGS Publications Warehouse

Mingyang, Li; Yunwei, Ju; Kumar, Sunil; Stohlgren, Thomas J.

2008-01-01

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.

Vitamin D Beliefs and Associations with Sunburns, Sun Exposure, and Sun Protection

PubMed Central

Kim, Bang Hyun; Glanz, Karen; Nehl, Eric J.

2012-01-01

The main objective of this study was to examine certain beliefs about vitamin D and associations with sun exposure, sun protection behaviors, and sunburns. A total of 3,922 lifeguards, pool managers, and parents completed a survey in 2006 about beliefs regarding vitamin D and sun-related behaviors. Multivariate ordinal regression analyses and linear regression analysis were used to examine associations of beliefs and other variables. Results revealed that Non-Caucasian lifeguards and pool managers were less likely to agree that they needed to go out in the sun to get enough vitamin D. Lifeguards and parents who were non-Caucasian were less likely to report that sunlight helped the body to produce vitamin D. A stronger belief about the need to go out in the sun to get enough vitamin D predicted more sun exposure for lifeguards. For parents, a stronger belief that they can get enough vitamin D from foods predicted greater sun protection and a stronger belief that sunlight helps the body produce vitamin D predicted lower sun exposure. This study provides information regarding vitamin D beliefs and their association with certain sun related behaviors across different demographic groups that can inform education efforts about vitamin D and sun protection. PMID:22851950

Analyses of Field Test Data at the Atucha-1 Spent Fuel Pools

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

Sitaraman, S.

A field test was conducted at the Atucha-1 spent nuclear fuel pools to validate a software package for gross defect detection that is used in conjunction with the inspection tool, Spent Fuel Neutron Counter (SFNC). A set of measurements was taken with the SFNC and the software predictions were compared with these data and analyzed. The data spanned a wide range of cooling times and a set of burnup levels leading to count rates from the several hundreds to around twenty per second. The current calibration in the software using linear fitting required the use of multiple calibration factors tomore » cover the entire range of count rates recorded. The solution to this was to use power regression data fitting to normalize the predicted response and derive one calibration factor that can be applied to the entire set of data. The resulting comparisons between the predicted and measured responses were generally good and provided a quantitative method of detecting missing fuel in virtually all situations. Since the current version of the software uses the linear calibration method, it would need to be updated with the new power regression method to make it more user-friendly for real time verification and fieldable for the range of responses that will be encountered.« less

Kindergarten stressors and cumulative adrenocortical activation: the "first straws" of allostatic load?

PubMed

Bush, Nicole R; Obradović, Jelena; Adler, Nancy; Boyce, W Thomas

2011-11-01

Using an ethnically diverse longitudinal sample of 338 kindergarten children, this study examined the effects of cumulative contextual stressors on children's developing hypothalamic-pituitary-adrenocortical (HPA) axis regulation as an early life indicator of allostatic load. Chronic HPA axis regulation was assessed using cumulative, multiday measures of cortisol in both the fall and spring seasons of the kindergarten year. Hierarchical linear regression analyses revealed that contextual stressors related to ethnic minority status, socioeconomic status, and family adversity each uniquely predicted children's daily HPA activity and that some of those associations were curvilinear in conformation. Results showed that the quadratic, U-shaped influences of family socioeconomic status and family adversity operate in different directions to predict children's HPA axis regulation. Results further suggested that these associations differ for White and ethnic minority children. In total, this study revealed that early childhood experiences contribute to shifts in one of the principal neurobiological systems thought to generate allostatic load, confirming the importance of early prevention and intervention efforts. Moreover, findings suggested that analyses of allostatic load and developmental theories accounting for its accrual would benefit from an inclusion of curvilinear associations in tested predictive models.

Estimation of antioxidant components of tomato using VIS-NIR reflectance data by handheld portable spectrometer

NASA Astrophysics Data System (ADS)

Szuvandzsiev, Péter; Helyes, Lajos; Lugasi, Andrea; Szántó, Csongor; Baranowski, Piotr; Pék, Zoltán

2014-10-01

Processing tomato production represents an important part of the total production of processed vegetables in the world. The quality characteristics of processing tomato, important for the food industry, are soluble solids content and antioxidant content (such as lycopene and polyphenols) of the fruit. Analytical quantification of these components is destructive, time and labour consuming. That is why researchers try to develop a non-destructive and rapid method to assess those quality parameters. The present study reports the suitability of a portable handheld visible near infrared spectrometer to predict soluble solids, lycopene and polyphenol content of tomato fruit puree. Spectral ranges of 500-1000 nm were directly acquired on fruit puree of five different tomato varieties using a FieldSpec HandHeld 2™ Portable Spectroradiometer. Immediately after spectral measurement, each fruit sample was analysed to determine soluble solids, lycopene and polyphenol content. Partial least square regressions were carried out to create models of prediction between spectral data and the values obtained from the analytical results. The accuracy of the predictions was analysed according to the coefficient of determination value (R2), the root mean square error of calibration/ cross-validation.

Sexual Functioning Among a Cohort of Treatment-Seeking Canadian Military Personnel and Veterans With Psychiatric Conditions.

PubMed

McIntyre-Smith, Alexandra; St Cyr, Kate; King, Lisa

2015-07-01

The aim of this study was to assess potential predictors of sexual dysfunction and dissatisfaction in a sample of 99 current and former Canadian Forces members attending the Parkwood Hospital Operational Stress Injury Clinic for mental health treatment. Respondents completed a number of questionnaires assessing sexual functioning, post-traumatic stress disorder symptom severity, health-related quality of life, and self-perceived masculinity traits. Regression analyses revealed that role limitations because of physical problems predicted erectile functioning (β = 0.107, p = 0.075), whereas vitality predicted orgasmic functioning (β = 0.044, p = 0.032). Hypermasculinity was the strongest predictor of sexual desire (β = 0.466, p = 0.036), and sexual satisfaction was significantly predicted by bodily pain (β = 0.036, p = 0.019). Preliminary analyses revealed a significant mediating effect of bodily pain on the relationship between post-traumatic stress disorder symptom severity. Results suggest a nuanced interplay between physical health and mental health factors regarding sexual functioning in treatment-seeking military personnel and veterans; however, further research is needed to better delineate the relationship between the 2. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Predictive Value of National Football League Scouting Combine on Future Performance of Running Backs and Wide Receivers.

PubMed

Teramoto, Masaru; Cross, Chad L; Willick, Stuart E

2016-05-01

The National Football League (NFL) Scouting Combine is held each year before the NFL Draft to measure athletic abilities and football skills of college football players. Although the NFL Scouting Combine can provide the NFL teams with an opportunity to evaluate college players for the upcoming NFL Draft, its value for predicting future success of players has been questioned. This study examined whether the NFL Combine measures can predict future performance of running backs (RBs) and wide receivers (WRs) in the NFL. We analyzed the 2000-09 Combine data of RBs (N = 276) and WRs (N = 447) and their on-field performance for the first 3 years after the draft and over their entire careers in the NFL, using correlation and regression analyses, along with a principal component analysis (PCA). The results of the analyses showed that, after accounting for the number of games played, draft position, height (HT), and weight (WT), the time on 10-yard dash was the most important predictor of rushing yards per attempt of the first 3 years (p = 0.002) and of the careers (p < 0.001) in RBs. For WRs, vertical jump was found to be significantly associated with receiving yards per reception of the first 3 years (p = 0.001) and of the careers (p = 0.004) in the NFL, after adjusting for the covariates above. Furthermore, HT was most important in predicting future performance of WRs. The analyses also revealed that the 8 athletic drills in the Combine seemed to have construct validity. It seems that the NFL Scouting Combine has some value for predicting future performance of RBs and WRs in the NFL.

Determination of grain-size characteristics from electromagnetic seabed mapping data: A NW Iberian shelf study

NASA Astrophysics Data System (ADS)

Baasch, Benjamin; Müller, Hendrik; von Dobeneck, Tilo; Oberle, Ferdinand K. J.

2017-05-01

The electric conductivity and magnetic susceptibility of sediments are fundamental parameters in environmental geophysics. Both can be derived from marine electromagnetic profiling, a novel, fast and non-invasive seafloor mapping technique. Here we present statistical evidence that electric conductivity and magnetic susceptibility can help to determine physical grain-size characteristics (size, sorting and mud content) of marine surficial sediments. Electromagnetic data acquired with the bottom-towed electromagnetic profiler MARUM NERIDIS III were analysed and compared with grain size data from 33 samples across the NW Iberian continental shelf. A negative correlation between mean grain size and conductivity (R=-0.79) as well as mean grain size and susceptibility (R=-0.78) was found. Simple and multiple linear regression analyses were carried out to predict mean grain size, mud content and the standard deviation of the grain-size distribution from conductivity and susceptibility. The comparison of both methods showed that multiple linear regression models predict the grain-size distribution characteristics better than the simple models. This exemplary study demonstrates that electromagnetic benthic profiling is capable to estimate mean grain size, sorting and mud content of marine surficial sediments at a very high significance level. Transfer functions can be calibrated using grains-size data from a few reference samples and extrapolated along shelf-wide survey lines. This study suggests that electromagnetic benthic profiling should play a larger role for coastal zone management, seafloor contamination and sediment provenance studies in worldwide continental shelf systems.

A latent class analysis of social activities and health among community-dwelling older adults in Korea.

PubMed

Park, Mi Jin; Park, Nan Sook; Chiriboga, David A

2018-05-01

This study presents an empirical typology of social activity and its association with the depressive symptoms and self-rated health of community-dwelling older adults (n = 464) in South Korea. Latent class analysis (LCA) was used to classify the types of social activities. Data analyses were conducted using Mplus 7.2 program for LCA and SPSS 22.0 for multiple regression analyses. LCA identified people who fell into one of the four activity groups: Diverse, Community Center/Disengaged, Religion Plus, and Friendship/Leisure. Membership in these four groups predicted differences in depressive symptoms and self-rated health. Results indicate that typologies of social activity could enhance practitioners' understanding of activity patterns and their associations with health and well-being.

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

Reporting quality of statistical methods in surgical observational studies: protocol for systematic review.

PubMed

Wu, Robert; Glen, Peter; Ramsay, Tim; Martel, Guillaume

2014-06-28

Observational studies dominate the surgical literature. Statistical adjustment is an important strategy to account for confounders in observational studies. Research has shown that published articles are often poor in statistical quality, which may jeopardize their conclusions. The Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines have been published to help establish standards for statistical reporting.This study will seek to determine whether the quality of statistical adjustment and the reporting of these methods are adequate in surgical observational studies. We hypothesize that incomplete reporting will be found in all surgical observational studies, and that the quality and reporting of these methods will be of lower quality in surgical journals when compared with medical journals. Finally, this work will seek to identify predictors of high-quality reporting. This work will examine the top five general surgical and medical journals, based on a 5-year impact factor (2007-2012). All observational studies investigating an intervention related to an essential component area of general surgery (defined by the American Board of Surgery), with an exposure, outcome, and comparator, will be included in this systematic review. Essential elements related to statistical reporting and quality were extracted from the SAMPL guidelines and include domains such as intent of analysis, primary analysis, multiple comparisons, numbers and descriptive statistics, association and correlation analyses, linear regression, logistic regression, Cox proportional hazard analysis, analysis of variance, survival analysis, propensity analysis, and independent and correlated analyses. Each article will be scored as a proportion based on fulfilling criteria in relevant analyses used in the study. A logistic regression model will be built to identify variables associated with high-quality reporting. A comparison will be made between the scores of surgical observational studies published in medical versus surgical journals. Secondary outcomes will pertain to individual domains of analysis. Sensitivity analyses will be conducted. This study will explore the reporting and quality of statistical analyses in surgical observational studies published in the most referenced surgical and medical journals in 2013 and examine whether variables (including the type of journal) can predict high-quality reporting.

Statistical analyses on sandstones: Systematic approach for predicting petrographical and petrophysical properties

NASA Astrophysics Data System (ADS)

Stück, H. L.; Siegesmund, S.

2012-04-01

Sandstones are a popular natural stone due to their wide occurrence and availability. The different applications for these stones have led to an increase in demand. From the viewpoint of conservation and the natural stone industry, an understanding of the material behaviour of this construction material is very important. Sandstones are a highly heterogeneous material. Based on statistical analyses with a sufficiently large dataset, a systematic approach to predicting the material behaviour should be possible. Since the literature already contains a large volume of data concerning the petrographical and petrophysical properties of sandstones, a large dataset could be compiled for the statistical analyses. The aim of this study is to develop constraints on the material behaviour and especially on the weathering behaviour of sandstones. Approximately 300 samples from historical and presently mined natural sandstones in Germany and ones described worldwide were included in the statistical approach. The mineralogical composition and fabric characteristics were determined from detailed thin section analyses and descriptions in the literature. Particular attention was paid to evaluating the compositional and textural maturity, grain contact respectively contact thickness, type of cement, degree of alteration and the intergranular volume. Statistical methods were used to test for normal distributions and calculating the linear regression of the basic petrophysical properties of density, porosity, water uptake as well as the strength. The sandstones were classified into three different pore size distributions and evaluated with the other petrophysical properties. Weathering behavior like hygric swelling and salt loading tests were also included. To identify similarities between individual sandstones or to define groups of specific sandstone types, principle component analysis, cluster analysis and factor analysis were applied. Our results show that composition and porosity evolution during diagenesis is a very important control on the petrophysical properties of a building stone. The relationship between intergranular volume, cementation and grain contact, can also provide valuable information to predict the strength properties. Since the samples investigated mainly originate from the Triassic German epicontinental basin, arkoses and feldspar-arenites are underrepresented. In general, the sandstones can be grouped as follows: i) quartzites, highly mature with a primary porosity of about 40%, ii) quartzites, highly mature, showing a primary porosity of 40% but with early clay infiltration, iii) sublitharenites-lithic arenites exhibiting a lower primary porosity, higher cementation with quartz and Fe-oxides ferritic and iv) sublitharenites-lithic arenites with a higher content of pseudomatrix. However, in the last two groups the feldspar and lithoclasts can also show considerable alteration. All sandstone groups differ with respect to the pore space and strength data, as well as water uptake properties, which were obtained by linear regression analysis. Similar petrophysical properties are discernible for each type when using principle component analysis. Furthermore, strength as well as the porosity of sandstones shows distinct differences considering their stratigraphic ages and the compositions. The relationship between porosity, strength as well as salt resistance could also be verified. Hygric swelling shows an interrelation to pore size type, porosity and strength but also to the degree of alteration (e.g. lithoclasts, pseudomatrix). To summarize, the different regression analyses and the calculated confidence regions provide a significant tool to classify the petrographical and petrophysical parameters of sandstones. Based on this, the durability and the weathering behavior of the sandstone groups can be constrained. Keywords: sandstones, petrographical & petrophysical properties, predictive approach, statistical investigation

Using multilevel modeling to assess case-mix adjusters in consumer experience surveys in health care.

PubMed

Damman, Olga C; Stubbe, Janine H; Hendriks, Michelle; Arah, Onyebuchi A; Spreeuwenberg, Peter; Delnoij, Diana M J; Groenewegen, Peter P

2009-04-01

Ratings on the quality of healthcare from the consumer's perspective need to be adjusted for consumer characteristics to ensure fair and accurate comparisons between healthcare providers or health plans. Although multilevel analysis is already considered an appropriate method for analyzing healthcare performance data, it has rarely been used to assess case-mix adjustment of such data. The purpose of this article is to investigate whether multilevel regression analysis is a useful tool to detect case-mix adjusters in consumer assessment of healthcare. We used data on 11,539 consumers from 27 Dutch health plans, which were collected using the Dutch Consumer Quality Index health plan instrument. We conducted multilevel regression analyses of consumers' responses nested within health plans to assess the effects of consumer characteristics on consumer experience. We compared our findings to the results of another methodology: the impact factor approach, which combines the predictive effect of each case-mix variable with its heterogeneity across health plans. Both multilevel regression and impact factor analyses showed that age and education were the most important case-mix adjusters for consumer experience and ratings of health plans. With the exception of age, case-mix adjustment had little impact on the ranking of health plans. On both theoretical and practical grounds, multilevel modeling is useful for adequate case-mix adjustment and analysis of performance ratings.

Relationships of Measurement Error and Prediction Error in Observed-Score Regression

ERIC Educational Resources Information Center

Moses, Tim

2012-01-01

The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…

The role of homework in cognitive-behavioral therapy for cocaine dependence.

PubMed

Gonzalez, Vivian M; Schmitz, Joy M; DeLaune, Katherine A

2006-06-01

This study examines the effect of homework compliance on treatment outcome in 123 participants receiving cognitive-behavioral therapy (CBT) for cocaine dependence. Regression analyses revealed a significant relationship between homework compliance and cocaine use that was moderated by readiness to change. Homework compliance predicted less cocaine use during treatment but only for participants higher in readiness to change. For those lower in readiness to change, homework compliance was not associated with cocaine use during treatment. Homework compliance early in therapy was associated with better retention in treatment. Homework compliance was not predicted by participants' level of education or readiness to change. These findings support the use of homework during CBT for substance use disorders. Copyright 2006 APA, all rights reserved.

Low trait self-control predicts self-handicapping.

PubMed

Uysal, Ahmet; Knee, C Raymond

2012-02-01

Past research has shown that self-handicapping stems from uncertainty about one's ability and self-presentational concerns. The present studies suggest that low dispositional self-control is also associated with self-handicapping. In 3 studies (N = 289), the association between self-control and self-handicapping was tested. Self-control was operationalized as trait self-control, whereas self-handicapping was operationalized as trait self-handicapping in Study 1 (N = 160), self-reported self-handicapping in Study 2 (N = 74), and behavioral self-handicapping in Study 3 (N = 55). In all 3 studies, hierarchical regression analyses revealed that low self-control predicts self-handicapping, independent of self-esteem, self-doubt, social desirability, and gender. © 2012 The Authors. Journal of Personality © 2012, Wiley Periodicals, Inc.

Relationships between testosterone levels and cognition in patients with Alzheimer disease and nondemented elderly men.

PubMed

Seidl, Jennifer N Travis; Massman, Paul J

2015-03-01

Previous research suggests that low levels of testosterone may be associated with the development of Alzheimer disease (AD), as well as poorer performance on certain neuropsychological tests and increased risk of depression. This study utilized data from 61 nondemented older men and 68 men with probable AD. Testosterone levels did not differ between the groups. Regression analyses in men with AD revealed that testosterone levels did not significantly predict performance on neuropsychological tests or a measure of depression. Among controls, testosterone levels predicted estimated premorbid verbal IQ and performance on a verbal fluency test. Findings suggest that testosterone is not associated with most neuropsychological test performances in patients with AD. © The Author(s) 2014.

Jocks, gender, binge drinking, and adolescent violence.

PubMed

Miller, Kathleen E; Melnick, Merrill J; Farrell, Michael P; Sabo, Donald F; Barnes, Grace M

2006-01-01

Previous research has suggested a link between athletic involvement and elevated levels of adolescent violence outside the sport context. The present study expanded on this literature by positing differences in the sport-violence relationship across dimensions of athletic involvement (athletic participation vs. jock identity), type of violence (family vs. nonfamily), and gender as well as by examining the impact of binge drinking on the sport-violence relationship. Regression analyses using a sample of 608 Western New York adolescents indicated that (a) jock identity (but not athletic participation) was associated with more frequent violence, (b) jock identity predicted nonfamily violence (but not family violence), and (c) the link between jock identity and nonfamily violence was stronger for boys than for girls. Binge drinking predicted family violence among nonjocks only.

Social support and conscientiousness in hemodialysis adherence.

PubMed

Moran, P J; Christensen, A J; Lawton, W J

1997-01-01

Previous conclusions regarding the role of social support in hemodialysis adherence are inconsistent, suggesting that other factors may moderate this relationship. Using the Five-Factor Model of Personality, we examined the hypothesis that conscientiousness would interact with social support in predicting fluid-intake and medication adherence in a sample of 56 chronic hemodialysis patients. Hierarchical regression analyses (controlling for demographic, clinical, and other personality variables) revealed a significant interaction between social support and conscientiousness. However, inconsistent with prediction, high support among patients with low conscientiousness was associated with poorer fluid-intake adherence, while support had little effect on fluid-intake adherence among high conscientiousness patients. No main or interactive effects were found for support or conscientiousness on a measure of medication adherence.

The predictive validity of the MCAT exam in relation to academic performance through medical school: a national cohort study of 2001-2004 matriculants.

PubMed

Dunleavy, Dana M; Kroopnick, Marc H; Dowd, Keith W; Searcy, Cynthia A; Zhao, Xiaohui

2013-05-01

Most research examining the predictive validity of the Medical College Admission Test (MCAT) has focused on the relationship between MCAT scores and scores on the United States Medical Licensing Examination Step exams. This study examined whether MCAT scores predict students' unimpeded progress toward graduation (UP), which the authors defined as not withdrawing or being dismissed for academic reasons, graduating within five years of matriculation, and passing the Step 1, Step 2 Clinical Knowledge, and Step 2 Clinical Skills exams on the first attempt. Students who matriculated during 2001-2004 at 119 U.S. medical schools were included in the analyses. Logistic regression analyses were used to estimate the relationships between UP and MCAT total scores alone, undergraduate grade point averages (UGPAs) alone, and UGPAs and MCAT total scores together. All analyses were conducted at the school level and were considered together to evaluate relationships across schools. The majority of matriculants experienced UP. Together, UGPAs and MCAT total scores predicted UP well. MCAT total scores alone were a better predictor than UGPAs alone. Relationships were similar across schools; however, there was more variability across schools in the relationship between UP and UGPAs than between UP and MCAT total scores. The combination of UGPAs and MCAT total scores performs well as a predictor of UP. Both UGPAs and MCAT total scores are strong predictors of academic performance in medical school through graduation, not just the first two years. Further, these relationships generalize across medical schools.

Teasing apart low mindfulness: differentiating deficits in mindfulness and in psychological flexibility in predicting symptoms of generalized anxiety disorder and depression.

PubMed

Curtiss, Joshua; Klemanski, David H

2014-09-01

This research investigated the differential ability of three components of low mindfulness to uniquely predict symptoms of generalized anxiety disorder (GAD) and depression, while controlling for psychological inflexibility, a construct conceptually related to low mindfulness. Also examined was the meditational role of several mindfulness facets in the relationship between psychological inflexibility and symptoms of each disorder. Using a clinical sample (n=153) containing mostly patients with GAD or depression diagnoses, we conducted hierarchical multiple regression analyses and mediation analyses to determine unique relationships. Whereas deficits in adopting a non-reactive perspective exhibited incremental validity beyond psychological inflexibility in predicting symptoms of GAD, deficits in acting with awareness did so in predicting symptoms of depression. Results of mediation analyses corroborated this pattern, as the relationships psychological inflexibility exhibited with symptoms of GAD and of depression were mediated by non-reactivity and acting with awareness, respectively. The cross-sectional design of this study precludes causal interpretations of the mediation models. Findings corroborate the following conclusions: (i) the lack of present oriented awareness experienced by individuals with symptoms of depression is not completely accounted for by psychological inflexibility nor by symptoms of GAD; (ii) the reactive approach to automatic thoughts adopted by those with symptoms of GAD is not completely accounted for by psychological inflexibility nor by symptoms of depression. These conclusions suggest that it would be profitable for mindfulness-based therapies to concentrate on these specific mindfulness deficits to ameliorate the severity of GAD and depression. Copyright © 2014 Elsevier B.V. All rights reserved.

Serum microRNA expression patterns that predict early treatment failure in prostate cancer patients.

PubMed

Singh, Prashant K; Preus, Leah; Hu, Qiang; Yan, Li; Long, Mark D; Morrison, Carl D; Nesline, Mary; Johnson, Candace S; Koochekpour, Shahriar; Kohli, Manish; Liu, Song; Trump, Donald L; Sucheston-Campbell, Lara E; Campbell, Moray J

2014-02-15

We aimed to identify microRNA (miRNA) expression patterns in the serum of prostate cancer (CaP) patients that predict the risk of early treatment failure following radical prostatectomy (RP). Microarray and Q-RT-PCR analyses identified 43 miRNAs as differentiating disease stages within 14 prostate cell lines and reflectedpublically available patient data. 34 of these miRNA were detectable in the serum of CaP patients. Association with time to biochemical progression was examined in a cohort of CaP patients following RP. A greater than two-fold increase in hazard of biochemical progression associated with altered expression of miR-103, miR-125b and miR-222 (p<.0008) in the serum of CaP patients. Prediction models based on penalized regression analyses showed that the levels of the miRNAs and PSA together were better at detecting false positives than models without miRNAs, for similar level of sensitivity. Analyses of publically available data revealed significant and reciprocal relationships between changes in CpG methylation and miRNA expression patterns suggesting a role for CpG methylation to regulate miRNA. Exploratory validation supported roles for miR-222 and miR-125b to predict progression risk in CaP. The current study established that expression patterns of serum-detectable miRNAs taken at the time of RP are prognostic for men who are at risk of experiencing subsequent early biochemical progression. These non-invasive approaches could be used to augment treatment decisions.