The role of ENSO in understanding changes in Colombia's annual malaria burden by region, 1960–2006

PubMed Central

Mantilla, Gilma; Oliveros, Hugo; Barnston, Anthony G

2009-01-01

Background Malaria remains a serious problem in Colombia. The number of malaria cases is governed by multiple climatic and non-climatic factors. Malaria control policies, and climate controls such as rainfall and temperature variations associated with the El Niño/Southern Oscillation (ENSO), have been associated with malaria case numbers. Using historical climate data and annual malaria case number data from 1960 to 2006, statistical models are developed to isolate the effects of climate in each of Colombia's five contrasting geographical regions. Methods Because year to year climate variability associated with ENSO causes interannual variability in malaria case numbers, while changes in population and institutional control policy result in more gradual trends, the chosen predictors in the models are annual indices of the ENSO state (sea surface temperature [SST] in the tropical Pacific Ocean) and time reference indices keyed to two major malaria trends during the study period. Two models were used: a Poisson and a Negative Binomial regression model. Two ENSO indices, two time reference indices, and one dummy variable are chosen as candidate predictors. The analysis was conducted using the five geographical regions to match the similar aggregation used by the National Institute of Health for its official reports. Results The Negative Binomial regression model is found better suited to the malaria cases in Colombia. Both the trend variables and the ENSO measures are significant predictors of malaria case numbers in Colombia as a whole, and in two of the five regions. A one degree Celsius change in SST (indicating a weak to moderate ENSO event) is seen to translate to an approximate 20% increase in malaria cases, holding other variables constant. Conclusion Regional differentiation in the role of ENSO in understanding changes in Colombia's annual malaria burden during 1960–2006 was found, constituting a new approach to use ENSO as a significant predictor of the malaria cases in Colombia. These results naturally point to additional needed work: (1) refining the regional and seasonal dependence of climate on the ENSO state, and of malaria on the climate variables; (2) incorporating ENSO-related climate variability into dynamic malaria models. PMID:19133152

Older Age and Leg Pain Are Good Predictors of Pain and Disability Outcomes in 2710 Patients Who Receive Lumbar Fusion.

PubMed

Cook, Chad E; Frempong-Boadu, Anthony K; Radcliff, Kristen; Karikari, Isaac; Isaacs, Robert

2015-10-01

Identifying appropriate candidates for lumbar spine fusion is a challenging and controversial topic. The purpose of this study was to identify baseline characteristics related to poor/favorable outcomes at 1 year for a patient who received lumbar spine fusion. The aims of this study were to describe baseline characteristics of those who received lumbar surgery and to identify baseline characteristics from a spine repository that were related to poor and favorable pain and disability outcomes for patient who received lumbar fusion (with or without decompression), who were followed up for 1 full year and discriminate predictor variables that were either or in contrast to prognostic variables reported in the literature. This study analyzed data from 2710 patients who underwent lumbar spine fusion. All patient data was part of a multicenter, multi-national spine repository. Ten relatively commonly captured data variables were used as predictors for the study. Univariate/multivariate logistic regression analyses were run against outcome variables of pain/disability. Multiple univariate findings were associated with pain/disability outcomes at 1 year including age, previous surgical history, baseline disability, baseline pain, baseline quality of life scores, and leg pain greater than back pain. Notably significant multivariate findings for both pain and disability include older age, previous surgical history, and baseline mental summary scores, disability, and pain. Leg pain greater than back pain and older age may yield promising value when predicting positive outcomes. Other significant findings may yield less value since these findings are similar to those that are considered to be prognostic regardless of intervention type.

Relationship among several measurements of slipperiness obtained in a laboratory environment.

PubMed

Chang, Wen-Ruey; Chang, Chien-Chi

2018-04-01

Multiple sensing mechanisms could be used in forming responses to avoid slips, but previous studies, correlating only two parameters, revealed a limited picture of this complex system. In this study, the participants walked as fast as possible without a slip under 15 conditions of different degrees of slipperiness. The relationships among various response parameters, including perceived slipperiness rating, utilized coefficient of friction (UCOF), slipmeter measurement and kinematic parameters, were evaluated. The results showed that the UCOF, perceived rating and heel angle had higher adjusted R 2 values as dependent variables in the multiple linear regressions with the remaining variables in the final pool as independent variables. Although each variable in the final data pool could reflect some measurement of slipperiness, these three variables are more inclusive than others in representing the other variables and were bigger predictors of other variables, so they could be better candidates for measurements of slipperiness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Candidate Predictors of Health-Related Quality of Life of Colorectal Cancer Survivors: A Systematic Review

PubMed Central

van der Linden, Bernadette W.A.; Winkels, Renate M.; van Duijnhoven, Fränzel J.; Mols, Floortje; van Roekel, Eline H.; Kampman, Ellen; Beijer, Sandra; Weijenberg, Matty P.

2016-01-01

The population of colorectal cancer (CRC) survivors is growing and many survivors experience deteriorated health-related quality of life (HRQoL) in both early and late post-treatment phases. Identification of CRC survivors at risk for HRQoL deterioration can be improved by using prediction models. However, such models are currently not available for oncology practice. As a starting point for developing prediction models of HRQoL for CRC survivors, a comprehensive overview of potential candidate HRQoL predictors is necessary. Therefore, a systematic literature review was conducted to identify candidate predictors of HRQoL of CRC survivors. Original research articles on associations of biopsychosocial factors with HRQoL of CRC survivors were searched in PubMed, Embase, and Google Scholar. Two independent reviewers assessed eligibility and selected articles for inclusion (N = 53). Strength of evidence for candidate HRQoL predictors was graded according to predefined methodological criteria. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) was used to develop a biopsychosocial framework in which identified candidate HRQoL predictors were mapped across the main domains of the ICF: health condition, body structures and functions, activities, participation, and personal and environmental factors. The developed biopsychosocial ICF framework serves as a basis for selecting candidate HRQoL predictors, thereby providing conceptual guidance for developing comprehensive, evidence-based prediction models of HRQoL for CRC survivors. Such models are useful in clinical oncology practice to aid in identifying individual CRC survivors at risk for HRQoL deterioration and could also provide potential targets for a biopsychosocial intervention aimed at safeguarding the HRQoL of at-risk individuals. Implications for Practice: More and more people now survive a diagnosis of colorectal cancer. The quality of life of these cancer survivors is threatened by health problems persisting for years after diagnosis and treatment. Early identification of survivors at risk of experiencing low quality of life in the future is thus important for taking preventive measures. Clinical prediction models are tools that can help oncologists identify at-risk individuals. However, such models are currently not available for clinical oncology practice. This systematic review outlines candidate predictors of low quality of life of colorectal cancer survivors, providing a firm conceptual basis for developing prediction models. PMID:26911406

Prediction of Waitlist Mortality in Adult Heart Transplant Candidates: The Candidate Risk Score.

PubMed

Jasseron, Carine; Legeai, Camille; Jacquelinet, Christian; Leprince, Pascal; Cantrelle, Christelle; Audry, Benoît; Porcher, Raphael; Bastien, Olivier; Dorent, Richard

2017-09-01

The cardiac allocation system in France is currently based on urgency and geography. Medical urgency is defined by therapies without considering objective patient mortality risk factors. This study aimed to develop a waitlist mortality risk score from commonly available candidate variables. The study included all patients, aged 16 years or older, registered on the national registry CRISTAL for first single-organ heart transplantation between January 2010 and December 2014. This population was randomly divided in a 2:1 ratio into derivation and validation cohorts. The association of variables at listing with 1-year waitlist death or delisting for worsening medical condition was assessed within the derivation cohort. The predictors were used to generate a candidate risk score (CRS). Validation of the CRS was performed in the validation cohort. Concordance probability estimation (CPE) was used to evaluate the discriminative capacity of the models. During the study period, 2333 patients were newly listed. The derivation (n =1 555) and the validation cohorts (n = 778) were similar. Short-term mechanical circulatory support, natriuretic peptide decile, glomerular filtration rate, and total bilirubin level were included in a simplified model and incorporated into the score. The Concordance probability estimation of the CRS was 0.73 in the derivation cohort and 0.71 in the validation cohort. The correlation between observed and expected 1-year waitlist mortality in the validation cohort was 0.87. The candidate risk score provides an accurate objective prediction of waitlist mortality. It is currently being used to develop a modified cardiac allocation system in France.

Frequency and clinical predictors of coronary artery disease in chronic renal failure renal transplant candidates.

PubMed

de Albuquerque Seixas, Emerson; Carmello, Beatriz Leone; Kojima, Christiane Akemi; Contti, Mariana Moraes; Modeli de Andrade, Luiz Gustavo; Maiello, José Roberto; Almeida, Fernando Antonio; Martin, Luis Cuadrado

2015-05-01

Cardiovascular diseases are major causes of mortality in chronic renal failure patients before and after renal transplantation. Among them, coronary disease presents a particular risk; however, risk predictors have been used to diagnose coronary heart disease. This study evaluated the frequency and importance of clinical predictors of coronary artery disease in chronic renal failure patients undergoing dialysis who were renal transplant candidates, and assessed a previously developed scoring system. Coronary angiographies conducted between March 2008 and April 2013 from 99 candidates for renal transplantation from two transplant centers in São Paulo state were analyzed for associations between significant coronary artery diseases (≥70% stenosis in one or more epicardial coronary arteries or ≥50% in the left main coronary artery) and clinical parameters. Univariate logistic regression analysis identified diabetes, angina, and/or previous infarction, clinical peripheral arterial disease and dyslipidemia as predictors of coronary artery disease. Multiple logistic regression analysis identified only diabetes and angina and/or previous infarction as independent predictors. The results corroborate previous studies demonstrating the importance of these factors when selecting patients for coronary angiography in clinical pretransplant evaluation.

Generic biomass functions for Norway spruce in Central Europe--a meta-analysis approach toward prediction and uncertainty estimation.

PubMed

Wirth, Christian; Schumacher, Jens; Schulze, Ernst-Detlef

2004-02-01

To facilitate future carbon and nutrient inventories, we used mixed-effect linear models to develop new generic biomass functions for Norway spruce (Picea abies (L.) Karst.) in Central Europe. We present both the functions and their respective variance-covariance matrices and illustrate their application for biomass prediction and uncertainty estimation for Norway spruce trees ranging widely in size, age, competitive status and site. We collected biomass data for 688 trees sampled in 102 stands by 19 authors. The total number of trees in the "base" model data sets containing the predictor variables diameter at breast height (D), height (H), age (A), site index (SI) and site elevation (HSL) varied according to compartment (roots: n = 114, stem: n = 235, dry branches: n = 207, live branches: n = 429 and needles: n = 551). "Core" data sets with about 40% fewer trees could be extracted containing the additional predictor variables crown length and social class. A set of 43 candidate models representing combinations of lnD, lnH, lnA, SI and HSL, including second-order polynomials and interactions, was established. The categorical variable "author" subsuming mainly methodological differences was included as a random effect in a mixed linear model. The Akaike Information Criterion was used for model selection. The best models for stem, root and branch biomass contained only combinations of D, H and A as predictors. More complex models that included site-related variables resulted for needle biomass. Adding crown length as a predictor for needles, branches and roots reduced both the bias and the confidence interval of predictions substantially. Applying the best models to a test data set of 17 stands ranging in age from 16 to 172 years produced realistic allocation patterns at the tree and stand levels. The 95% confidence intervals (% of mean prediction) were highest for crown compartments (approximately +/- 12%) and lowest for stem biomass (approximately +/- 5%), and within each compartment, they were highest for the youngest and oldest stands, respectively.

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Access to Heart Transplantation: A Proper Analysis of the Competing Risks of Death and Transplantation Is Required to Optimize Graft Allocation.

PubMed

Cantrelle, Christelle; Legeai, Camille; Latouche, Aurélien; Tuppin, Philippe; Jasseron, Carine; Sebbag, Laurent; Bastien, Olivier; Dorent, Richard

2017-08-01

Heart allocation systems are usually urgency-based, offering grafts to candidates at high risk of waitlist mortality. In the context of a revision of the heart allocation rules, we determined observed predictors of 1-year waitlist mortality in France, considering the competing risk of transplantation, to determine which candidate subgroups are favored or disadvantaged by the current allocation system. Patients registered on the French heart waitlist between 2010 and 2013 were included. Cox cause-specific hazards and Fine and Gray subdistribution hazards were used to determine candidate characteristics associated with waitlist mortality and access to transplantation. Of the 2053 candidates, 7 variables were associated with 1-year waitlist mortality by the Fine and Gray method including 4 candidate characteristics related to heart failure severity (hospitalization at listing, serum natriuretic peptide level, systolic pulmonary artery pressure, and glomerular filtration rate) and 3 characteristics not associated with heart failure severity but with lower access to transplantation (blood type, age, and body mass index). Observed waitlist mortality for candidates on mechanical circulatory support was like that of others. The heart allocation system strongly modifies the risk of pretransplant mortality related to heart failure severity. An in-depth competing risk analysis is therefore a more appropriate method to evaluate graft allocation systems. This knowledge should help to prioritize candidates in the context of a limited donor pool.

Internet and Democracy: Is the Internet an Important Predictor for Physical Education Teacher Candidates' Attitudes towards Democracy?

ERIC Educational Resources Information Center

Ünlü, Hüseyin

2017-01-01

Today, in the digital age, the Internet usage is common among university students. The Internet is also an important platform for actively participating in democracy. This study explores physical education (PE) candidate teachers' attitudes toward the Internet and democracy. It also explores whether the Internet is an important predictor for…

Can adverse maternal and perinatal outcomes be predicted when blood pressure becomes elevated? Secondary analyses from the CHIPS (Control of Hypertension In Pregnancy Study) randomized controlled trial.

PubMed

Magee, Laura A; von Dadelszen, Peter; Singer, Joel; Lee, Terry; Rey, Evelyne; Ross, Susan; Asztalos, Elizabeth; Murphy, Kellie E; Menzies, Jennifer; Sanchez, Johanna; Gafni, Amiram; Gruslin, Andrée; Helewa, Michael; Hutton, Eileen; Lee, Shoo K; Logan, Alexander G; Ganzevoort, Wessel; Welch, Ross; Thornton, Jim G; Moutquin, Jean Marie

2016-07-01

For women with chronic or gestational hypertension in CHIPS (Control of Hypertension In Pregnancy Study, NCT01192412), we aimed to examine whether clinical predictors collected at randomization could predict adverse outcomes. This was a planned, secondary analysis of data from the 987 women in the CHIPS Trial. Logistic regression was used to examine the impact of 19 candidate predictors on the probability of adverse perinatal (pregnancy loss or high level neonatal care for >48 h, or birthweight <10th percentile) or maternal outcomes (severe hypertension, preeclampsia, or delivery at <34 or <37 weeks). A model containing all candidate predictors was used to start the stepwise regression process based on goodness of fit as measured by the Akaike information criterion. For face validity, these variables were forced into the model: treatment group ("less tight" or "tight" control), antihypertensive type at randomization, and blood pressure within 1 week before randomization. Continuous variables were represented continuously or dichotomized based on the smaller p-value in univariate analyses. An area-under-the-receiver-operating-curve (AUC ROC) of ≥0.70 was taken to reflect a potentially useful model. Point estimates for AUC ROC were <0.70 for all but severe hypertension (0.70, 95% CI 0.67-0.74) and delivery at <34 weeks (0.71, 95% CI 0.66-0.75). Therefore, no model warranted further assessment of performance. CHIPS data suggest that when women with chronic hypertension develop an elevated blood pressure in pregnancy, or formerly normotensive women develop new gestational hypertension, maternal and current pregnancy clinical characteristics cannot predict adverse outcomes in the index pregnancy. © 2016 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Pharmacogenetics of schizophrenia.

PubMed

Reynolds, Gavin P; Templeman, Lucy A; Godlewska, Beata R

2006-08-01

There is substantial unexplained interindividual variability in the drug treatment of schizophrenia. A substantial proportion of patients respond inadequately to antipsychotic drugs, and many experience limiting side effects. As genetic factors are likely to contribute to this variability, the pharmacogenetics of schizophrenia has attracted substantial effort. The approaches have mainly been limited to association studies of polymorphisms in candidate genes, which have been indicated by the pharmacology of antipsychotic drugs. Although some advances have been made, particularly in understanding the pharmacogenetics of some limiting side effects, genetic prediction of symptom response remains elusive. Nevertheless, with improvements in defining the response phenotype in carefully assessed and homogeneous subject groups, the near future is likely to see the identification of genetic predictors of outcome that may inform the choice of pharmacotherapy.

The Effect of Religion on Candidate Preference in the 2008 and 2012 Republican Presidential Primaries

PubMed Central

Bradberry, Leigh A.

2016-01-01

Thanks to the work of politics and religion scholars, we now know a lot about the relationship between religion and voting in American presidential general elections. However, we know less about the influence of religion on individual vote choice in presidential primaries. This article fills that gap by exploring the relationship between religion and candidate preference in the 2008 and 2012 Republican primaries. Using pre-Super Tuesday surveys conducted by the Pew Research Center, I find that the Republican candidate who most explicitly appealed to religious voters (Mike Huckabee in 2008 and Rick Santorum in 2012) was the preferred candidate of Republican respondents who attended religious services at the highest levels, and that as attendance increased, so did the likelihood of preferring that candidate. I also find that identification as a born again Christian mattered to candidate preference. Specifically, born again Christians were more likely than non-born again Christians to prefer Huckabee to Mitt Romney, John McCain and Ron Paul in 2008, and Santorum to Romney in 2012. Although ideology was not the primary subject of this article, I find that ideology was also a statistically significant predictor of Republican candidate preference in both 2008 and 2012. This robust finding reinforces scholars’ prior work on the importance of ideology in explaining presidential primary vote choice. The overall findings of the paper provide evidence that religion variables can add to our understanding of why voters prefer one candidate over another in presidential primaries. PMID:27043438

The Effect of Religion on Candidate Preference in the 2008 and 2012 Republican Presidential Primaries.

PubMed

Bradberry, Leigh A

2016-01-01

Thanks to the work of politics and religion scholars, we now know a lot about the relationship between religion and voting in American presidential general elections. However, we know less about the influence of religion on individual vote choice in presidential primaries. This article fills that gap by exploring the relationship between religion and candidate preference in the 2008 and 2012 Republican primaries. Using pre-Super Tuesday surveys conducted by the Pew Research Center, I find that the Republican candidate who most explicitly appealed to religious voters (Mike Huckabee in 2008 and Rick Santorum in 2012) was the preferred candidate of Republican respondents who attended religious services at the highest levels, and that as attendance increased, so did the likelihood of preferring that candidate. I also find that identification as a born again Christian mattered to candidate preference. Specifically, born again Christians were more likely than non-born again Christians to prefer Huckabee to Mitt Romney, John McCain and Ron Paul in 2008, and Santorum to Romney in 2012. Although ideology was not the primary subject of this article, I find that ideology was also a statistically significant predictor of Republican candidate preference in both 2008 and 2012. This robust finding reinforces scholars' prior work on the importance of ideology in explaining presidential primary vote choice. The overall findings of the paper provide evidence that religion variables can add to our understanding of why voters prefer one candidate over another in presidential primaries.

IMG candidates' demographic characteristics as predictors of CEHPEA CE1 results

PubMed Central

Nayer, Marla; Rothman, Arthur

2013-01-01

Objective To assess the extent to which demographic characteristics are related to international medical graduate (IMG) candidate performance on the Centre for the Evaluation of Health Professionals Educated Abroad General Comprehensive Clinical Examination 1 (CE1). Design Retrospective study. Setting Toronto, Ont. Participants All IMG candidates who registered for and took the CE1 in 2007 (n = 430), 2008 (n = 480), and 2009 (n = 472) were included in this analysis. All candidates completed the Centre for the Evaluation of Health Professionals Educated Abroad CE1, a 12-station objective structured clinical examination. Main outcome measures Mean (SD) examination scores for groups based on demographic variables (age, region of medical training, and Medical Council of Canada Qualifying Examination Part 1 [MCCQE1] score) were calculated. Analysis of variance was done using CE1 examination total scores as the dependent variables. Results Candidates from countries where both medical education and patient care are conducted in English and those from South America and Western Europe achieved the highest scores, while candidates from the Western Pacific region and Africa achieved the lowest scores. Younger candidates achieved higher scores than older candidates. These results were consistent across the 3 years of CE1 examination administration. There was a significant relationship between MCCQE1 and CE1 scores in 2 of the 3 years: 2007 (r = 0.218, P < .001) and 2008 (r = 0.23, P < .01). Conclusion The CE1 includes an assessment of communication skills; hence it is reasonable that candidates with stronger English skills have the highest scores on the CE1. Age, as a proxy for time since graduation, also has a substantial effect on examination scores, possibly owing to those further from their training lacking some currency of knowledge or being in focused rather than general practices. It is reasonable that those who had higher scores on the written test (the MCCQE1) would also have higher scores on the clinical test (the CE1). Demographic characteristics appear to be related to performance on the CE1. PMID:23418245

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

Predictors of academic performance for applicants to an international dental studies program in the United States.

PubMed

Pitigoi-Aron, Gabriela; King, Patricia A; Chambers, David W

2011-12-01

The number of U.S. and Canadian dental schools offering programs for dentists with degrees from other countries leading to the D.D.S. or D.M.D. degree has increased recently. This fact, along with the diversity of educational systems represented by candidates for these programs, increases the importance of identifying valid admissions predictors of success in international dental student programs. Data from 148 students accepted into the international dental studies program at the University of the Pacific from 1994 through 2004 were analyzed. Dependent variables were comprehensive cumulative GPA at the end of both the first and second years of the two-year program. The Test of English as a Foreign Language (TOEFL) and both Parts I and II of the National Board Dental Examination (NBDE) were significant positive predictors of success. Performance on laboratory tests of clinical skill in operative dentistry and in fixed prosthodontics and ratings from interviewers were not predictive of overall success in the program. Although this study confirms the predictive value of written tests such as the TOEFL and NBDE, it also contributes to the literature documenting inconsistent results regarding other types of predictors. It may be the case that characteristics of individual programs or features of the applicant pools for each may require use of admissions predictors that are unique to schools.

Age, Body Mass Index, and Frequency of Sexual Activity are Independent Predictors of Testosterone Deficiency in Men With Erectile Dysfunction.

PubMed

Pagano, Matthew J; De Fazio, Adam; Levy, Alison; RoyChoudhury, Arindam; Stahl, Peter J

2016-04-01

To identify clinical predictors of testosterone deficiency (TD) in men with erectile dysfunction (ED), thereby identifying subgroups that are most likely to benefit from targeted testosterone screening. Retrospective review was conducted on 498 men evaluated for ED between January 2013 and July 2014. Testing for TD by early morning serum measurement was offered to all eligible men. Patients with history of prostate cancer or testosterone replacement were excluded. Univariable linear regression was conducted to analyze 19 clinical variables for associations with serum total testosterone (TT), calculated free testosterone (cFT), and TD (T <300 ng/dL or cFT <6.5 ng/dL). Variables significant on univariable analysis were included in multiple regression models. A total of 225 men met inclusion criteria. Lower TT levels were associated with greater body mass index (BMI), less frequent sexual activity, and absence of clinical depression on multiple regression analysis. TT decreased by 49.5 ng/dL for each 5-point increase in BMI. BMI and age were the only independent predictors of cFT levels on multivariable analysis. Overall, 62 subjects (27.6%) met criteria for TD. Older age, greater BMI, and less frequent sexual activity were the only independent predictors of TD on multiple regression. We observed a 2.2-fold increase in the odds of TD for every 5-point increase in BMI, and a 1.8-fold increase for every 10 year increase in age. Men with ED and elevated BMI, advanced age, or infrequent sexual activity appear to be at high risk of TD, and such patients represent excellent potential candidates for targeted testosterone screening. Copyright © 2016 Elsevier Inc. All rights reserved.

Symptom Severity Predicts Prolonged Recovery after Sport-Related Concussion: Age and Amnesia Do Not

PubMed Central

Meehan, William P.; Mannix, Rebekah C.; Stracciolini, Andrea; Elbin, R.J.; Collins, Michael W.

2013-01-01

Objective To identify predictors of prolonged symptoms for athletes who sustain concussions. Study design We conducted a multi-center, prospective, cohort study of patients in 2 sport concussion clinics. Possible predictors of prolonged symptoms from concussion were compared between two groups: those whose symptoms resolved within 28 days and those whose symptoms persisted beyond 28 days. Candidate predictor variables were entered into a logistic regression model that was used to generate adjusted odds ratios. Results During the study period, 182 patients met inclusion criteria. The mean age was 15.2 years (SD 3.04 years). Over a third (N=65) of patients underwent computerized neurocognitive testing on their initial visit. In univariate analyses, Post Concussion Symptom Scale (PCSS) score and all composite scores on computerized neurocognitive testing appeared to be associated with prolonged symptom duration. Sex, age, loss of consciousness at time of injury and amnesia at time of injury were not associated with prolonged symptom duration. After adjusting for potential confounding, however, only total score on the PCSS score was associated with the odds of suffering prolonged symptoms. Conclusions After adjusting for other potential confounding variables, only total score on the PCSS was associated with the odds of suffering prolonged symptoms from sport-related concussions; age and amnesia were not. Further efforts to develop clinical tools for predicting which athletes will suffer prolonged recoveries after concussion should focus on initial symptom score. PMID:23628374

Most Likely to Succeed: Exploring Predictor Variables for the Counselor Preparation Comprehensive Examination

ERIC Educational Resources Information Center

Hartwig, Elizabeth Kjellstrand; Van Overschelde, James P.

2016-01-01

The authors investigated predictor variables for the Counselor Preparation Comprehensive Examination (CPCE) to examine whether academic variables, demographic variables, and test version were associated with graduate counseling students' CPCE scores. Multiple regression analyses revealed all 3 variables were statistically significant predictors of…

Persistent topographic quantitative EEG sequelae of chronic marihuana use: a replication study and initial discriminant function analysis.

PubMed

Struve, F A; Straumanis, J J; Patrick, G

1994-04-01

In a previous pilot study using psychiatric patients we reported that daily marihuana users had significant elevations of (1) Absolute Alpha Power, (2) Relative Alpha Power, and (3) Interhemispheric Alpha Coherence over both frontal and frontal-central areas when contrasted with subjects who did not use marihuana. We referred to this phenomenon as Hyperfrontality of Alpha. The study presented here is a successful replication of our previous findings using new samples of subjects and identical methods. Post hoc analyses based on the combined sample from both studies suggest that variables of psychiatric diagnoses and medication did not bias our results. In addition, a discriminant function analysis using quantitative EEG variables as candidate predictors generated a 95% correct THC user versus nonuser classification accuracy which received a successful jackknife replication.

Demographic factors affect willingness to register as an organ donor more than a personal relationship with a transplant candidate

PubMed Central

Regalia, Kirsten; Zheng, Patricia; Sillau, Stefan; Aggarwal, Anuj; Bellevue, Oliver; Fix, Oren K.; Prinz, Jennifer; Dunn, Susan; Biggins, Scott W.

2014-01-01

Background Transplant candidate caregivers (TCCs) are an under-utilized but potentially devoted pool of advocates who themselves may be recruited to register for deceased organ donation. Aim To assess and compare recruitment barriers to deceased donor registration efforts in TCCs and health fair attendees (HFAs). Methods A 42-item questionnaire assessing willingness to register as an organ donor and perceptions and knowledge about organ donation was administered to 452 participants (174 in Denver, 278 in San Francisco). Logistic regression, stratified by study site, was used to assess associations between explanatory variables and willingness to register as an organ donor. Results In Denver, 83% of TCCs vs 68% of HFAs indicated a willingness to register (p = 0.03). Controlling for study group (TCC vs HFA), predictors of willingness to register were female gender (OR 2.4), Caucasian race (OR 2.3), college graduate (OR 11.1), married (OR 2.4) and higher positive perception of organ donation (OR 1.2), each p<0.05. In San Francisco, 58% of TCCs vs 70% of HFAs indicated a willingness to register (p = 0.03). Controlling for study group (TCC vs HFA), predictors of willingness to register were Caucasian race (OR 3.5), college graduate (OR 2.2), married (OR 1.9), higher knowledge (OR 1.6) and higher positive perception of organ donation (OR 1.2), each p<0.05. In both locales, Caucasians were more likely to have positive perceptions about organ donation and were more willing to register. Conclusions Demographic characteristics, not personal connection to a transplant candidate, explain willingness to register as an organ donor. PMID:24519521

Environmental Risk Factors for the Incidence of American Cutaneous Leishmaniasis in a Sub-Andean Zone of Colombia (Chaparral, Tolima)

PubMed Central

Valderrama-Ardila, Carlos; Alexander, Neal; Ferro, Cristina; Cadena, Horacio; Marín, Dairo; Holford, Theodore R.; Munstermann, Leonard E.; Ocampo, Clara B.

2010-01-01

Environmental risk factors for cutaneous leishmaniasis were investigated for the largest outbreak recorded in Colombia. The outbreak began in 2003 in Chaparral, and in the following five years produced 2,313 cases in a population of 56,228. Candidate predictor variables were land use, elevation, and climatic variables such as mean temperature and precipitation. Spatial analysis showed that incidence of cutaneous leishmaniasis was higher in townships with mean temperatures in the middle of the county's range. Incidence was independently associated with higher coverage with forest or shrubs (2.6% greater for each additional percent coverage, 95% credible interval [CI] = 0.5–4.9%), and lower population density (22% lower for each additional 100 persons/km2, 95% CI = 7–41%). The extent of forest or shrub coverage did not show major changes over time. These findings confirmed the roles of climate and land use in leishmaniasis transmission. However, environmental variables were not sufficient to explain the spatial variation in incidence. PMID:20134000

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

PubMed

Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M Natália D S; Cagide Fajin, J Luis; Morell, Carlos; Ruiz, Reinaldo Molina; Cañizares-Carmenate, Yudith; Dominguez, Elena Rosa

2008-01-01

Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

A Rapid Approach to Modeling Species-Habitat Relationships

NASA Technical Reports Server (NTRS)

Carter, Geoffrey M.; Breinger, David R.; Stolen, Eric D.

2005-01-01

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Is It All Worth It? The Experiences of New PhDs on the Job Market, 2007-10

ERIC Educational Resources Information Center

McFall, Brooke Helppie; Murray-Close, Marta; Willis, Robert J.; Chen, Uniko

2015-01-01

The authors describe job market experiences of new PhD economists, 2007-10. Using information from PhD programs' job candidate Web sites and original surveys, they present information about job candidates' characteristics, preferences, and expectations; how job candidates fared at each stage of the market; and predictors of outcomes at…

Predicting change over time in career planning and career exploration for high school students.

PubMed

Creed, Peter A; Patton, Wendy; Prideaux, Lee-Ann

2007-06-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making self efficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted the outcome variable at T2; (c) whether the T1 predictor variables predicted change in the outcome variable from T1-T2; and (d) whether changes in the predictor variables from T1-T2 predicted change in the outcome variable from T1-T2. Strong associations (R(2)=34%) were identified for the T1 analysis (confidence, ability and paid work experience were positively associated with career planning/exploration). T1 variables were less useful predictors of career planning/exploration at T2 (R(2)=9%; having more confidence at T1 was associated with more career planning/exploration at T2) and change in career planning/exploration from T1-T2 (R(2)=11%; less confidence and no work experience were associated with change in career planning/exploration from T1-T2). When testing effect of changes in predictor variables predicting changes in outcome variable (R(2)=22%), three important predictors, indecision, work experience and confidence, were identified. Overall, results indicated important roles for self-efficacy and early work experiences in current and future career planning/exploration of high school students.

In Pursuit of the Elusive Elixir: Predictors of First Grade Reading.

ERIC Educational Resources Information Center

Porter, Robin

Multivariate sets of predictor variables including both cognitive and social variables, different types of preschool experiences, and family environment variables were used to predict the first-grade reading achievement of 144 first-grade boys and girls. Measures for the predictor variables had been taken at school entry and at the end of the…

Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin and cyclophosphamide chemotherapy response predictor in a multicenter randomized trial in breast cancer

PubMed Central

Tabchy, Adel; Valero, Vicente; Vidaurre, Tatiana; Lluch, Ana; Gomez, Henry; Martin, Miguel; Qi, Yuan; Barajas-Figueroa, Luis Javier; Souchon, Eduardo; Coutant, Charles; Doimi, Franco D; Ibrahim, Nuhad K; Gong, Yun; Hortobagyi, Gabriel N; Hess, Kenneth R; Symmans, W Fraser; Pusztai, Lajos

2010-01-01

Purpose We examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FAC×6 preoperative chemotherapy. We also performed an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms. Experimental Design 273 patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n=138), or FAC × 6 (n=135) neoadjuvant chemotherapy. All patients underwent a pretreatment FNA biopsy of the tumor for gene expression profiling and treatment response prediction. Results The pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (p<0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% (95%CI:21–56%), the negative predictive value (NPV) 88% (CI:77–95%) and the AUC 0.711. In the FAC arm, the PPV was 9% (CI:1–29%) and the AUC 0.584. This suggests that the genomic predictor may have regimen-specificity. Its performance was similar to a clinical variable-based predictor nomogram. Conclusions Gene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next generation predictive markers will need to be developed separately for different molecular subsets of breast cancers. PMID:20829329

Neuroticism Combined With Slower and More Variable Reaction Time: Synergistic Risk Factors for 7-Year Cognitive Decline in Females

PubMed Central

Shickle, Darren A.; Roberts, Beverly A.; Deary, Ian J.

2012-01-01

Objective. Among adults, slower and more variable reaction times are associated with worse cognitive function and increased mortality risk. Therefore, it is important to elucidate risk factors for reaction time change over the life course. Method. Data from the Health and Lifestyle Survey (HALS) were used to examine predictors of 7-year decline in reaction time (N = 4,260). Regression-derived factor scores were used to summarize general change across 4 reaction time variables: simple mean, 4-choice mean, simple variability, and 4-choice variability (53.52% of variance). Results. Age (B = .02, p < .001) and HALS1 baseline reaction time (B = −.10, p = .001) were significant risk factors for males (N = 1,899). In addition to these variables, in females (N = 2,361), neuroticism was significant and interacted synergistically with baseline reaction time (B = .06, p = .04). Adjustment for physiological variables explained the interaction with neuroticism, suggesting that candidate mechanisms had been identified. Discussion. A priority for future research is to replicate interactions between personality and reaction time in other samples and find specific mechanisms. Stratification of population data on cognitive health by personality and reaction time could improve strategies for identifying those at greater risk of cognitive decline. PMID:22367712

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Developing a school functioning index for middle schools.

PubMed

Birnbaum, Amanda S; Lytle, Leslie A; Perry, Cheryl L; Murray, David; Story, Mary

2003-08-01

Despite widespread recognition of schools' role in the healthy development of youth, surprisingly little research has examined the relationships between schools' overall functioning and the health-related behavior of students. School functioning could become an important predictor of students' health-related behavior and may be amenable to intervention. This paper describes the development and testing of the School Functioning Index (SFI) as a first step in investigating this question. The index was developed for use with middle schools and conceived as a predictor of students' violent behavior, with the potential for extending research applications to additional health and social behaviors. Using social cognitive theory, social ecological theory, and social disorganization theory as guides, three domains were identified to operationalize school functioning and identify candidate SFI items: 1) resources available to the school and students; 2) stability of the school population; and 3) the schools' performance as a socializing agent for students. Data for candidate SFI items were collected from public archives and directly from 16 middle schools participating in a school-based dietary intervention study. Data collection from schools, particularly concerning student aggressive behavior and disciplinary actions, presented challenges. The final SFI comprised nine items and demonstrated good internal consistency and variability. The SFI was modestly correlated in expected directions with violence and other health behaviors. This work supports the feasibility of combining multiple school-level indicators to create a measure of overall school functioning. Further investigation of validity and more acceptable data collection methods are warranted.

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

PubMed Central

Coupé, Christophe

2018-01-01

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

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

PubMed

Coupé, Christophe

2018-01-01

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

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

NASA Astrophysics Data System (ADS)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Autonomous motivation and quality of life as predictors of physical activity in patients with schizophrenia.

PubMed

Costa, Raquel; Bastos, Tânia; Probst, Michel; Seabra, André; Vilhena, Estela; Corredeira, Rui

2018-02-08

Being physically active is a complex behaviour in patients with schizophrenia. Several factors were identified as barriers to achieving active behaviours in this population. Therefore, the purpose of this study was to investigate among a number of barriers what predicts the most on physical activity (PA) in patients with schizophrenia. A total of 114 patients (28♀) with schizophrenia were included. Body mass index (BMI) was calculated. Autonomous and controlled motivation (Behavioural Regulation in Exercise Questionnaire - 3), self-esteem (Rosenberg Self-esteem scale), quality of life (World Health Organization Quality of Life Scale - Brief version) and functional exercise capacity (6-minute walk test - 6MWT) were evaluated. Multiple Regression Analysis was applied to assess the effect of these variables on Total PA per week (International Physical Activity Questionnaire - short version). Autonomous motivation and domains of quality of life were positively correlated with Total PA per week. Stepwise multiple regression analyses showed that of all the candidate factors to predict PA, autonomous motivation and global domain of quality of life were found as significant predictors. Our findings help to understand the importance of autonomous motivation and quality of life for PA in patients with schizophrenia. Knowledge about these predictors may provide guidance to improve PA behaviour in this population.

Predictors of Poor School Readiness in Children Without Developmental Delay at Age 2

PubMed Central

Dudovitz, Rebecca N.; Coker, Tumaini R.; Barnert, Elizabeth S.; Biely, Christopher; Li, Ning; Szilagyi, Peter G.; Larson, Kandyce; Halfon, Neal; Zimmerman, Frederick J.; Chung, Paul J.

2016-01-01

BACKGROUND AND OBJECTIVES: Current recommendations emphasize developmental screening and surveillance to identify developmental delays (DDs) for referral to early intervention (EI) services. Many young children without DDs, however, are at high risk for poor developmental and behavioral outcomes by school entry but are ineligible for EI. We developed models for 2-year-olds without DD that predict, at kindergarten entry, poor academic performance and high problem behaviors. METHODS: Data from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), were used for this study. The analytic sample excluded children likely eligible for EI because of DDs or very low birth weight. Dependent variables included low academic scores and high problem behaviors at the kindergarten wave. Regression models were developed by using candidate predictors feasibly obtainable during typical 2-year well-child visits. Models were cross-validated internally on randomly selected subsamples. RESULTS: Approximately 24% of all 2-year-old children were ineligible for EI at 2 years of age but still had poor academic or behavioral outcomes at school entry. Prediction models each contain 9 variables, almost entirely parental, social, or economic. Four variables were associated with both academic and behavioral risk: parental education below bachelor’s degree, little/no shared reading at home, food insecurity, and fair/poor parental health. Areas under the receiver-operating characteristic curve were 0.76 for academic risk and 0.71 for behavioral risk. Adding the mental scale score from the Bayley Short Form–Research Edition did not improve areas under the receiver-operating characteristic curve for either model. CONCLUSIONS: Among children ineligible for EI services, a small set of clinically available variables at age 2 years predicted academic and behavioral outcomes at school entry. PMID:27432845

Psychometric and demographic predictors of the perceived risk of terrorist threats and the willingness to pay for terrorism risk management programs.

PubMed

Mumpower, Jeryl L; Shi, Liu; Stoutenborough, James W; Vedlitz, Arnold

2013-10-01

A 2009 national telephone survey of 924 U.S. adults assessed perceptions of terrorism and homeland security issues. Respondents rated severity of effects, level of understanding, number affected, and likelihood of four terrorist threats: poisoned water supply; explosion of a small nuclear device in a major U.S. city; an airplane attack similar to 9/11; and explosion of a bomb in a building, train, subway, or highway. Respondents rated perceived risk and willingness to pay (WTP) for dealing with each threat. Demographic, attitudinal, and party affiliation data were collected. Respondents rated bomb as highest in perceived risk but gave the highest WTP ratings to nuclear device. For both perceived risk and WTP, psychometric variables were far stronger predictors than were demographic ones. OLS regression analyses using both types of variables to predict perceived risk found only two significant demographic predictors for any threat--Democrat (a negative predictor for bomb) and white male (a significant positive predictor for airline attack). In contrast, among psychometric variables, severity, number affected, and likelihood were predictors of all four threats and level of understanding was a predictor for one. For WTP, education was a negative predictor for three threats; no other demographic variables were significant predictors for any threat. Among psychometric variables, perceived risk and number affected were positive predictors of WTP for all four threats; severity and likelihood were predictors for three; level of understanding was a significant predictor for two. © 2013 Society for Risk Analysis.

Determining the factors affecting the distribution of Muscari latifolium, an endemic plant of Turkey, and a mapping species distribution model.

PubMed

Yilmaz, Hatice; Yilmaz, Osman Yalçın; Akyüz, Yaşar Feyza

2017-02-01

Species distribution modeling was used to determine factors among the large predictor candidate data set that affect the distribution of Muscari latifolium , an endemic bulbous plant species of Turkey, to quantify the relative importance of each factor and make a potential spatial distribution map of M. latifolium . Models were built using the Boosted Regression Trees method based on 35 presence and 70 absence records obtained through field sampling in the Gönen Dam watershed area of the Kazdağı Mountains in West Anatolia. Large candidate variables of monthly and seasonal climate, fine-scale land surface, and geologic and biotic variables were simplified using a BRT simplifying procedure. Analyses performed on these resources, direct and indirect variables showed that there were 14 main factors that influence the species' distribution. Five of the 14 most important variables influencing the distribution of the species are bedrock type, Quercus cerris density, precipitation during the wettest month, Pinus nigra density, and northness. These variables account for approximately 60% of the relative importance for determining the distribution of the species. Prediction performance was assessed by 10 random subsample data sets and gave a maximum the area under a receiver operating characteristic curve (AUC) value of 0.93 and an average AUC value of 0.8. This study provides a significant contribution to the knowledge of the habitat requirements and ecological characteristics of this species. The distribution of this species is explained by a combination of biotic and abiotic factors. Hence, using biotic interaction and fine-scale land surface variables in species distribution models improved the accuracy and precision of the model. The knowledge of the relationships between distribution patterns and environmental factors and biotic interaction of M. latifolium can help develop a management and conservation strategy for this species.

Situational and Intrapersonal Predictors of School and Life Satisfaction of Elementary School Students

ERIC Educational Resources Information Center

Drost, Amy Linden

2012-01-01

This study examined predictors of school and life satisfaction of fifth-grade students. Two situational predictor variables (school climate and school stress) and two intrapersonal predictor variables (locus of control and academic self-concept) were examined. It was hypothesized that positive school climate, low levels of school stress, internal…

Daily nest survival rates of Gunnison Sage-Grouse (Centrocercus minimus): assessing local- and landscape-scale drivers

USGS Publications Warehouse

Stanley, Thomas R.; Aldridge, Cameron L.; Joanne Saher,; Theresa Childers,

2015-01-01

The Gunnison Sage-Grouse (Centrocercus minimus) is a species of conservation concern and is a candidate for listing under the U.S. Endangered Species Act because of substantial declines in populations from historic levels. It is thought that loss, fragmentation, and deterioration of sagebrush (Artemisia spp.) habitat have contributed to the decline and isolation of this species into seven geographically distinct subpopulations. Nest survival is known to be a primary driver of demography of Greater Sage-Grouse (C. urophasianus), but no unbiased estimates of daily nest survival rates (hereafter nest survival) exist for Gunnison Sage-Grouse or published studies identifying factors that influence nest survival. We estimated nest survival of Gunnison Sage-Grouse for the western portion of Colorado's Gunnison Basin subpopulation, and assessed the effects and relative importance of local- and landscape-scale habitat characteristics on nest survival. Our top performing model was one that allowed variation in nest survival among areas, suggesting a larger landscape-area effect. Overall nest success during a 38-day nesting period (egg-laying plus incubation) was 50% (daily survival rate; SE  =  0.982 [0.003]), which is higher than previous estimates for Gunnison Sage-Grouse and generally higher than published for the closely related Greater Sage-Grouse. We did not find strong evidence that local-scale habitat variables were better predictors of nest survival than landscape-scale predictors, nor did we find strong evidence that any of the habitat variables we measured were good predictors of nest survival. Nest success of Gunnison Sage-Grouse in the western portion of the Gunnison Basin was higher than previously believed.

An AUC-based permutation variable importance measure for random forests

PubMed Central

2013-01-01

Background The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. Results We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. Conclusions The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html. PMID:23560875

An AUC-based permutation variable importance measure for random forests.

PubMed

Janitza, Silke; Strobl, Carolin; Boulesteix, Anne-Laure

2013-04-05

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html.

Take charge: Personality as predictor of recovery from eating disorder.

PubMed

Levallius, Johanna; Roberts, Brent W; Clinton, David; Norring, Claes

2016-12-30

Many treatments for eating disorders (ED) have demonstrated success. However, not all patients respond the same to interventions nor achieve full recovery, and obvious candidates like ED diagnosis and symptoms have generally failed to explain this variability. The current study investigated the predictive utility of personality for outcome in ED treatment. One hundred and thirty adult patients with bulimia nervosa or eating disorder not otherwise specified enrolled in an intensive multimodal treatment for 16 weeks. Personality was assessed with the NEO Personality Inventory Revised (NEO PI-R). Outcome was defined as recovered versus still ill and also as symptom score at termination with the Eating Disorder Inventory-2 (EDI-2). Personality significantly predicted both recovery (70% of patients) and symptom improvement. Patients who recovered reported significantly higher levels of Extraversion at baseline than the still ill, and Assertiveness emerged as the personality trait best predicting variance in outcome. This study indicates that personality might hold promise as predictor of recovery after treatment for ED. Future research might investigate if adding interventions to address personality features improves outcome for ED patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Theoretical predictor for candidate structure assignment from IMS data of biomolecule-related conformational space.

PubMed

Schenk, Emily R; Nau, Frederic; Fernandez-Lima, Francisco

2015-06-01

The ability to correlate experimental ion mobility data with candidate structures from theoretical modeling provides a powerful analytical and structural tool for the characterization of biomolecules. In the present paper, a theoretical workflow is described to generate and assign candidate structures for experimental trapped ion mobility and H/D exchange (HDX-TIMS-MS) data following molecular dynamics simulations and statistical filtering. The applicability of the theoretical predictor is illustrated for a peptide and protein example with multiple conformations and kinetic intermediates. The described methodology yields a low computational cost and a simple workflow by incorporating statistical filtering and molecular dynamics simulations. The workflow can be adapted to different IMS scenarios and CCS calculators for a more accurate description of the IMS experimental conditions. For the case of the HDX-TIMS-MS experiments, molecular dynamics in the "TIMS box" accounts for a better sampling of the molecular intermediates and local energy minima.

Meta-Analysis of Predictors of Dental School Performance

ERIC Educational Resources Information Center

DeCastro, Jeanette E.

2012-01-01

Accurate prediction of which candidates show the most promise of success in dental school is imperative for the candidates, the profession, and the public. Several studies suggested that predental GPAs and the Dental Admissions Test (DAT) produce a range of correlations with dental school performance measures. While there have been similarities,…

Hemodynamic exercise testing. A valuable tool in the selection of cardiac transplantation candidates.

PubMed

Chomsky, D B; Lang, C C; Rayos, G H; Shyr, Y; Yeoh, T K; Pierson, R N; Davis, S F; Wilson, J R

1996-12-15

Peak exercise oxygen consumption (Vo2), a noninvasive index of peak exercise cardiac output (CO), is widely used to select candidates for heart transplantation. However, peak exercise Vo2 can be influenced by noncardiac factors such as deconditioning, motivation, or body composition and may yield misleading prognostic information. Direct measurement of the CO response to exercise may avoid this problem and more accurately predict prognosis. Hemodynamic and ventilatory responses to maximal treadmill exercise were measured in 185 ambulatory patients with chronic heart failure who had been referred for cardiac transplantation (mean left ventricular ejection fraction, 22 +/- 7%; mean peak Vo2, 12.9 +/- 3.0 mL. min-1.kg-1). CO response to exercise was normal in 83 patients and reduced in 102. By univariate analysis, patients with normal CO responses had a better 1-year survival rate (95%) than did those with reduced CO responses (72%) (P < .0001). Survival in patients with peak Vo2 of > 14 mL.min-1.kg-1 (88%) was not different from that of patients with peak Vo2 of < or = 14 mL.min-1.kg-1 (79%) (P = NS). However, survival was worse in patients with peak Vo2 of < or = 10 mL.min-1.kg-1 (52%) versus those with peak Vo2 of > 10 mL.min-1.kg-1 (89%) (P < .0001). By Cox regression analysis, exercise CO response was the strongest independent predictor of survival (risk ratio, 4.3), with peak Vo2 dichotomized at 10 mL. min-1.kg-1 (risk ratio, 3.3) as the only other independent predictor. Patients with reduced CO responses and peak Vo2 of < or = 10 mL.min-1.kg-1 had an extremely poor 1-year survival rate (38%). Both CO response to exercise and peak exercise Vo2 provide valuable independent prognostic information in ambulatory patients with heart failure. These variables should be used in combination to select potential heart transplantation candidates.

Lifetime Self-Harm Behaviors Are Not More Prevalent in Bariatric Surgery Candidates than in Community Controls with Obesity.

PubMed

Müller, Astrid; Claes, Laurence; Smits, Dirk; Schag, Kathrin; de Zwaan, Martina

2018-01-01

The study aimed at investigating the lifetime prevalence of 22 self-harm behaviors in bariatric surgery candidates (pre-bariatric surgery group; PSG) compared to community controls with obesity (obese community group; OCG). The Self-Harm Inventory (SHI) was administered to the PSG (n = 139, BMI ≥ 35 kg/m2) and to the OCG (n = 122, BMI ≥ 35 kg/m2). Group comparison of cumulative SHI scores indicated a trend towards less endorsed SHI items in the PSG compared to the OCG (medianPSG = 1.00, IQRPSG = 2.00, medianOCG = 1.00, IQROCG = 2.25, U = 7.241, p = 0.033, η2 = 0.02). No significant group differences were found with regard to the rate of suicide attempts (12.4% vs. 9.4% for OCG vs. PSG). At least one type of lifetime self-harm behavior was admitted by 51.8% of the PSG and 63.9% of the OCG (χ2(1) = 3.91, p = 0.048). The results of logistic regressions using Firth's bias reduction method with at least one SHI item endorsed as dependent variable, group as categorical predictor (PSG as baseline), and age or BMI or PHQ-4 as continuous control variable indicated that only PHQ-4 had a positive effect on the odds ratio. The results suggest that self-harm (including suicidal attempts) is not more prevalent in bariatric surgery candidates than in community control participants with obesity. Further studies are needed to investigate self-harm in bariatric surgery patients, prior and following surgery, compared to non-operated patients with obesity. © 2018 The Author(s) Published by S. Karger GmbH, Freiburg.

Genetic Predictors of Susceptibility to Cutaneous Fungal Infections: a pilot Genome Wide Association Study to Refine a Candidate Gene Search

PubMed Central

Abdel-Rahman, Susan M.; Preuett, Barry L.

2012-01-01

Background Trichophyton tonsurans is the foremost fungal pathogen of minority children in the U.S. Despite overwhelming infection rates, it does not appear that this fungus infects children in a non-specific manner. Objective This study was designed to identify genes that may predispose or protect a child from T. tonsurans infection. Methods Children participating in an earlier longitudinal study wherein infection rates could be reliably determined were eligible for inclusion. DNA from a subset (n=40) of these children at the population extremes underwent whole genome genotyping (WGG). Allele frequencies between cases and controls were examined and significant SNPs were used to develop a candidate gene list for which the remainder of the cohort (n=115) were genotyped. Cumulative infection rate was examined by genotype and the ability of selected genotypes to predict the likelihood of infection explored by multivariable analysis. Results 23 genes with a putative mechanistic role in cutaneous infection were selected for evaluation. Of these, 21 demonstrated significant differences in infection rate between genotypes. A risk index assigned to genotypes in the 21 genes accounted for over 60% of the variability observed in infection rate (adjusted r2=0.665, p<0.001). Among these, 8 appeared to account for the majority of variability that was observed (r2=0.603, p<0.001). These included genes involved in: leukocyte activation and migration, extracellular matrix integrity and remodeling, epidermal maintenance and wound repair, and cutaneous permeability. Conclusions Applying WGG to individuals at the extremes of phenotype can help to guide the selection of candidate genes in populations of small cohorts where disease etiology is likely polygenic in nature. PMID:22704677

Global tilt and lumbar lordosis index: two parameters correlating with health-related quality of life scores-but how do they truly impact disability?

PubMed

Boissière, Louis; Takemoto, Mitsuru; Bourghli, Anouar; Vital, Jean-Marc; Pellisé, Ferran; Alanay, Ahmet; Yilgor, Caglar; Acaroglu, Emre; Perez-Grueso, Francisco Javier; Kleinstück, Frank; Obeid, Ibrahim

2017-04-01

Many radiological parameters have been reported to correlate with patient's disability including sagittal vertical axis (SVA), pelvic tilt (PT), and pelvic incidence minus lumbar lordosis (PI-LL). European literature reports other parameters such as lumbar lordosis index (LLI) and the global tilt (GT). If most parameters correlate with health-related quality of life scores (HRQLs), their impact on disability remains unclear. This study aimed to validate these parameters by investigating their correlation with HRQLs. It also aimed to evaluate the relationship between each of these sagittal parameters and HRQLs to fully understand the impact in adult spinal deformity management. A retrospective review of a multicenter, prospective database was carried out. The database inclusion criteria were adults (>18 years old) presenting any of the following radiographic parameters: scoliosis (Cobb ≥20°), SVA ≥5 cm, thoracic kyphosis ≥60° or PT ≥25°. All patients with complete data at baseline were included. Health-related quality of life scores, demographic variables (DVs), and radiographic parameters were collected at baseline. Differences in HRQLs among groups of each DV were assessed with analyses of variance. Correlations between radiographic variables and HRQLs were assessed using the Spearman rank correlation. Multivariate linear regression models were fitted for each of the HRQLs (Oswestry Disability Index [ODI], Scoliosis Research Society-22 subtotal score, or physical component summaries) with sagittal parameters and covariants as independent variables. A p<.05 value was considered statistically significant. Among a total of 755 included patients (mean age, 52.1 years), 431 were non-surgical candidates and 324 were surgical candidates. Global tilt and LLI significantly correlated with HRQLs (r=0.4 and -0.3, respectively) for univariate analysis. Demographic variables such as age, gender, body mass index, past surgery, and surgical or non-surgical candidate were significant predictors of ODI score. The likelihood ratio tests for the addition of the sagittal parameters showed that SVA, GT, T1 sagittal tilt, PI-LL, and LLI were statistically significant predictors for ODI score even adjusted for covariates. The differences of R 2 values from Model 1 were 1.5% at maximum, indicating that the addition of sagittal parameters to the reference model increased only 1.5% of the variance of ODI explained by the models. GT and LLI appear to be independent radiographic parameters impacting ODI variance. If most of the parameters described in the literature are correlated with ODI, the impact of these radiographic parameters is less than 2% of ODI variance, whereas 40% are explained by DVs. The importance of radiographic parameters lies more on their purpose to describe and understand the malalignment mechanisms than their univariate correlation with HRQLs. Copyright © 2016 Elsevier Inc. All rights reserved.

Employee Turnover: An Empirical and Methodological Assessment.

ERIC Educational Resources Information Center

Muchinsky, Paul M.; Tuttle, Mark L.

1979-01-01

Reviews research on the prediction of employee turnover. Groups predictor variables into five general categories: attitudinal (job satisfaction), biodata, work-related, personal, and test-score predictors. Consistent relationships between common predictor variables and turnover were found for four categories. Eight methodological problems/issues…

Modeling 3D Facial Shape from DNA

PubMed Central

Claes, Peter; Liberton, Denise K.; Daniels, Katleen; Rosana, Kerri Matthes; Quillen, Ellen E.; Pearson, Laurel N.; McEvoy, Brian; Bauchet, Marc; Zaidi, Arslan A.; Yao, Wei; Tang, Hua; Barsh, Gregory S.; Absher, Devin M.; Puts, David A.; Rocha, Jorge; Beleza, Sandra; Pereira, Rinaldo W.; Baynam, Gareth; Suetens, Paul; Vandermeulen, Dirk; Wagner, Jennifer K.; Boster, James S.; Shriver, Mark D.

2014-01-01

Human facial diversity is substantial, complex, and largely scientifically unexplained. We used spatially dense quasi-landmarks to measure face shape in population samples with mixed West African and European ancestry from three locations (United States, Brazil, and Cape Verde). Using bootstrapped response-based imputation modeling (BRIM), we uncover the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes. The facial effects of these variables are summarized as response-based imputed predictor (RIP) variables, which are validated using self-reported sex, genomic ancestry, and observer-based facial ratings (femininity and proportional ancestry) and judgments (sex and population group). By jointly modeling sex, genomic ancestry, and genotype, the independent effects of particular alleles on facial features can be uncovered. Results on a set of 20 genes showing significant effects on facial features provide support for this approach as a novel means to identify genes affecting normal-range facial features and for approximating the appearance of a face from genetic markers. PMID:24651127

Principal Selection Decisions Made by Teachers: The Influence of Principal Candidate Experience

ERIC Educational Resources Information Center

Winter, Paul A.; Jaeger, Mary Grace

2004-01-01

Public school teachers (N = 189) role-played as members of school councils making principal selection decisions by rating simulated candidates for principal vacancies. The independent variables were principal candidate job experience, candidate person characteristics, and teacher school level. The dependent variable was teacher rating of the job…

How Effective Are Military Academy Admission Standards

DTIC Science & Technology

2016-07-22

curriculum) 60 Leadership Composite Called the extracurricular composite; includes activities , leadership, and résumé 20 Selection Panel Score Consists of...score 60 Community Leadership Score Composite of the athletic activities score, the extracurricular activities score, and the faculty appraisal...promotion. Both the candidate fitness assessment and the athletic activities score are statistically significant predictors of graduation. The candidate

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

ERIC Educational Resources Information Center

Woolley, Kristin K.

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

Predictors of adjustment and growth in women with recurrent ovarian cancer.

PubMed

Ponto, Julie Ann; Ellington, Lee; Mellon, Suzanne; Beck, Susan L

2010-05-01

To analyze predictors of adjustment and growth in women who had experienced recurrent ovarian cancer using components of the Resiliency Model of Family Stress, Adjustment, and Adaptation as a conceptual framework. Cross-sectional. Participants were recruited from national cancer advocacy groups. 60 married or partnered women with recurrent ovarian cancer. Participants completed an online or paper survey. Independent variables included demographic and illness variables and meaning of illness. Outcome variables were psychological adjustment and post-traumatic growth. A model of five predictor variables (younger age, fewer years in the relationship, poorer performance status, greater symptom distress, and more negative meaning) accounted for 64% of the variance in adjustment but did not predict post-traumatic growth. This study supports the use of a model of adjustment that includes demographic, illness, and appraisal variables for women with recurrent ovarian cancer. Symptom distress and poorer performance status were the most significant predictors of adjustment. Younger age and fewer years in the relationship also predicted poorer adjustment. Nurses have the knowledge and skills to influence the predictors of adjustment to recurrent ovarian cancer, particularly symptom distress and poor performance status. Nurses who recognize the predictors of poorer adjustment can anticipate problems and intervene to improve adjustment for women.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

PubMed

Vilar, Santiago; Hripcsak, George

2016-01-01

Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

Examination of the Attitudes of Preschool Teacher Candidates and Teacher Candidates in Other Branches towards Scientific Research in Terms of Some Variables

ERIC Educational Resources Information Center

Ekici, Fatma Yasar

2017-01-01

The main objective of this study is to examine the attitudes of preschool teacher candidates and teacher candidates in other branches towards scientific research in terms of some variables. Survey method was used. The study group consists of 547 teacher candidates studying in education faculty of a private university in the spring term of…

Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis.

PubMed

Lewis, G N; Rice, D A; McNair, P J; Kluger, M

2015-04-01

Several studies have identified clinical, psychosocial, patient characteristic, and perioperative variables that are associated with persistent postsurgical pain; however, the relative effect of these variables has yet to be quantified. The aim of the study was to provide a systematic review and meta-analysis of predictor variables associated with persistent pain after total knee arthroplasty (TKA). Included studies were required to measure predictor variables prior to or at the time of surgery, include a pain outcome measure at least 3 months post-TKA, and include a statistical analysis of the effect of the predictor variable(s) on the outcome measure. Counts were undertaken of the number of times each predictor was analysed and the number of times it was found to have a significant relationship with persistent pain. Separate meta-analyses were performed to determine the effect size of each predictor on persistent pain. Outcomes from studies implementing uni- and multivariable statistical models were analysed separately. Thirty-two studies involving almost 30 000 patients were included in the review. Preoperative pain was the predictor that most commonly demonstrated a significant relationship with persistent pain across uni- and multivariable analyses. In the meta-analyses of data from univariate models, the largest effect sizes were found for: other pain sites, catastrophizing, and depression. For data from multivariate models, significant effects were evident for: catastrophizing, preoperative pain, mental health, and comorbidities. Catastrophizing, mental health, preoperative knee pain, and pain at other sites are the strongest independent predictors of persistent pain after TKA. © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predictors of Turkish Elementary Teacher Candidates' Energy Conservation Behaviors: An Approach on Value-Belief-Norm Theory

ERIC Educational Resources Information Center

Sahin, Elvan

2013-01-01

The present study aimed to explain elementary teacher candidates' energy conservation behaviors by using Value-Belief-Norm (VBN) Theory. Participants in this study were 512 students at Faculty of Education from two public universities in Turkey. Of the 512 students, 35.5% were enrolled in the early childhood education program, 30.9% were in the…

A candidate framework for PM2.5 source identification in highly industrialized urban-coastal areas

NASA Astrophysics Data System (ADS)

Mateus, Vinícius Lionel; Gioda, Adriana

2017-09-01

The variability of PM sources and composition impose tremendous challenges for police makers in order to establish guidelines. In urban PM, sources associated with industrial processes are among the most important ones. In this study, a 5-year monitoring of PM2.5 samples was carried out in an industrial district. Their chemical composition was strategically determined in two campaigns in order to check the effectiveness of mitigation policies. Gaseous pollutants (NO2, SO2, and O3) were also monitored along with meteorological variables. The new method called Conditional Bivariate Probability Function (CBPF) was successfully applied to allocate the observed concentration of criteria pollutants (gaseous pollutants and PM2.5) in cells defined by wind direction-speed which provided insights about ground-level and elevated pollution plumes. CBPF findings were confirmed by the Theil-Sen long trend estimations for criteria pollutants. By means of CBPF, elevated pollution plumes were detected in the range of 0.54-5.8 μg m-3 coming from a direction associated to stacks. With high interpretability, the use of Conditional Inference Trees (CIT) provided both classification and regression of the speciated PM2.5 in the two campaigns. The combination of CIT and Random Forests (RF) point out NO3- and Ca+2 as important predictors for PM2.5. The latter predictor mostly associated to non-sea-salt sources, given a nss-Ca2+ contribution equal to 96%.

Evidence-based selection process to the Master of Public Health program at Medical University.

PubMed

Panczyk, Mariusz; Juszczyk, Grzegorz; Zarzeka, Aleksander; Samoliński, Łukasz; Belowska, Jarosława; Cieślak, Ilona; Gotlib, Joanna

2017-09-11

Evaluation of the predictive validity of selected sociodemographic factors and admission criteria for Master's studies in Public Health at the Faculty of Health Sciences, Medical University of Warsaw (MUW). For the evaluation purposes recruitment data and learning results of students enrolled between 2008 and 2012 were used (N = 605, average age 22.9 ± 3.01). The predictive analysis was performed using the multiple linear regression method. In the proposed regression model 12 predictors were selected, including: sex, age, professional degree (BA), the Bachelor's studies grade point average (GPA), total score of the preliminary examination broken down into five thematic areas. Depending on the tested model, one of two dependent variables was used: first-year GPA or cumulative GPA in the Master program. The regression model based on the result variable of Master's GPA program was better matched to data in comparison to the model based on the first year GPA (adjusted R 2 0.413 versus 0.476 respectively). The Bachelor's studies GPA and each of the five subtests comprising the test entrance exam were significant predictors of success achieved by a student both after the first year and at the end of the course of studies. Criteria of admissions with total score of MCQs exam and Bachelor's studies GPA can be successfully used for selection of the candidates for Master's degree studies in Public Health. The high predictive validity of the recruitment system confirms the validity of the adopted admission policy at MUW.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

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

Changes in Situational and Dispositional Factors as Predictors of Job Satisfaction

ERIC Educational Resources Information Center

Keller, Anita C.; Semmer, Norbert K.

2013-01-01

Arguably, job satisfaction is one of the most important variables with regard to work. When explaining job satisfaction, research usually focuses on predictor variables in terms of levels but neglects growth rates. Therefore it remains unclear how potential predictors evolve over time and how their development affects job satisfaction. Using…

Predictors of posttraumatic stress symptoms following childbirth

PubMed Central

2014-01-01

Background Posttraumatic stress disorder (PTSD) following childbirth has gained growing attention in the recent years. Although a number of predictors for PTSD following childbirth have been identified (e.g., history of sexual trauma, emergency caesarean section, low social support), only very few studies have tested predictors derived from current theoretical models of the disorder. This study first aimed to replicate the association of PTSD symptoms after childbirth with predictors identified in earlier research. Second, cognitive predictors derived from Ehlers and Clark’s (2000) model of PTSD were examined. Methods N = 224 women who had recently given birth completed an online survey. In addition to computing single correlations between PTSD symptom severities and variables of interest, in a hierarchical multiple regression analyses posttraumatic stress symptoms were predicted by (1) prenatal variables, (2) birth-related variables, (3) postnatal social support, and (4) cognitive variables. Results Wellbeing during pregnancy and age were the only prenatal variables contributing significantly to the explanation of PTSD symptoms in the first step of the regression analysis. In the second step, the birth-related variables peritraumatic emotions and wellbeing during childbed significantly increased the explanation of variance. Despite showing significant bivariate correlations, social support entered in the third step did not predict PTSD symptom severities over and above the variables included in the first two steps. However, with the exception of peritraumatic dissociation all cognitive variables emerged as powerful predictors and increased the amount of variance explained from 43% to a total amount of 68%. Conclusions The findings suggest that the prediction of PTSD following childbirth can be improved by focusing on variables derived from a current theoretical model of the disorder. PMID:25026966

A Novel Study Paradigm for Long-term Prevention Trials in Alzheimer Disease: The Placebo Group Simulation Approach (PGSA): Application to MCI data from the NACC database.

PubMed

Berres, M; Kukull, W A; Miserez, A R; Monsch, A U; Monsell, S E; Spiegel, R

2014-01-01

The PGSA (Placebo Group Simulation Approach) aims at avoiding problems of sample representativeness and ethical issues typical of placebo-controlled secondary prevention trials with MCI patients. The PGSA uses mathematical modeling to forecast the distribution of quantified outcomes of MCI patient groups based on their own baseline data established at the outset of clinical trials. These forecasted distributions are then compared with the distribution of actual outcomes observed on candidate treatments, thus substituting for a concomitant placebo group. Here we investigate whether a PGSA algorithm that was developed from the MCI population of ADNI 1*, can reliably simulate the distribution of composite neuropsychological outcomes from a larger, independently selected MCI subject sample. Data available from the National Alzheimer's Coordinating Center (NACC) were used. We included 1523 patients with single or multiple domain amnestic mild cognitive impairment (aMCI) and at least two follow-ups after baseline. In order to strengthen the analysis and to verify whether there was a drift over time in the neuropsychological outcomes, the NACC subject sample was split into 3 subsamples of similar size. The previously described PGSA algorithm for the trajectory of a composite neuropsychological test battery (NTB) score was adapted to the test battery used in NACC. Nine demographic, clinical, biological and neuropsychological candidate predictors were included in a mixed model; this model and its error terms were used to simulate trajectories of the adapted NTB. The distributions of empirically observed and simulated data after 1, 2 and 3 years were very similar, with some over-estimation of decline in all 3 subgroups. The by far most important predictor of the NTB trajectories is the baseline NTB score. Other significant predictors are the MMSE baseline score and the interactions of time with ApoE4 and FAQ (functional abilities). These are essentially the same predictors as determined for the original NTB score. An algorithm comprising a small number of baseline variables, notably cognitive performance at baseline, forecasts the group trajectory of cognitive decline in subsequent years with high accuracy. The current analysis of 3 independent subgroups of aMCI patients from the NACC database supports the validity of the PGSA longitudinal algorithm for a NTB. Use of the PGSA in long-term secondary AD prevention trials deserves consideration.

A Pre-Screening Questionnaire to Predict Non-24-Hour Sleep-Wake Rhythm Disorder (N24HSWD) among the Blind

PubMed Central

Flynn-Evans, Erin E.; Lockley, Steven W.

2016-01-01

Study Objectives: There is currently no questionnaire-based pre-screening tool available to detect non-24-hour sleep-wake rhythm disorder (N24HSWD) among blind patients. Our goal was to develop such a tool, derived from gold standard, objective hormonal measures of circadian entrainment status, for the detection of N24HSWD among those with visual impairment. Methods: We evaluated the contribution of 40 variables in their ability to predict N24HSWD among 127 blind women, classified using urinary 6-sulfatoxymelatonin period, an objective marker of circadian entrainment status in this population. We subjected the 40 candidate predictors to 1,000 bootstrapped iterations of a logistic regression forward selection model to predict N24HSWD, with model inclusion set at the p < 0.05 level. We removed any predictors that were not selected at least 1% of the time in the 1,000 bootstrapped models and applied a second round of 1,000 bootstrapped logistic regression forward selection models to the remaining 23 candidate predictors. We included all questions that were selected at least 10% of the time in the final model. We subjected the selected predictors to a final logistic regression model to predict N24SWD over 1,000 bootstrapped models to calculate the concordance statistic and adjusted optimism of the final model. We used this information to generate a predictive model and determined the sensitivity and specificity of the model. Finally, we applied the model to a cohort of 1,262 blind women who completed the survey, but did not collect urine samples. Results: The final model consisted of eight questions. The concordance statistic, adjusted for bootstrapping, was 0.85. The positive predictive value was 88%, the negative predictive value was 79%. Applying this model to our larger dataset of women, we found that 61% of those without light perception, and 27% with some degree of light perception, would be referred for further screening for N24HSWD. Conclusions: Our model has predictive utility sufficient to serve as a pre-screening questionnaire for N24HSWD among the blind. Citation: Flynn-Evans EE, Lockley SW. A pre-screening questionnaire to predict non-24-hour sleep-wake rhythm disorder (N24HSWD) among the blind. J Clin Sleep Med 2016;12(5):703–710. PMID:26951421

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

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

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

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

Clinical predictors of positive urine cultures in young children at risk for urinary tract infection

PubMed Central

Couture, Élise; Labbé, Valérie; Cyr, Claude

2003-01-01

BACKGROUND: Urinary tract infections (UTIs) are a common source of bacterial infection among young febrile children. The diagnosis of UTI is challenging because the clinical presentation is not specific. OBJECTIVE: To describe clinical predictors to identify young children needing urine culture for evaluation of UTI. METHODS: Retrospective cohort study of all children younger than two years of age (719 hospital visits for 545 patients) suspected of having a UTI during a 12-month period. The outcome was UTI, defined as a catheterized urine culture with pure growth of 104 colonies/mL or greater, or suprapubic aspiration culture with 103 colonies/mL or greater. Candidate predictors included demographic, historical and physical examination variables. RESULTS: The medical records of 545 children younger than two years of age were reviewed. Forty-six per cent were girls. Mean age was 9.1 months (SD 7 months). Four variables were found to predict UTI: absence of another source of fever on examination (odds ratio [OR]=41.6 [95% CI, 8.8 to 197.4]), foul smelling urine (OR=19.7 [95% CI, 5.7 to 68.2]), white blood cell count greater than 15,000/mm3 (OR=4.3 [95% CI, 2.0 to 9.3]), younger than six months old (OR=3.1 [95% CI, 1.3 to 7.1]). The sensitivity of an abnormal urine analysis was 0.77 (95% CI, 0.66 to 0.88) and the specificity was 0.31 (95% CI, 0.2 to 0.42). CONCLUSION: An incremental increase in risk for UTI is associated with younger age (younger than six months), having a white blood cell count higher than 15,000/mm3, parental report of malodorous or foul smelling urine and the absence of an alternative source of fever. In the present patient population, obtaining a urine culture from children with at least one of these clinical predictors would have resulted in missing one UTI (2%), and 111 negative cultures (20%) would have been avoided. PMID:20020011

Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants.

PubMed

González, Mari Feli; Facal, David; Juncos-Rabadán, Onésimo; Yanguas, Javier

2017-10-01

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition. The total sample, drawn from the "Longitudinal Study Aging in Spain (ELES)" project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance. Education is the variable which best predicts cognitive performance in the 50-59, 60-69, and 70-79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model. Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

Oral Language, Sex and Socio-Economic Status as Predictors of Reading Achievement.

ERIC Educational Resources Information Center

Ebert, Dorothy Jo Williamson

This study was designed to discover the degree of relationship between a number of predictor variables and reading achievement for 65 black second grade students in two Austin, Texas, schools. The seven predictor variables used were: oral language performance as measured by the Gloria and David Beginning English, Series 20, Test 6 (GDBE); an…

Downscaling of daily precipitation using a hybrid model of Artificial Neural Network, Wavelet, and Quantile Mapping in Gharehsoo River Basin, Iran

NASA Astrophysics Data System (ADS)

Taie Semiromi, M.; Koch, M.

2017-12-01

Although linear/regression statistical downscaling methods are very straightforward and widely used, and they can be applied to a single predictor-predictand pair or spatial fields of predictors-predictands, the greatest constraint is the requirement of a normal distribution of the predictor and the predictand values, which means that it cannot be used to predict the distribution of daily rainfall because it is typically non-normal. To tacked with such a limitation, the current study aims to introduce a new developed hybrid technique taking advantages from Artificial Neural Networks (ANNs), Wavelet and Quantile Mapping (QM) for downscaling of daily precipitation for 10 rain-gauge stations located in Gharehsoo River Basin, Iran. With the purpose of daily precipitation downscaling, the study makes use of Second Generation Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma). Climate projections are available for three representative concentration pathways (RCPs) namely RCP 2.6, RCP 4.5 and RCP 8.5 for up to 2100. In this regard, 26 National Centers for Environmental Prediction (NCEP) reanalysis large-scale variables which have potential physical relationships with precipitation, were selected as candidate predictors. Afterwards, predictor screening was conducted using correlation, partial correlation and explained variance between predictors and predictand (precipitation). Depending on each rain-gauge station between two and three predictors were selected which their decomposed details (D) and approximation (A) obtained from discrete wavelet analysis were fed as inputs to the neural networks. After downscaling of daily precipitation, bias correction was conducted using quantile mapping. Out of the complete time series available, i.e. 1978-2005, two third of which namely 1978-1996 was used for calibration of QM and the reminder, i.e. 1997-2005 was considered for the validation. Result showed that the proposed hybrid method supported by QM for bias-correction could quite satisfactorily simulate daily precipitation. Also, results indicated that under all RCPs, precipitation will be more or less than 12% decreased by 2100. However, precipitation will be less decreased under RCP 8.5 compared with RCP 4.5.

A Genome-Wide Test of the Differential Susceptibility Hypothesis Reveals a Genetic Predictor of Differential Response to Psychological Treatments for Child Anxiety Disorders

PubMed Central

Keers, Robert; Coleman, Jonathan R.I.; Lester, Kathryn J.; Roberts, Susanna; Breen, Gerome; Thastum, Mikael; Bögels, Susan; Schneider, Silvia; Heiervang, Einar; Meiser-Stedman, Richard; Nauta, Maaike; Creswell, Cathy; Thirlwall, Kerstin; Rapee, Ronald M.; Hudson, Jennifer L.; Lewis, Cathryn; Plomin, Robert; Eley, Thalia C.

2016-01-01

Background The differential susceptibly hypothesis suggests that certain genetic variants moderate the effects of both negative and positive environments on mental health and may therefore be important predictors of response to psychological treatments. Nevertheless, the identification of such variants has so far been limited to preselected candidate genes. In this study we extended the differential susceptibility hypothesis from a candidate gene to a genome-wide approach to test whether a polygenic score of environmental sensitivity predicted response to cognitive behavioural therapy (CBT) in children with anxiety disorders. Methods We identified variants associated with environmental sensitivity using a novel method in which within-pair variability in emotional problems in 1,026 monozygotic twin pairs was examined as a function of the pairs' genotype. We created a polygenic score of environmental sensitivity based on the whole-genome findings and tested the score as a moderator of parenting on emotional problems in 1,406 children and response to individual, group and brief parent-led CBT in 973 children with anxiety disorders. Results The polygenic score significantly moderated the effects of parenting on emotional problems and the effects of treatment. Individuals with a high score responded significantly better to individual CBT than group CBT or brief parent-led CBT (remission rates: 70.9, 55.5 and 41.6%, respectively). Conclusions Pending successful replication, our results should be considered exploratory. Nevertheless, if replicated, they suggest that individuals with the greatest environmental sensitivity may be more likely to develop emotional problems in adverse environments but also benefit more from the most intensive types of treatment. PMID:27043157

VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making.

PubMed

Alverson, Charlotte Y; Yamamoto, Scott H

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school, secondary disability status, and total number of VR services. Competitive employment was the criterion variable. Only one predictor variable, Total Number of VR Services, was significant across all 10 years. IEP status in high school was not significant in any year. The remaining predictors were significant in one or more years. Further research and implications for researchers and practitioners are included.

Modeling Predictors of Duties Not Including Flying Status.

PubMed

Tvaryanas, Anthony P; Griffith, Converse

2018-01-01

The purpose of this study was to reuse available datasets to conduct an analysis of potential predictors of U.S. Air Force aircrew nonavailability in terms of being in "duties not to include flying" (DNIF) status. This study was a retrospective cohort analysis of U.S. Air Force aircrew on active duty during the period from 2003-2012. Predictor variables included age, Air Force Specialty Code (AFSC), clinic location, diagnosis, gender, pay grade, and service component. The response variable was DNIF duration. Nonparametric methods were used for the exploratory analysis and parametric methods were used for model building and statistical inference. Out of a set of 783 potential predictor variables, 339 variables were identified from the nonparametric exploratory analysis for inclusion in the parametric analysis. Of these, 54 variables had significant associations with DNIF duration in the final model fitted to the validation data set. The predicted results of this model for DNIF duration had a correlation of 0.45 with the actual number of DNIF days. Predictor variables included age, 6 AFSCs, 7 clinic locations, and 40 primary diagnosis categories. Specific demographic (i.e., age), occupational (i.e., AFSC), and health (i.e., clinic location and primary diagnosis category) DNIF drivers were identified. Subsequent research should focus on the application of primary, secondary, and tertiary prevention measures to ameliorate the potential impact of these DNIF drivers where possible.Tvaryanas AP, Griffith C Jr. Modeling predictors of duties not including flying status. Aerosp Med Hum Perform. 2018; 89(1):52-57.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Predictors of Seizure Threshold in Right Unilateral Ultrabrief Electroconvulsive Therapy: Role of Concomitant Medications and Anaesthesia Used.

PubMed

Gálvez, Verònica; Hadzi-Pavlovic, Dusan; Smith, Deidre; Loo, Colleen K

2015-01-01

An individualized approach to maximize electroconvulsive therapy (ECT) efficacy and minimize cognitive side effects is to treat patients relative to their seizure threshold (ST). However, although Right Unilateral-Ultrabrief (0.3 ms) (RUL-UB) ECT is increasingly used in clinical settings as an effective form of ECT with minimal cognitive effects, there is sparse data regarding predictors of ST. To analyze the relationship between ST and clinical and demographic factors in a sample of patients treated with RUL-UB ECT. Clinical, demographic and ECT data from 179 patients in ECT research studies were examined. Seizure threshold was titrated at the first ECT session. ECT was performed with a Thymatron(®) or Mecta(®) device, with thiopentone (2.5-5 mg/kg) or propofol (1-2 mg/kg) anaesthesia. Medications taken at the time of ST titration were documented. The association between ST and candidate predictor variables was examined with regression analysis. Multiple regression analyses showed that 34% of the variance in ST (P < 0.001) could be predicted. Older age (R(2) = 0.194, P < 0.001), propofol (vs thiopentone) (R(2) = 0.029, P ≤ 0.01) and higher anaesthetic dose (mg in propofol equivalents) (R(2) = 0.029, P < 0.05) were found to be predictors of higher initial ST. Treatment with lithium (R(2) = 0.043, P < 0.01) and study site (R(2) = 0.019, P < 0.05) significantly predicted lower initial ST. Empirical titration is recommended for accurate determination of ST in patients receiving RUL-UB ECT. Novel findings of this study are that propofol anaesthesia resulted in higher ST than thiopentone and concomitant treatment with lithium treatment lowered ST. Copyright © 2015 Elsevier Inc. All rights reserved.

Psychosocial predictors of quality of life and weight loss two years after bariatric surgery: Results from the Toronto Bari-PSYCH study.

PubMed

Sockalingam, Sanjeev; Hawa, Raed; Wnuk, Susan; Santiago, Vincent; Kowgier, Matthew; Jackson, Timothy; Okrainec, Allan; Cassin, Stephanie

2017-07-01

Studies exploring the impact of pre-surgery psychiatric status as a predictor of health related quality of life (QOL) after bariatric surgery have been limited to short-term follow-up and variable use of psychosocial measures. We examined the effect of pre-operative psychiatric factors on QOL and weight loss 2-years after surgery. 156 patients participated in this prospective cohort study, the Toronto Bariatric Psychosocial Cohort Study, between 2010 and 2014. Patients were assessed pre-surgery for demographic factors, weight, psychiatric diagnosis using the MINI International Neuropsychiatric Interview and symptom measures for QOL, depression and anxiety at pre-surgery and at 1 and 2years post-surgery. At 2-years post-bariatric surgery, patients experienced a significant decrease in mean weight (-48.43kg, 95% [-51.1, -45.76]) and an increase only in physical QOL (+18.91, 95% [17.01, 20.82]) scores as compared to pre-surgery. Multivariate regression analysis identified pre-surgery physical QOL score (p<0.001), younger age (p=0.005), and a history of a mood disorder as significant predictors of physical QOL. Only a history of a mood disorder (p=0.032) significantly predicted mental QOL (p=0.006). Pre-surgery weight (p<0.001) and a history of a mood disorder (p=0.047) were significant predictors of weight loss 2-years post-surgery. Bariatric surgery had a sustained impact on physical QOL but not mental QOL at 2-years post-surgery. A history of mood disorder unexpectedly increased physical QOL scores and weight loss following surgery. Further research is needed to determine if these results are due to bariatric surgery candidate selection within this program. Copyright © 2017 Elsevier Inc. All rights reserved.

Predictors of In-Hospital Mortality After Rapid Response Team Calls in a 274 Hospital Nationwide Sample.

PubMed

Shappell, Claire; Snyder, Ashley; Edelson, Dana P; Churpek, Matthew M

2018-07-01

Despite wide adoption of rapid response teams across the United States, predictors of in-hospital mortality for patients receiving rapid response team calls are poorly characterized. Identification of patients at high risk of death during hospitalization could improve triage to intensive care units and prompt timely reevaluations of goals of care. We sought to identify predictors of in-hospital mortality in patients who are subjects of rapid response team calls and to develop and validate a predictive model for death after rapid response team call. Analysis of data from the national Get with the Guidelines-Medical Emergency Team event registry. Two-hundred seventy four hospitals participating in Get with the Guidelines-Medical Emergency Team from June 2005 to February 2015. 282,710 hospitalized adults on surgical or medical wards who were subjects of a rapid response team call. None. The primary outcome was death during hospitalization; candidate predictors included patient demographic- and event-level characteristics. Patients who died after rapid response team were older (median age 72 vs 66 yr), were more likely to be admitted for noncardiac medical illness (70% vs 58%), and had greater median length of stay prior to rapid response team (81 vs 47 hr) (p < 0.001 for all comparisons). The prediction model had an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.78-0.79), with systolic blood pressure, time since admission, and respiratory rate being the most important variables. Patients who die following rapid response team calls differ significantly from surviving peers. Recognition of these factors could improve postrapid response team triage decisions and prompt timely goals of care discussions.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Hepatitis C virus in the new era: perspectives in epidemiology, prevention, diagnostics and predictors of response to therapy.

PubMed

Ansaldi, Filippo; Orsi, Andrea; Sticchi, Laura; Bruzzone, Bianca; Icardi, Giancarlo

2014-08-07

Despite the great successes achieved in the fields of virology and diagnostics, several difficulties affect improvements in hepatitis C virus (HCV) infection control and eradication in the new era. New HCV infections still occur, especially in some of the poorest regions of the world, where HCV is endemic and long-term sequelae have a growing economic and health burden. An HCV vaccine is still no available, despite years of researches and discoveries about the natural history of infection and host-virus interactions: several HCV vaccine candidates have been developed in the last years, targeting different HCV antigens or using alternative delivery systems, but viral variability and adaption ability constitute major challenges for vaccine development. Many new antiviral drugs for HCV therapy are in preclinical or early clinical development, but different limitations affect treatment validity. Treatment predictors are important tools, as they provide some guidance for the management of therapy in patients with chronic HCV infection: in particular, the role of host genomics in HCV infection outcomes in the new era of direct-acting antivirals may evolve for new therapeutic targets, representing a chance for modulated and personalized treatment management, when also very potent therapies will be available. In the present review we discuss the most recent data about HCV epidemiology, the new perspectives for the prevention of HCV infection and the most recent evidence regarding HCV diagnosis, therapy and predictors of response to it.

Comparison of statistical methods for detection of serum lipid biomarkers for mesothelioma and asbestos exposure.

PubMed

Xu, Rengyi; Mesaros, Clementina; Weng, Liwei; Snyder, Nathaniel W; Vachani, Anil; Blair, Ian A; Hwang, Wei-Ting

2017-07-01

We compared three statistical methods in selecting a panel of serum lipid biomarkers for mesothelioma and asbestos exposure. Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance. Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control. Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.

Peer Educators and Close Friends as Predictors of Male College Students' Willingness to Prevent Rape

ERIC Educational Resources Information Center

Stein, Jerrold L.

2007-01-01

Astin's (1977, 1991, 1993) input-environment-outcome (I-E-O) model provided a conceptual framework for this study which measured 156 male college students' willingness to prevent rape (outcome variable). Predictor variables included personal attitudes (input variable), perceptions of close friends' attitudes toward rape and rape prevention…

Public Mood and Consumption Choices: Evidence from Sales of Sony Cameras on Taobao

PubMed Central

Ma, Qingguo; Zhang, Wuke

2015-01-01

Previous researchers have tried to predict social and economic phenomena with indicators of public mood, which were extracted from online data. This method has been proved to be feasible in many areas such as financial markets, economic operations and even national suicide numbers. However, few previous researches have examined the relationship between public mood and consumption choices at society level. The present study paid attention to the “Diaoyu Island” event, and extracted Chinese public mood data toward Japan from Sina MicroBlog (the biggest social media in China), which demonstrated a significant cross-correlation between the public mood variable and sales of Sony cameras on Taobao (the biggest Chinese e-business company). Afterwards, several candidate predictors of sales were examined and finally three significant stepwise regression models were obtained. Results of models estimation showed that significance (F-statistics), R-square and predictive accuracy (MAPE) all improved due to inclusion of public mood variable. These results indicate that public mood is significantly associated with consumption choices and may be of value in sales forecasting for particular products. PMID:25902358

Public mood and consumption choices: evidence from sales of Sony cameras on Taobao.

PubMed

Ma, Qingguo; Zhang, Wuke

2015-01-01

Previous researchers have tried to predict social and economic phenomena with indicators of public mood, which were extracted from online data. This method has been proved to be feasible in many areas such as financial markets, economic operations and even national suicide numbers. However, few previous researches have examined the relationship between public mood and consumption choices at society level. The present study paid attention to the "Diaoyu Island" event, and extracted Chinese public mood data toward Japan from Sina MicroBlog (the biggest social media in China), which demonstrated a significant cross-correlation between the public mood variable and sales of Sony cameras on Taobao (the biggest Chinese e-business company). Afterwards, several candidate predictors of sales were examined and finally three significant stepwise regression models were obtained. Results of models estimation showed that significance (F-statistics), R-square and predictive accuracy (MAPE) all improved due to inclusion of public mood variable. These results indicate that public mood is significantly associated with consumption choices and may be of value in sales forecasting for particular products.

PI-RADS version 2 for prediction of pathological downgrading after radical prostatectomy: a preliminary study in patients with biopsy-proven Gleason Score 7 (3+4) prostate cancer.

PubMed

Woo, Sungmin; Kim, Sang Youn; Lee, Joongyub; Kim, Seung Hyup; Cho, Jeong Yeon

2016-10-01

To evaluate PI-RADSv2 for predicting pathological downgrading after radical prostatectomy (RP) in patients with biopsy-proven Gleason score (GS) 7(3+4) PC. A total of 105 patients with biopsy-proven GS 7(3+4) PC who underwent multiparametric prostate MRI followed by RP were included. Two radiologists assigned PI-RADSv2 scores for each patient. Preoperative clinicopathological variables and PI-RADSv2 scores were compared between patients with and without downgrading after RP using the Wilcoxon rank sum test or Fisher's exact test. Logistic regression analyses with Firth's bias correction were performed to assess their association with downgrading. Pathological downgrading was identified in ten (9.5 %) patients. Prostate-specific antigen (PSA), PSA density, percentage of cores with GS 7(3+4), and greatest percentage of core length (GPCL) with GS 7(3+4) were significantly lower in patients with downgrading (p = 0.002-0.037). There was no significant difference in age and clinical stage (p = 0.537-0.755). PI-RADSv2 scores were significantly lower in patients with downgrading (3.8 versus 4.4, p = 0.012). At univariate logistic regression analysis, PSA, PSA density, and PI-RADSv2 scores were significant predictors of downgrading (p = 0.003-0.022). Multivariate analysis revealed only PSA density and PI-RADSv2 scores as independent predictors of downgrading (p = 0.014-0.042). The PI-RADSv2 scoring system was an independent predictor of pathological downgrading after RP in patients with biopsy-proven GS 7(3+4) PC. • PI-RADSv2 was an independent predictor of downgrading in biopsy-proven GS 7(3+4) PC • PSA density was also an independent predictor of downgrading • MRI may assist in identifying AS candidates in biopsy-proven GS 7(3+4) PC patients.

The Impact of Individual Differences on E-Learning System Behavioral Intention

NASA Astrophysics Data System (ADS)

Liao, Peiwen; Yu, Chien; Yi, Chincheh

This study investigated the impact of contingent variables on the relationship between four predictors and employees' behavioral intention with e-learning. Seven hundred and twenty-two employees in online training and education were asked to answer questionnaires about their learning styles, perceptions of the quality of the proposed predictors and behavioral intention with e-learning systems. The results of analysis showed that three contingent variables, gender, job title and industry, significantly influenced the perceptions of predictors and employees' behavioral intention with the e-learning system. This study also found a statistically significant moderating effect of two contingent variables, gender, job title and industry, on the relationship between predictors and e-learning system behavioral intention. The results suggest that a serious consideration of contingent variables is crucial for improving e-learning system behavioral intention. The implications of these results for the management of e-learning systems are discussed.

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental.

PubMed

Shieh, Gwowen

2010-05-28

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term. This article attempts to clarify the misconception of multicollinearity in MMR studies. The counterintuitive yet beneficial effects of multicollinearity on the ability to detect moderator relationships are explored. Comprehensive treatments and numerical investigations are presented for the simplest interaction model and more complex three-predictor setting. The results provide critical insight that both helps avoid misleading interpretations and yields better understanding for the impact of intercorrelation among predictor variables in MMR analyses.

The Investigation of Elementary Mathematics Teacher Candidates' Problem Solving Skills According to Various Variables

ERIC Educational Resources Information Center

Kaya, Deniz; Izgiol, Dilek; Kesan, Cenk

2014-01-01

The aim was to determine elementary mathematics teacher candidates' problem solving skills and analyze problem solving skills according to various variables. The data were obtained from total 306 different grade teacher candidates receiving education in Department of Elementary Mathematics Education, Buca Faculty of Education, Dokuz Eylul…

Assessing risk factors for dental caries: a statistical modeling approach.

PubMed

Trottini, Mario; Bossù, Maurizio; Corridore, Denise; Ierardo, Gaetano; Luzzi, Valeria; Saccucci, Matteo; Polimeni, Antonella

2015-01-01

The problem of identifying potential determinants and predictors of dental caries is of key importance in caries research and it has received considerable attention in the scientific literature. From the methodological side, a broad range of statistical models is currently available to analyze dental caries indices (DMFT, dmfs, etc.). These models have been applied in several studies to investigate the impact of different risk factors on the cumulative severity of dental caries experience. However, in most of the cases (i) these studies focus on a very specific subset of risk factors; and (ii) in the statistical modeling only few candidate models are considered and model selection is at best only marginally addressed. As a result, our understanding of the robustness of the statistical inferences with respect to the choice of the model is very limited; the richness of the set of statistical models available for analysis in only marginally exploited; and inferences could be biased due the omission of potentially important confounding variables in the model's specification. In this paper we argue that these limitations can be overcome considering a general class of candidate models and carefully exploring the model space using standard model selection criteria and measures of global fit and predictive performance of the candidate models. Strengths and limitations of the proposed approach are illustrated with a real data set. In our illustration the model space contains more than 2.6 million models, which require inferences to be adjusted for 'optimism'.

Standardized reporting guidelines for emergency department syncope risk-stratification research.

PubMed

Sun, Benjamin C; Thiruganasambandamoorthy, Venkatesh; Cruz, Jeffrey Dela

2012-06-01

There is increasing research interest in the risk stratification of emergency department (ED) syncope patients. A major barrier to comparing and synthesizing existing research is wide variation in the conduct and reporting of studies. The authors wanted to create standardized reporting guidelines for ED syncope risk-stratification research using an expert consensus process. In that pursuit, a panel of syncope researchers was convened and a literature review was performed to identify candidate reporting guideline elements. Candidate elements were grouped into four sections: eligibility criteria, outcomes, electrocardiogram (ECG) findings, and predictors. A two-round, modified Delphi consensus process was conducted using an Internet-based survey application. In the first round, candidate elements were rated on a five-point Likert scale. In the second round, panelists rerated items after receiving information about group ratings from the first round. Items that were rated by >80% of the panelists at the two highest levels of the Likert scale were included in the final guidelines. There were 24 panelists from eight countries who represented five clinical specialties. The panel identified an initial set of 183 candidate elements. After two survey rounds, the final reporting guidelines included 92 items that achieved >80% consensus. These included 10 items for study eligibility, 23 items for outcomes, nine items for ECG abnormalities, and 50 items for candidate predictors. Adherence to these guidelines should facilitate comparison of future research in this area. © 2012 by the Society for Academic Emergency Medicine.

Peak oxygen consumption measured during the stair-climbing test in lung resection candidates.

PubMed

Brunelli, Alessandro; Xiumé, Francesco; Refai, Majed; Salati, Michele; Di Nunzio, Luca; Pompili, Cecilia; Sabbatini, Armando

2010-01-01

The stair-climbing test is commonly used in the preoperative evaluation of lung resection candidates, but it is difficult to standardize and provides little physiologic information on the performance. To verify the association between the altitude and the V(O2peak) measured during the stair-climbing test. 109 consecutive candidates for lung resection performed a symptom-limited stair-climbing test with direct breath-by-breath measurement of V(O2peak) by a portable gas analyzer. Stepwise logistic regression and bootstrap analyses were used to verify the association of several perioperative variables with a V(O2peak) <15 ml/kg/min. Subsequently, multiple regression analysis was also performed to develop an equation to estimate V(O2peak) from stair-climbing parameters and other patient-related variables. 56% of patients climbing <14 m had a V(O2peak) <15 ml/kg/min, whereas 98% of those climbing >22 m had a V(O2peak) >15 ml/kg/min. The altitude reached at stair-climbing test resulted in the only significant predictor of a V(O2peak) <15 ml/kg/min after logistic regression analysis. Multiple regression analysis yielded an equation to estimate V(O2peak) factoring altitude (p < 0.0001), speed of ascent (p = 0.005) and body mass index (p = 0.0008). There was an association between altitude and V(O2peak) measured during the stair-climbing test. Most of the patients climbing more than 22 m are able to generate high values of V(O2peak) and can proceed to surgery without any additional tests. All others need to be referred for a formal cardiopulmonary exercise test. In addition, we were able to generate an equation to estimate V(O2peak), which could assist in streamlining the preoperative workup and could be used across different settings to standardize this test. Copyright (c) 2010 S. Karger AG, Basel.

Predicting Change over Time in Career Planning and Career Exploration for High School Students

ERIC Educational Resources Information Center

Creed, Peter A.; Patton, Wendy; Prideaux, Lee-Ann

2007-01-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making selfefficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted…

Organizational commitment as a predictor variable in nursing turnover research: literature review.

PubMed

Wagner, Cheryl M

2007-11-01

This paper is a report of a literature review to (1) demonstrate the predictability of organizational commitment as a variable, (2) compare organizational commitment and job satisfaction as predictor variables and (3) determine the usefulness of organizational commitment in nursing turnover research. Organizational commitment is not routinely selected as a predictor variable in nursing studies, although the evidence suggests that it is a reliable predictor. Findings from turnover studies can help determine the previous performance of organizational commitment, and be compared to those of studies using the more conventional variable of job satisfaction. Published research studies in English were accessed for the period 1960-2006 using the CINAHL, EBSCOHealthsource Nursing, ERIC, PROQUEST, Journals@OVID, PubMed, PsychINFO, Health and Psychosocial Instruments (HAPI) and COCHRANE library databases and Business Source Premier. The search terms included nursing turnover, organizational commitment or job satisfaction. Only studies reporting mean comparisons, R(2) or beta values related to organizational commitment and turnover or turnover antecedents were included in the review. There were 25 studies in the final data set, with a subset of 23 studies generated to compare the variables of organizational commitment and job satisfaction. Results indicated robust indirect predictability of organizational commitment overall, with greater predictability by organizational commitment vs job satisfaction. Organizational commitment is a useful predictor of turnover in nursing research, and effective as a variable with the most direct impact on antecedents of turnover such as intent to stay. The organizational commitment variable should be routinely employed in nursing turnover research studies.

The prehospital intravenous access assessment: a prospective study on intravenous access failure and access delay in prehospital emergency medicine.

PubMed

Prottengeier, Johannes; Albermann, Matthias; Heinrich, Sebastian; Birkholz, Torsten; Gall, Christine; Schmidt, Joachim

2016-12-01

Intravenous access in prehospital emergency care allows for early administration of medication and extended measures such as anaesthesia. Cannulation may, however, be difficult, and failure and resulting delay in treatment and transport may have negative effects on the patient. Therefore, our study aims to perform a concise assessment of the difficulties of prehospital venous cannulation. We analysed 23 candidate predictor variables on peripheral venous cannulations in terms of cannulation failure and exceedance of a 2 min time threshold. Multivariate logistic regression models were fitted for variables of predictive value (P<0.25) and evaluated by the area under the curve (AUC>0.6) of their respective receiver operating characteristic curve. A total of 762 intravenous cannulations were enroled. In all, 22% of punctures failed on the first attempt and 13% of punctures exceeded 2 min. Model selection yielded a three-factor model (vein visibility without tourniquet, vein palpability with tourniquet and insufficient ambient lighting) of fair accuracy for the prediction of puncture failure (AUC=0.76) and a structurally congruent model of four factors (failure model factors plus vein visibility with tourniquet) for the exceedance of the 2 min threshold (AUC=0.80). Our study offers a simple assessment to identify cases of difficult intravenous access in prehospital emergency care. Of the numerous factors subjectively perceived as possibly exerting influences on cannulation, only the universal - not exclusive to emergency care - factors of lighting, vein visibility and palpability proved to be valid predictors of cannulation failure and exceedance of a 2 min threshold.

Assessment of the uncertainty and predictive power of large-scale predictors for nonlinear precipitation downscaling in the European Arctic (Invited)

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

Despite the extensive research on downscaling methods there is still little consensus about the choice of useful atmospheric predictor variables. Besides the general decision of a proper statistical downscaling model, the selection of an informative predictor set is crucial for the accuracy and stability of the resulting downscaled time series. These requirements must be fullfilled by both the atmospheric variables and the predictor domains in terms of geographical location and spatial extend, to which in general not much attention is paid. However, only a limited number of studies is interested in the predictive capability of the predictor domain size or shape, and the question to what extent variability of neighboring grid points influence local-scale events. In this study we emphasized the spatial relationships between observed daily precipitation and selected number of atmospheric variables for the European Arctic. Several nonlinear regression models are used to link the large-scale predictors obtained from reanalysed Weather Research and Forecast model runs to the local-scale observed precipitation. Inferences on the sources of uncertainty are then drawn from variance based sensitivity measures, which also permit to capture interaction effects between individual predictors. The information is further used to develop more parsimonious downscaling models with only small decreases in accuracy. Individual predictors (without interactions) account for almost 2/3 of the total output variance, while the remaining fraction is solely due to interactions. Neglecting predictor interactions in the screening process will lead to some loss of information. Hence, linear screening methods are insufficient as they neither account for interactions nor for non-additivity as given by many nonlinear prediction algorithms.

Patient or treatment centre? Where are efforts invested to improve cancer patients' psychosocial outcomes?

PubMed Central

Carey, ML; Clinton-McHarg, T; Sanson-Fisher, RW; Campbell, S; Douglas, HE

2011-01-01

The psychosocial outcomes of cancer patients may be influenced by individual-level, social and treatment centre predictors. This paper aimed to examine the extent to which individual, social and treatment centre variables have been examined as predictors or targets of intervention for psychosocial outcomes of cancer patients. Medline was searched to find studies in which the psychological outcomes of cancer patient were primary variables. Papers published in English between 1999 and 2009 that reported primary data relevant to psychosocial outcomes for cancer patients were included, with 20% randomly selected for further coding. Descriptive studies were coded for inclusion of individual, social or treatment centre variables. Intervention studies were coded to determine if the unit of intervention was the individual patient, social unit or treatment centre. After random sampling, 412 publications meeting the inclusion criteria were identified, 169 were descriptive and 243 interventions. Of the descriptive papers 95.0% included individual predictors, and 5.0% social predictors. None of the descriptive papers examined treatment centre variables as predictors of psychosocial outcomes. Similarly, none of the interventions evaluated the effectiveness of treatment centre interventions for improving psychosocial outcomes. Potential reasons for the overwhelming dominance of individual predictors and individual-focused interventions in psychosocial literature are discussed. PMID:20646035

Efficiency in Reading Comprehension: A Comparison of Students' Competency in Reading Printed and Digital Texts

ERIC Educational Resources Information Center

Duran, Erol

2013-01-01

In this study, survey model was used, for investigating the effect of printed and electronic texts on the reading comprehension levels of teacher candidates. While dependent variable of the research comprises the levels of understanding of the teacher candidates, independent variable comprises the departments of the teacher candidates, types of…

The Relationship between Admission Criteria and TExES Generalist EC-6: An Archival Study of Predictors of Future Success in the Teacher Preparation Program at a Historical Black College in Southwest Texas

ERIC Educational Resources Information Center

Warren, Sonja Barnes

2017-01-01

A Review of the Nation's Teacher Preparation Programs, which studies and analyzes the results of all the teacher education programs and teacher candidates' examination scores in the United States, reveals disturbing statistics about the performance of teacher candidates on their certification examinations and the adverse effects this poor…

Early identification of patients requiring massive transfusion, embolization, or hemostatic surgery for traumatic hemorrhage: a systematic review protocol.

PubMed

Tran, Alexandre; Matar, Maher; Steyerberg, Ewout W; Lampron, Jacinthe; Taljaard, Monica; Vaillancourt, Christian

2017-04-13

Hemorrhage is a major cause of early mortality following a traumatic injury. The progression and consequences of significant blood loss occur quickly as death from hemorrhagic shock or exsanguination often occurs within the first few hours. The mainstay of treatment therefore involves early identification of patients at risk for hemorrhagic shock in order to provide blood products and control of the bleeding source if necessary. The intended scope of this review is to identify and assess combinations of predictors informing therapeutic decision-making for clinicians during the initial trauma assessment. The primary objective of this systematic review is to identify and critically assess any existing multivariable models predicting significant traumatic hemorrhage that requires intervention, defined as a composite outcome comprising massive transfusion, surgery for hemostasis, or angiography with embolization for the purpose of external validation or updating in other study populations. If no suitable existing multivariable models are identified, the secondary objective is to identify candidate predictors to inform the development of a new prediction rule. We will search the EMBASE and MEDLINE databases for all randomized controlled trials and prospective and retrospective cohort studies developing or validating predictors of intervention for traumatic hemorrhage in adult patients 16 years of age or older. Eligible predictors must be available to the clinician during the first hour of trauma resuscitation and may be clinical, lab-based, or imaging-based. Outcomes of interest include the need for surgical intervention, angiographic embolization, or massive transfusion within the first 24 h. Data extraction will be performed independently by two reviewers. Items for extraction will be based on the CHARMS checklist. We will evaluate any existing models for relevance, quality, and the potential for external validation and updating in other populations. Relevance will be described in terms of appropriateness of outcomes and predictors. Quality criteria will include variable selection strategies, adequacy of sample size, handling of missing data, validation techniques, and measures of model performance. This systematic review will describe the availability of multivariable prediction models and summarize evidence regarding predictors that can be used to identify the need for intervention in patients with traumatic hemorrhage. PROSPERO CRD42017054589.

Multicollinearity and Regression Analysis

NASA Astrophysics Data System (ADS)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Variables Affecting Preservice Teacher Candidate Identification of Teacher Sexual Misconduct

ERIC Educational Resources Information Center

Haverland, Jeffrey A.

2017-01-01

Using a quantitative research model, this study explored variables affecting pre-service teacher candidate identification of teacher sexual misconduct through a scenario-based survey instrument. Independent variables in this study were respondent gender, student gender, teacher gender, student age-related ambiguity (students depicted were 17),…

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

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

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

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era.

PubMed

Zador, Zsolt; Huang, Wendy; Sperrin, Matthew; Lawton, Michael T

2018-06-01

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

Identifying gnostic predictors of the vaccine response.

PubMed

Haining, W Nicholas; Pulendran, Bali

2012-06-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis of their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of vaccine prediction. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. Copyright © 2012 Elsevier Ltd. All rights reserved.

Identifying gnostic predictors of the vaccine response

PubMed Central

Haining, W. Nicholas; Pulendran, Bali

2012-01-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis for their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of outcome classification. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. PMID:22633886

Psychosocial predictors of the onset of anxiety disorders in women: Results from a prospective 3-year longitudinal study

PubMed Central

Calkins, Amanda W.; Otto, Michael W.; Cohen, Lee S.; Soares, Claudio N.; Vitonis, Alison F.; Hearon, Bridget A.; Harlow, Bernard L.

2009-01-01

In a prospective, longitudinal, population-based study of 643 women participating in the Harvard Study of Moods and Cycles we examined whether psychosocial variables predicted a new or recurrent onset of an anxiety disorder. Presence of anxiety disorders was assessed every six months over three years via structured clinical interviews. Among individuals who had a new episode of anxiety, we confirmed previous findings that history of anxiety, increased anxiety sensitivity (the fear of anxiety related sensations), and increased neuroticism were significant predictors. We also found trend level support for assertiveness as a predictor of anxiety onset. However, of these variables, only history of anxiety and anxiety sensitivity provided unique prediction. We did not find evidence for negative life events as a predictor of onset of anxiety either alone or in interaction with other variables in a diathesis-stress model. These findings from a prospective longitudinal study are discussed in relation to the potential role of such predictors in primary or relapse prevention efforts. PMID:19699609

Incidence of workers compensation indemnity claims across socio-demographic and job characteristics.

PubMed

Du, Juan; Leigh, J Paul

2011-10-01

We hypothesized that low socioeconomic status, employer-provided health insurance, low wages, and overtime were predictors of reporting workers compensation indemnity claims. We also tested for gender and race disparities. Responses from 17,190 (person-years) Americans participating in the Panel Study of Income Dynamics, 1997-2005, were analyzed with logistic regressions. The dependent variable indicated whether the subject collected benefits from a claim. Odds ratios for men and African-Americans were relatively large and strongly significant predictors of claims; significance for Hispanics was moderate and confounded by education. Odds ratios for variables measuring education were the largest for all statistically significant covariates. Neither low wages nor employer-provided health insurance was a consistent predictor. Due to confounding from the "not salaried" variable, overtime was not a consistently significant predictor. Few studies use nationally representative longitudinal data to consider which demographic and job characteristics predict reporting workers compensation indemnity cases. This study did and tested some common hypotheses about predictors. Copyright © 2011 Wiley-Liss, Inc.

Short-term variability and predictors of urinary pentachlorophenol levels in Ohio preschool children

EPA Science Inventory

Pentachlorophenol (PCP) is a persistent and ubiquitous environmental contaminant. No published data exist on the temporal variability or important predictors of urinary PCP concentrations in young children. In this further analysis of study data, we have examined the associations...

Incidence, predictors and outcomes of acute-on-chronic liver failure in outpatients with cirrhosis.

PubMed

Piano, Salvatore; Tonon, Marta; Vettore, Elia; Stanco, Marialuisa; Pilutti, Chiara; Romano, Antonietta; Mareso, Sara; Gambino, Carmine; Brocca, Alessandra; Sticca, Antonietta; Fasolato, Silvano; Angeli, Paolo

2017-12-01

Acute-on-chronic liver failure (ACLF) is the most life-threatening complication of cirrhosis. Prevalence and outcomes of ACLF have recently been described in hospitalized patients with cirrhosis. However, no data is currently available on the prevalence and the risk factors of ACLF in outpatients with cirrhosis. The aim of this study was to evaluate incidence, predictors and outcomes of ACLF in a large cohort of outpatients with cirrhosis. A total of 466 patients with cirrhosis consecutively evaluated in the outpatient clinic of a tertiary hospital were included and followed up until death and/or liver transplantation for a mean of 45±44months. Data on development of hepatic and extrahepatic organ failures were collected during this period. ACLF was defined and graded according to the EASL-CLIF Consortium definition. During the follow-up, 118 patients (25%) developed ACLF: 57 grade-1, 33 grade-2 and 28 grade-3. The probability of developing ACLF was 14%, 29%, and 41% at 1year, 5years, and 10years, respectively. In the multivariate analysis, baseline mean arterial pressure (hazard ratio [HR] 0.96; p=0.012), ascites (HR 2.53; p=0.019), model of end-stage liver disease score (HR 1.26; p<0.001) and baseline hemoglobin (HR 0.07; p=0.012) were found to be independent predictors of the development of ACLF at one year. As expected, ACLF was associated with a poor prognosis, with a 3-month probability of transplant-free survival of 56%. Outpatients with cirrhosis have a high risk of developing ACLF. The degree of liver failure and circulatory dysfunction are associated with the development of ACLF, as well as low values of hemoglobin. These simple variables may help to identify patients at a high risk of developing ACLF and to plan a program of close surveillance and prevention in these patients. There is a need to identify predictors of acute-on-chronic liver failure (ACLF) in patients with cirrhosis in order to identify patients at high risk of developing ACLF and to plan strategies of prevention. In this study, we identified four simple predictors of ACLF: model of end-stage liver disease (MELD) score, ascites, mean arterial pressure and hemoglobin. These variables may help to identify patients with cirrhosis, at a high risk of developing ACLF, that are candidates for new strategies of surveillance and prevention. Anemia is a potential new target for treating these patients. Copyright © 2017 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

The no-show patient in the model family practice unit.

PubMed

Dervin, J V; Stone, D L; Beck, C H

1978-12-01

Appointment breaking by patients causes problems for the physician's office. Patients who neither keep nor cancel their appointments are often referred to as "no shows." Twenty variables were identified as potential predictors of no-show behavior. These predictors were applied to 291 Family Practice Center patients during a one-month study in April 1977. A discriminant function and multiple regression procedure were utilized ascertain the predictability of the selected variables. Predictive accuracy of the variables was 67.4 percent compared to the presently utilized constant predictor technique, which is 73 percent accurate. Modification of appointment schedules based upon utilization of the variables studies as predictors of show/no-show behavior does not appear to be an effective strategy in the Family Practice Center of the Community Hospital of Sonoma County, Santa Rosa, due to the high proportion of patients who do, in fact, show. In clinics with lower show rates, the technique may prove to be an effective strategy.

A Longitudinal Study of Work After Retirement: Examining Predictors of Bridge Employment, Continued Career Employment, and Retirement.

PubMed

Bennett, Misty M; Beehr, Terry A; Lepisto, Lawrence R

2016-09-01

Older employees are increasingly accepting bridge employment, which occurs when older workers take employment for pay after they retire from their main career. This study examined predictors of workers' decisions to engage in bridge employment versus full retirement and career employment. A national sample of 482 older people in the United States was surveyed regarding various work-related and nonwork related predictors of retirement decisions, and their retirement status was measured 5 years later. In bivariate analyses, both work-related variables (career goal achievement and experienced pressure to retire) and nonwork-related variables (psychological distress and traditional gender role orientation) predicted taking bridge employment, but in multinomial logistic regression, only nonwork variables had unique effects. Few predictors differentiated the bridge employed and fully retired groups. Nonwork variables were salient in making the decision to retire, and bridge employment may be conceptually more similar to full retirement than to career employment. © The Author(s) 2016.

Predictors of Hearing-Aid Outcomes

PubMed Central

Johannesen, Peter T.; Pérez-González, Patricia; Blanco, José L.; Kalluri, Sridhar; Edwards, Brent

2017-01-01

Over 360 million people worldwide suffer from disabling hearing loss. Most of them can be treated with hearing aids. Unfortunately, performance with hearing aids and the benefit obtained from using them vary widely across users. Here, we investigate the reasons for such variability. Sixty-eight hearing-aid users or candidates were fitted bilaterally with nonlinear hearing aids using standard procedures. Treatment outcome was assessed by measuring aided speech intelligibility in a time-reversed two-talker background and self-reported improvement in hearing ability. Statistical predictive models of these outcomes were obtained using linear combinations of 19 predictors, including demographic and audiological data, indicators of cochlear mechanical dysfunction and auditory temporal processing skills, hearing-aid settings, working memory capacity, and pretreatment self-perceived hearing ability. Aided intelligibility tended to be better for younger hearing-aid users with good unaided intelligibility in quiet and with good temporal processing abilities. Intelligibility tended to improve by increasing amplification for low-intensity sounds and by using more linear amplification for high-intensity sounds. Self-reported improvement in hearing ability was hard to predict but tended to be smaller for users with better working memory capacity. Indicators of cochlear mechanical dysfunction, alone or in combination with hearing settings, did not affect outcome predictions. The results may be useful for improving hearing aids and setting patients’ expectations. PMID:28929903

Physiological and behavioral indices of emotion dysregulation as predictors of outcome from cognitive behavioral therapy and acceptance and commitment therapy for anxiety.

PubMed

Davies, Carolyn D; Niles, Andrea N; Pittig, Andre; Arch, Joanna J; Craske, Michelle G

2015-03-01

Identifying for whom and under what conditions a treatment is most effective is an essential step toward personalized medicine. The current study examined pre-treatment physiological and behavioral variables as predictors and moderators of outcome in a randomized clinical trial comparing cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) for anxiety disorders. Sixty individuals with a DSM-IV defined principal anxiety disorder completed 12 sessions of either CBT or ACT. Baseline physiological and behavioral variables were measured prior to entering treatment. Self-reported anxiety symptoms were assessed at pre-treatment, post-treatment, and 6- and 12-month follow-up from baseline. Higher pre-treatment heart rate variability was associated with worse outcome across ACT and CBT. ACT outperformed CBT for individuals with high behavioral avoidance. Subjective anxiety levels during laboratory tasks did not predict or moderate treatment outcome. Due to small sample sizes of each disorder, disorder-specific predictors were not tested. Future research should examine these predictors in larger samples and across other outcome variables. Lower heart rate variability was identified as a prognostic indicator of overall outcome, whereas high behavioral avoidance was identified as a prescriptive indicator of superior outcome from ACT versus CBT. Investigation of pre-treatment physiological and behavioral variables as predictors and moderators of outcome may help guide future treatment-matching efforts. Copyright © 2014 Elsevier Ltd. All rights reserved.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

PubMed

Heidema, A Geert; Boer, Jolanda M A; Nagelkerke, Nico; Mariman, Edwin C M; van der A, Daphne L; Feskens, Edith J M

2006-04-21

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

Miscarriage: A Special Type of Family Crisis.

ERIC Educational Resources Information Center

Day, Randal D.; Hooks, Daniel

1987-01-01

Surveyed 102 women about their experience with miscarriage. Found that family resource variables were a much stronger predictor of level of crisis and recovery than were personal or community resource variables. Adaptation and cohesion were significant predictors of speed or recovery and level of crisis, respectively. (Author/NB)

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

NASA Astrophysics Data System (ADS)

O'Carroll, Jack P. J.; Kennedy, Robert; Ren, Lei; Nash, Stephen; Hartnett, Michael; Brown, Colin

2017-10-01

The INFOMAR (Integrated Mapping For the Sustainable Development of Ireland's Marine Resource) initiative has acoustically mapped and classified a significant proportion of Ireland's Exclusive Economic Zone (EEZ), and is likely to be an important tool in Ireland's efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables. To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used a combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay was also assessed using the Infaunal Quality Index. The ANN that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for the ANN using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the temporally variable and sparsely distributed data within the study area.

Doubling down on phosphorylation as a variable peptide modification.

PubMed

Cooper, Bret

2016-09-01

Some mass spectrometrists believe that searching for variable PTMs like phosphorylation of serine or threonine when using database-search algorithms to interpret peptide tandem mass spectra will increase false-positive matching. The basis for this is the premise that the algorithm compares a spectrum to both a nonphosphorylated peptide candidate and a phosphorylated candidate, which is double the number of candidates compared to a search with no possible phosphorylation. Hence, if the search space doubles, false-positive matching could increase accordingly as the algorithm considers more candidates to which false matches could be made. In this study, it is shown that the search for variable phosphoserine and phosphothreonine modifications does not always double the search space or unduly impinge upon the FDR. A breakdown of how one popular database-search algorithm deals with variable phosphorylation is presented. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Study of Social Desirability Levels of Female Youth Camp Leader Candidates in Accordance with Some Variables

ERIC Educational Resources Information Center

Üzümcü, Bülent

2016-01-01

The aim of the study examination of the study of social desirability levels of female youth camp leader candidates in according with some variables. The study the scope of the research consists of 326 female trainees participated in the relevant course of youth camp leader candidates, depending on the Youth and Sport Ministry. As a measurement…

Investigation of the Attitudes of Physical Education Teacher Candidates toward Teaching Profession and Sense of Competence in Terms of Some Variables

ERIC Educational Resources Information Center

Murathan, Talha; Özdemir, Kübra

2017-01-01

The purpose of this study was to examine the attitudes of physical education teacher candidates toward the teaching profession and the perceptions of professional competence according to some variables. A total of 351 teacher candidates, studying in the last class of Physical Education and Sport Teaching Department in the Faculty of Sports…

Interpreting incremental value of markers added to risk prediction models.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Pencina, Karol M; Janssens, A Cecile J W; Greenland, Philip

2012-09-15

The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.

Variables that Predict Serve Efficacy in Elite Men’s Volleyball with Different Quality of Opposition Sets

PubMed Central

Valhondo, Álvaro; Fernández-Echeverría, Carmen; González-Silva, Jara; Claver, Fernando; Moreno, M. Perla

2018-01-01

Abstract The objective of this study was to determine the variables that predicted serve efficacy in elite men’s volleyball, in sets with different quality of opposition. 3292 serve actions were analysed, of which 2254 were carried out in high quality of opposition sets and 1038 actions were in low quality of opposition sets, corresponding to a total of 24 matches played during the Men’s European Volleyball Championships held in 2011. The independent variables considered in this study were the serve zone, serve type, serving player, serve direction, reception zone, receiving player and reception type; the dependent variable was serve efficacy and the situational variable was quality of opposition sets. The variables that acted as predictors in both high and low quality of opposition sets were the serving player, reception zone and reception type. The serve type variable only acted as a predictor in high quality of opposition sets, while the serve zone variable only acted as a predictor in low quality of opposition sets. These results may provide important guidance in men’s volleyball training processes. PMID:29599869

Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

NASA Technical Reports Server (NTRS)

Vybiral, T.; Glaeser, D. H.; Goldberger, A. L.; Rigney, D. R.; Hess, K. R.; Mietus, J.; Skinner, J. E.; Francis, M.; Pratt, C. M.

1993-01-01

OBJECTIVES. The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND. Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS. Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS. Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS. Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

Relationships between Speech Intelligibility and Word Articulation Scores in Children with Hearing Loss

PubMed Central

Ertmer, David J.

2012-01-01

Purpose This investigation sought to determine whether scores from a commonly used word-based articulation test are closely associated with speech intelligibility in children with hearing loss. If the scores are closely related, articulation testing results might be used to estimate intelligibility. If not, the importance of direct assessment of intelligibility would be reinforced. Methods Forty-four children with hearing losses produced words from the Goldman-Fristoe Test of Articulation-2 and sets of 10 short sentences. Correlation analyses were conducted between scores for seven word-based predictor variables and percent-intelligible scores derived from listener judgments of stimulus sentences. Results Six of seven predictor variables were significantly correlated with percent-intelligible scores. However, regression analysis revealed that no single predictor variable or multi- variable model accounted for more than 25% of the variability in intelligibility scores. Implications The findings confirm the importance of assessing connected speech intelligibility directly. PMID:20220022

Predictors of First-Year Sultan Qaboos University Students' Grade Point Average

ERIC Educational Resources Information Center

Alkhausi, Hussain Ali; Al-Yahmadi, Hamad; Al-Kalbani, Muna; Clayton, David; Al-Barwani, Thuwayba; Al-Sulaimani, Humaira; Neisler, Otherine; Khan, Mohammad Athar

2015-01-01

This study investigated predictors of first-year university grade point average (GPA) using academic and nonacademic variables. Data were collected from 1511 Omani students selected conveniently from the population of students entering Sultan Qaboos University (SQU) in Fall 2010. Variables considered in the analysis were general education diploma…

Life Expectancy of Persons with Down Syndrome.

ERIC Educational Resources Information Center

Eyman, Richard K.; And Others

1991-01-01

Longevity of 12,543 Down's syndrome clients of the California Department of Developmental Services was examined. Findings indicated that predictors of survival were not different from mortality-related variables in the general population. Lack of mobility or poor feeding skills were better predictors of early death than variables associated with…

Centering Effects in HLM Level-1 Predictor Variables.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Bembry, Karen

Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…

A Study of Predictors of College Completion among SEEK Immigrant Students

ERIC Educational Resources Information Center

Nazon, Marie C.

2010-01-01

This study examined the strength of the relationship between eight situational and demographic variables and college completion among immigrant students in SEEK, an educational opportunity program. The eight variables studied as possible predictors of college completion included household composition, length of residency, English as a primary…

Predicting the In-Hospital Responsiveness to Treatment of Alcoholics. Social Factors as Predictors of Outcome. Brain Damage as a Factor in Treatment Outcome of Chronic Alcoholic Patients.

ERIC Educational Resources Information Center

Mascia, George V.; And Others

The authors attempt to locate predictor variables associated with the outcome of alcoholic treatment programs. Muscia's study focuses on the predictive potential of: (1) response to a GSR conditioning procedure; (2) several personality variables; and (3) age and IQ measures. Nine variables, reflecting diverse perspectives, were selected as a basis…

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

PubMed

Marill, Keith A

2004-01-01

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

Parent involvement in school: English speaking versus Spanish speaking families.

PubMed

Lee, Sang Min; Thorn, Antoinette; Bloomdahl, Susana Contreras; Ha, Jung Hee; Nam, Suk Kyung; Lee, Jayoung

2012-07-01

The purpose of the present study was to explore the relationships between three predictor variables (attitude toward school, parent-child communication, and school commitment action) and the criterion variable (parent involvement) in a representative sample and to examine if these relationships were consistent across three groups (English speaking Caucasian family, English speaking Latino family, and Spanish speaking Latino families). Using a national database (N = 9.841), multi-group SEM analyses were conducted to investigate the relationship between three predictor variables and the criterion variable in three family groups. While all three predictor variables significantly predicted parent involvement in English speaking Caucasian and Latino families, only two variables (parent-child communication and school commitment actions), significantly predicted parent involvement in Spanish speaking Latino families. The results of this study suggest that when administrators, teachers and counselors in school strive to share specific school-related information with Latino families, Spanish speaking families are more likely to become involved with schools.

Quantitatively measured tremor in hand-arm vibration-exposed workers.

PubMed

Edlund, Maria; Burström, Lage; Hagberg, Mats; Lundström, Ronnie; Nilsson, Tohr; Sandén, Helena; Wastensson, Gunilla

2015-04-01

The aim of the present study was to investigate the possible increase in hand tremor in relation to hand-arm vibration (HAV) exposure in a cohort of exposed and unexposed workers. Participants were 178 male workers with or without exposure to HAV. The study is cross-sectional regarding the outcome of tremor and has a longitudinal design with respect to exposure. The dose of HAV exposure was collected via questionnaires and measurements at several follow-ups. The CATSYS Tremor Pen(®) was used for measuring postural tremor. Multiple linear regression methods were used to analyze associations between different tremor variables and HAV exposure, along with predictor variables with biological relevance. There were no statistically significant associations between the different tremor variables and cumulative HAV or current exposure. Age was a statistically significant predictor of variation in tremor outcomes for three of the four tremor variables, whereas nicotine use was a statistically significant predictor of either left or right hand or both hands for all four tremor variables. In the present study, there was no evidence of an exposure-response association between HAV exposure and measured postural tremor. Increase in age and nicotine use appeared to be the strongest predictors of tremor.

Rationale, objectives, and design of the EUTrigTreat clinical study: a prospective observational study for arrhythmia risk stratification and assessment of interrelationships among repolarization markers and genotype

PubMed Central

Seegers, Joachim; Vos, Marc A.; Flevari, Panagiota; Willems, Rik; Sohns, Christian; Vollmann, Dirk; Lüthje, Lars; Kremastinos, Dimitrios T.; Floré, Vincent; Meine, Mathias; Tuinenburg, Anton; Myles, Rachel C.; Simon, Dirk; Brockmöller, Jürgen; Friede, Tim; Hasenfuß, Gerd; Lehnart, Stephan E.; Zabel, Markus

2012-01-01

Aims The EUTrigTreat clinical study has been designed as a prospective multicentre observational study and aims to (i) risk stratify patients with an implantable cardioverter defibrillator (ICD) for mortality and shock risk using multiple novel and established risk markers, (ii) explore a link between repolarization biomarkers and genetics of ion (Ca2+, Na+, K+) metabolism, (iii) compare the results of invasive and non-invasive electrophysiological (EP) testing, (iv) assess changes of non-invasive risk stratification tests over time, and (v) associate arrythmogenomic risk through 19 candidate genes. Methods and results Patients with clinical ICD indication are eligible for the trial. Upon inclusion, patients will undergo non-invasive risk stratification, including beat-to-beat variability of repolarization (BVR), T-wave alternans, T-wave morphology variables, ambient arrhythmias from Holter, heart rate variability, and heart rate turbulence. Non-invasive or invasive programmed electrical stimulation will assess inducibility of ventricular arrhythmias, with the latter including recordings of monophasic action potentials and assessment of restitution properties. Established candidate genes are screened for variants. The primary endpoint is all-cause mortality, while one of the secondary endpoints is ICD shock risk. A mean follow-up of 3.3 years is anticipated. Non-invasive testing will be repeated annually during follow-up. It has been calculated that 700 patients are required to identify risk predictors of the primary endpoint, with a possible increase to 1000 patients based on interim risk analysis. Conclusion The EUTrigTreat clinical study aims to overcome current shortcomings in sudden cardiac death risk stratification and to answer several related research questions. The initial patient recruitment is expected to be completed in July 2012, and follow-up is expected to end in September 2014. Clinicaltrials.gov identifier: NCT01209494. PMID:22117037

Personal and organizational predictors of workplace sexual harassment of women by men.

PubMed

Dekker, I; Barling, J

1998-01-01

The authors investigated the predictors of workplace sexual harassment in 278 male university faculty and staff (M age = 45 years). Workplace variables (perceptions of organizational sanctions against harassment and perceptions of a sexualized workplace) and personal variables (adversarial sexual beliefs, sexual harassment beliefs, perspective taking, and self-esteem) were studied as predictors of sexualized and gender harassment. Social desirability was controlled. Both organizational variables and beliefs about sexual harassment predicted gender harassment and sexualized harassment. Perspective taking, adversarial sexual beliefs, and sexual harassment beliefs moderated the effects of perceived organizational sanctions against harassment on sexualized harassment. Findings are discussed as they relate to organizational efforts to reduce or prevent sexual harassment.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

a Latent Variable Path Analysis Model of Secondary Physics Enrollments in New York State.

NASA Astrophysics Data System (ADS)

Sobolewski, Stanley John

The Percentage of Enrollment in Physics (PEP) at the secondary level nationally has been approximately 20% for the past few decades. For a more scientifically literate citizenry as well as specialists to continue scientific research and development, it is desirable that more students enroll in physics. Some of the predictor variables for physics enrollment and physics achievement that have been identified previously includes a community's socioeconomic status, the availability of physics, the sex of the student, the curriculum, as well as teacher and student data. This study isolated and identified predictor variables for PEP of secondary schools in New York. Data gathered by the State Education Department for the 1990-1991 school year was used. The source of this data included surveys completed by teachers and administrators on student characteristics and school facilities. A data analysis similar to that done by Bryant (1974) was conducted to determine if the relationships between a set of predictor variables related to physics enrollment had changed in the past 20 years. Variables which were isolated included: community, facilities, teacher experience, number of type of science courses, school size and school science facilities. When these variables were isolated, latent variable path diagrams were proposed and verified by the Linear Structural Relations computer modeling program (LISREL). These diagrams differed from those developed by Bryant in that there were more manifest variables used which included achievement scores in the form of Regents exam results. Two criterion variables were used, percentage of students enrolled in physics (PEP) and percent of students enrolled passing the Regents physics exam (PPP). The first model treated school and community level variables as exogenous while the second model treated only the community level variables as exogenous. The goodness of fit indices for the models was 0.77 for the first model and 0.83 for the second model. No dramatic differences were found between the relationship of predictor variables to physics enrollment in 1972 and 1991. New models indicated that smaller school size, enrollment in previous science and math courses and other school variables were more related to high enrollment rather than achievement. Exogenous variables such as community size were related to achievement. It was shown that achievement and enrollment were related to a different set of predictor variables.

Psychosocial predictors of human papillomavirus vaccination intentions for young women 18 to 26: religiosity, morality, promiscuity, and cancer worry.

PubMed

Krakow, Melinda M; Jensen, Jakob D; Carcioppolo, Nick; Weaver, Jeremy; Liu, Miao; Guntzviller, Lisa M

2015-01-01

To determine whether five psychosocial variables, namely, religiosity, morality, perceived promiscuity, cancer worry frequency, and cancer worry severity, predict young women's intentions to receive the human papillomavirus (HPV) vaccination. Female undergraduate students (n=408) completed an online survey. Questions pertaining to hypothesized predictors were analyzed through bivariate correlations and hierarchical regression equations. Regressions examined whether the five psychosocial variables of interest predicted intentions to vaccinate above and beyond controls. Proposed interactions among predictor variables were also tested. Study findings supported cancer worry as a direct predictor of HPV vaccination intention, and religiosity and sexual experience as moderators of the relationship between concerns of promiscuity reputation and intentions to vaccinate. One dimension of cancer worry (severity) emerged as a particularly robust predictor for this population. This study provides support for several important, yet understudied, factors contributing to HPV vaccination intentions among college-aged women: cancer worry severity and religiosity. Future research should continue to assess the predictive contributions of these variables and evaluate how messages and campaigns to increase HPV vaccination uptake can utilize religious involvement and worry about cancer to promote more effectively HPV vaccination as a cancer prevention strategy. Copyright © 2015 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Predictors of functional dependency after stroke in Nigeria.

PubMed

Ojagbemi, Akin; Owolabi, Mayowa

2013-11-01

The factors impacting poststroke functional dependency have not been adequately explored in sub-Saharan Africa. This study examined the risk factors for functional dependency in a group of Nigerian African stroke survivors. One hundred twenty-eight stroke survivors attending a tertiary general hospital in southwestern Nigeria were consecutively recruited and assessed for functional dependency using the modified Rankin Scale (mRS). Stroke was diagnosed according to the World Health Organization criteria. Candidate independent variables assessed included the demographic and clinical characteristics of survivors, cognitive dysfunction, and a diagnosis of major depressive disorder. Variables with significant relationship to functional dependency were entered into a logistic regression model to identify factors that were predictive of functional dependency among the stroke survivors. In all, 60.9% of the stroke survivors were functionally dependent (mRS scores≥3), with mean±SD mRS scores of 2.71±1.01. Female sex (P=.003; odds ratio [OR] 3.08; 95% confidence interval [CI] 1.47-6.44), global cognitive dysfunction (P=.002; OR 5.04; 95% CI 1.79-14.16), and major depressive disorder (P<.0001; OR 3.06; 95% CI 1.92-4.87) were strongly associated with functional dependency in univariate analysis. Major depressive disorder was an independent predictor of functional dependency in multivariate analysis (P<.0001; OR 6.89; 95% CI 2.55-18.6; R2=0.19). Depression, female sex, and cognitive dysfunction were strongly associated with poorer functioning after stroke. Interventions aimed at depression and cognitive dysfunction after stroke may improve functional independence in stroke survivors. Copyright © 2013 National Stroke Association. Published by Elsevier Inc. All rights reserved.

A Bayesian method for assessing multiscalespecies-habitat relationships

USGS Publications Warehouse

Stuber, Erica F.; Gruber, Lutz F.; Fontaine, Joseph J.

2017-01-01

ContextScientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.ObjectivesOur objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.MethodsWe introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.ResultsOur method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.ConclusionsGiven the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

Calculation of the ALMA Risk of Right Ventricular Failure After Left Ventricular Assist Device Implantation.

PubMed

Loforte, Antonio; Montalto, Andrea; Musumeci, Francesco; Amarelli, Cristiano; Mariani, Carlo; Polizzi, Vincenzo; Lilla Della Monica, Paola; Grigioni, Francesco; Di Bartolomeo, Roberto; Marinelli, Giuseppe

2018-05-08

Right ventricular failure after continuous-flow left ventricular assist device (LVAD) implantation is still an unsolved issue and remains a life-threatening event for patients. We undertook this study to determine predictors of the patients who are candidates for isolated LVAD therapy as opposed to biventricular support (BVAD). We reviewed demographic, echocardiographic, hemodynamic, and laboratory variables for 258 patients who underwent both isolated LVAD implantation and unplanned BVAD because of early right ventricular failure after LVAD insertion, between 2006 and 2017 (LVAD = 170 and BVAD = 88). The final study patients were randomly divided into derivation (79.8%, n = 206) and validation (20.1%, n = 52) cohorts. Fifty-seven preoperative risk factors were compared between patients who were successfully managed with an LVAD and those who required a BVAD. Nineteen variables demonstrated statistical significance on univariable analysis. Multivariable logistic regression analysis identified destination therapy (odds ratio [OR] 2.0 [1.7-3.9], p = 0.003), a pulmonary artery pulsatility index <2 (OR 3.3 [1.7-6.1], p = 0.001), a right ventricle/left ventricle end-diastolic diameter ratio >0.75 (OR 2.7 [1.5-5.5], p = 0.001), an right ventricle stroke work index <300 mm Hg/ml/m (OR 4.3 [2.5-7.3], p < 0.001), and a United Network for Organ Sharing modified Model for End-Stage Liver Disease Excluding INR score >17 (OR 3.5 [1.9-6.9], p < 0.001) as the major predictors of the need for BVAD. Using these data, we propose a simple risk calculator to determine the suitability of patients for isolated LVAD support in the era of continuous-flow mechanical circulatory support devices.

Apolipoprotein CIII and N-terminal prohormone b-type natriuretic peptide as independent predictors for cardiovascular disease in type 2 diabetes.

PubMed

Colombo, Marco; Looker, Helen C; Farran, Bassam; Agakov, Felix; Brosnan, M Julia; Welsh, Paul; Sattar, Naveed; Livingstone, Shona; Durrington, Paul N; Betteridge, D John; McKeigue, Paul M; Colhoun, Helen M

2018-05-21

Developing sparse panels of biomarkers for cardiovascular disease in type 2 diabetes would enable risk stratification for clinical decision making and selection into clinical trials. We examined the individual and joint performance of five candidate biomarkers for incident cardiovascular disease (CVD) in type 2 diabetes that an earlier discovery study had yielded. Apolipoprotein CIII (apoCIII), N-terminal prohormone B-type natriuretic peptide (NT-proBNP), high sensitivity Troponin T (hsTnT), Interleukin-6, and Interleukin-15 were measured in baseline serum samples from the Collaborative Atorvastatin Diabetes trial (CARDS) of atorvastatin versus placebo. Among 2105 persons with type 2 diabetes and median age of 62.9 years (range 39.2-77.3), there were 144 incident CVD (acute coronary heart disease or stroke) cases during the maximum 5-year follow up. We used Cox Proportional Hazards models to identify biomarkers associated with incident CVD and the area under the receiver operating characteristic curves (AUROC) to assess overall model prediction. Three of the biomarkers were singly associated with incident CVD independently of other risk factors; NT-proBNP (Hazard Ratio per standardised unit 2.02, 95% Confidence Interval [CI] 1.63, 2.50), apoCIII (1.34, 95% CI 1.12, 1.60) and hsTnT (1.40, 95% CI 1.16, 1.69). When combined in a single model, only NT-proBNP and apoCIII were independent predictors of CVD, together increasing the AUROC using Framingham risk variables from 0.661 to 0.745. The biomarkers NT-proBNP and apoCIII substantially increment the prediction of CVD in type 2 diabetes beyond that obtained with the variables used in the Framingham risk score. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Primary care physicians' perceptions about and confidence in deciding which patients to refer for total joint arthroplasty of the hip and knee.

PubMed

Waugh, E J; Badley, E M; Borkhoff, C M; Croxford, R; Davis, A M; Dunn, S; Gignac, M A; Jaglal, S B; Sale, J; Hawker, G A

2016-03-01

The purpose of this study is to examine the perceptions of primary care physicians (PCPs) regarding indications, contraindications, risks and benefits of total joint arthroplasty (TJA) and their confidence in selecting patients for referral for TJA. PCPs recruited from among those providing care to participants in an established community cohort with hip or knee osteoarthritis (OA). Self-completed questionnaires were used to collect demographic and practice characteristics and perceptions about TJA. Confidence in referring appropriate patients for TJA was measured on a scale from 1 to 10; respondents scoring in the lowest tertile were considered to have 'low confidence'. Descriptive analyses were conducted and multiple logistic regression was used to determine key predictors of low confidence. 212 PCPs participated (58% response rate) (65% aged 50+ years, 45% female, 77% >15 years of practice). Perceptions about TJA were highly variable but on average, PCPs perceived that a typical surgical candidate would have moderate pain and disability, identified few absolute contraindications to TJA, and overestimated both the effectiveness and risks of TJA. On average, PCPs indicated moderate confidence in deciding who to refer. Independent predictors of low confidence were female physicians (OR = 2.18, 95% confidence interval (CI): 1.06-4.46) and reporting a 'lack of clarity about surgical indications' (OR = 3.54, 95% CI: 1.87-6.66). Variability in perceptions and lack of clarity about surgical indications underscore the need for decision support tools to inform PCP - patient decision making regarding referral for TJA. Copyright © 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Ecological and personal predictors of science achievement in an urban center

NASA Astrophysics Data System (ADS)

Guidubaldi, John Michael

This study sought to examine selected personal and environmental factors that predict urban students' achievement test scores on the science subject area of the Ohio standardized test. Variables examined were in the general categories of teacher/classroom, student, and parent/home. It assumed that these clusters might add independent variance to a best predictor model, and that discovering relative strength of different predictors might lead to better selection of intervention strategies to improve student performance. This study was conducted in an urban school district and was comprised of teachers and students enrolled in ninth grade science in three of this district's high schools. Consenting teachers (9), students (196), and parents (196) received written surveys with questions designed to examine the predictive power of each variable cluster. Regression analyses were used to determine which factors best correlate with student scores and classroom science grades. Selected factors were then compiled into a best predictive model, predicting success on standardized science tests. Students t tests of gender and racial subgroups confirmed that there were racial differences in OPT scores, and both gender and racial differences in science grades. Additional examinations were therefore conducted for all 12 variables to determine whether gender and race had an impact on the strength of individual variable predictions and on the final best predictor model. Of the 15 original OPT and cluster variable hypotheses, eight showed significant positive relationships that occurred in the expected direction. However, when more broadly based end-of-the-year science class grade was used as a criterion, 13 of the 15 hypotheses showed significant relationships in the expected direction. With both criteria, significant gender and racial differences were observed in the strength of individual predictors and in the composition of best predictor models.

Factors influencing teamwork and collaboration within a tertiary medical center

PubMed Central

Chien, Shu Feng; Wan, Thomas TH; Chen, Yu-Chih

2012-01-01

AIM: To understand how work climate and related factors influence teamwork and collaboration in a large medical center. METHODS: A survey of 3462 employees was conducted to generate responses to Sexton’s Safety Attitudes Questionnaire (SAQ) to assess perceptions of work environment via a series of five-point, Likert-scaled questions. Path analysis was performed, using teamwork (TW) and collaboration (CO) as endogenous variables. The exogenous variables are effective communication (EC), safety culture (SC), job satisfaction (JS), work pressure (PR), and work climate (WC). The measurement instruments for the variables or summated subscales are presented. Reliability of each sub-scale are calculated. Alpha Cronbach coefficients are relatively strong: TW (0.81), CO (0.76), EC (0.70), SC (0.83), JS (0.91), WP (0.85), and WC (0.78). Confirmatory factor analysis was performed for each of these constructs. RESULTS: Path analysis enables to identify statistically significant predictors of two endogenous variables, teamwork and intra-organizational collaboration. Significant amounts of variance in perceived teamwork (R2 = 0.59) and in collaboration (R2 = 0.75) are accounted for by the predictor variables. In the initial model, safety culture is the most important predictor of perceived teamwork, with a β weight of 0.51, and work climate is the most significant predictor of collaboration, with a β weight of 0.84. After eliminating statistically insignificant causal paths and allowing correlated predictors1, the revised model shows that work climate is the only predictor positively influencing both teamwork (β = 0.26) and collaboration (β = 0.88). A relatively weak positive (β = 0.14) but statistically significant relationship exists between teamwork and collaboration when the effects of other predictors are simultaneously controlled. CONCLUSION: Hospital executives who are interested in improving collaboration should assess the work climate to ensure that employees are operating in a setting conducive to intra-organizational collaboration. PMID:25237612

Job Satisfaction in Mexican Faculty: An Analysis of its Predictor Variables. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Galaz-Fontes, Jesus Francisco; Gil-Anton, Manuel

This study examined overall job satisfaction among college faculty in Mexico. The study used data from a 1992-93 Carnegie International Faculty Survey. Secondary multiple regression analysis identified predictor variables for several faculty subgroups. Results were interpreted by differentiating between work-related and intrinsic factors, as well…

Teacher and Child Predictors of Achieving IEP Goals of Children with Autism

ERIC Educational Resources Information Center

Ruble, Lisa; McGrew, John H.

2013-01-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in "The…

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental

ERIC Educational Resources Information Center

Shieh, Gwowen

2010-01-01

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term.…

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

PubMed

Schrag, Tobias A; Westhues, Matthias; Schipprack, Wolfgang; Seifert, Felix; Thiemann, Alexander; Scholten, Stefan; Melchinger, Albrecht E

2018-04-01

The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates. Copyright © 2018 by the Genetics Society of America.

Motivation for change as a predictor of treatment response for dysthymia.

PubMed

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

Predictors of short-term outcome to exercise and manual therapy for people with hip osteoarthritis.

PubMed

French, Helen P; Galvin, Rose; Cusack, Tara; McCarthy, Geraldine M

2014-01-01

Physical therapy for hip osteoarthritis (OA) has shown short-term effects but limited long-term benefit. There has been limited research, with inconsistent results, in identifying prognostic factors associated with a positive response to physical therapy. The purpose of this study was to identify potential predictors of response to physical therapy (exercise therapy [ET] with or without adjunctive manual therapy [MT]) for hip OA based on baseline patient-specific and clinical characteristics. A prognostic study was conducted. Secondary analysis of data from a multicenter randomized controlled trial (RCT) (N=131) that evaluated the effectiveness of ET and ET+MT for hip OA was undertaken. Treatment response was defined using OMERACT/OARSI responder criteria. Ten baseline measures were used as predictor variables. Regression analyses were undertaken to identify predictors of outcome. Discriminative ability (sensitivity, specificity, and likelihood ratios) of significant variables was calculated. The RCT results showed no significant difference in most outcomes between ET and ET+MT at 9 and 18 weeks posttreatment. Forty-six patients were classified as responders at 9 weeks, and 36 patients were classified as responders at 18 weeks. Four baseline variables were predictive of a positive outcome at 9 weeks: male sex, pain with activity (<6/10), Western Ontario and McMaster Universities Osteoarthritis Index physical function subscale score (<34/68), and psychological health (Hospital Anxiety and Depression Scale score <9/42). No predictor variables were identified at the 18-week follow-up. Prognostic accuracy was fair for all 4 variables (sensitivity=0.5-0.58, specificity=0.57-0.72, likelihood ratios=1.25-1.77), indicating fair discriminative ability at predicting treatment response. The short-term follow-up limits the interpretation of results, and the low number of identified responders may have resulted in possible overfitting of the predictor model. The authors were unable to identify baseline variables in patients with hip OA that indicate those most likely to respond to treatment due to low discriminative ability. Further validation studies are needed to definitively define the best predictors of response to physical therapy in people with hip OA.

Preadmission Predictors of On-time Graduation in a Doctor of Pharmacy Program.

PubMed

Allen, Rondall E; Diaz, Carroll; Gant, Kisha; Taylor, Ashley; Onor, Ifeanyi

2016-04-25

Objective. To determine which preadmission variables or combination of variables are able to predict on-time graduation in a doctor of pharmacy program. Methods. Transcripts and student files were reviewed for 460 students who entered the college between 2007 and 2009. Results. The preadmission variables with significant correlations to on-time graduation included having a prior degree, student type, the number of unsatisfactory grades (nonscience and math-science courses, and the combination), prepharmacy cumulative grade point average (GPA), and math-science GPA. Of these variables, the significant predictors of on-time graduation were prior degree, the presence of no unsatisfactory grades in nonscience courses, and prepharmacy cumulative GPA. Conclusion. Having a prior degree, lack of unsatisfactory grades in nonscience courses, and prepharmacy GPA were identified as significant predictors of on-time graduation.

Preadmission Predictors of On-time Graduation in a Doctor of Pharmacy Program

PubMed Central

Diaz, Carroll; Gant, Kisha; Taylor, Ashley; Onor, Ifeanyi

2016-01-01

Objective. To determine which preadmission variables or combination of variables are able to predict on-time graduation in a doctor of pharmacy program. Methods. Transcripts and student files were reviewed for 460 students who entered the college between 2007 and 2009. Results. The preadmission variables with significant correlations to on-time graduation included having a prior degree, student type, the number of unsatisfactory grades (nonscience and math-science courses, and the combination), prepharmacy cumulative grade point average (GPA), and math-science GPA. Of these variables, the significant predictors of on-time graduation were prior degree, the presence of no unsatisfactory grades in nonscience courses, and prepharmacy cumulative GPA. Conclusion. Having a prior degree, lack of unsatisfactory grades in nonscience courses, and prepharmacy GPA were identified as significant predictors of on-time graduation. PMID:27170814

Best candidates for cognitive treatment of illness perceptions in chronic low back pain: results of a theory-driven predictor study.

PubMed

Siemonsma, Petra C; Stuvie, Ilse; Roorda, Leo D; Vollebregt, Joke A; Lankhorst, Gustaaf J; Lettinga, Ant T

2011-04-01

The aim of this study was to identify treatment-specific predictors of the effectiveness of a method of evidence-based treatment: cognitive treatment of illness perceptions. This study focuses on what treatment works for whom, whereas most prognostic studies focusing on chronic non-specific low back pain rehabilitation aim to reduce the heterogeneity of the population of patients who are suitable for rehabilitation treatment in general. Three treatment-specific predictors were studied in patients with chronic non-specific low back pain receiving cognitive treatment of illness perceptions: a rational approach to problem-solving, discussion skills and verbal skills. Hierarchical linear regression analysis was used to assess their predictive value. Short-term changes in physical activity, measured with the Patient-Specific Functioning List, were the outcome measure for cognitive treatment of illness perceptions effect. A total of 156 patients with chronic non-specific low back pain participated in the study. Rational problem-solving was found to be a significant predictor for the change in physical activity. Discussion skills and verbal skills were non-significant. Rational problem-solving explained 3.9% of the total variance. The rational problem-solving scale results are encouraging, because chronic non-specific low back pain problems are complex by nature and can be influenced by a variety of factors. A minimum score of 44 points on the rational problem-solving scale may assist clinicians in selecting the most appropriate candidates for cognitive treatment of illness perceptions.

Difference Imaging of Lensed Quasar Candidates in the Sloan Digital Sky Survey Supernova Survey Region

NASA Astrophysics Data System (ADS)

Lacki, Brian C.; Kochanek, Christopher S.; Stanek, Krzysztof Z.; Inada, Naohisa; Oguri, Masamune

2009-06-01

Difference imaging provides a new way to discover gravitationally lensed quasars because few nonlensed sources will show spatially extended, time variable flux. We test the method on the fields of lens candidates in the Sloan Digital Sky Survey (SDSS) Supernova Survey region from the SDSS Quasar Lens Search (SQLS) and one serendipitously discovered lensed quasar. Starting from 20,536 sources, including 49 SDSS quasars, 32 candidate lenses/lensed images, and one known lensed quasar, we find that 174 sources including 35 SDSS quasars, 16 candidate lenses/lensed images, and the known lensed quasar are nonperiodic variable sources. We can measure the spatial structure of the variable flux for 119 of these variable sources and identify only eight as candidate extended variables, including the known lensed quasar. Only the known lensed quasar appears as a close pair of sources on the difference images. Inspection of the remaining seven suggests they are false positives, and only two were spectroscopically identified quasars. One of the lens candidates from the SQLS survives our cuts, but only as a single image instead of a pair. This indicates a false positive rate of order ~1/4000 for the method, or given our effective survey area of order 0.82 deg2, ~5 per deg2 in the SDSS Supernova Survey. The fraction of quasars not found to be variable and the false positive rate would both fall if we had analyzed the full, later data releases for the SDSS fields. While application of the method to the SDSS is limited by the resolution, depth, and sampling of the survey, several future surveys such as Pan-STARRS, LSST, and SNAP will significantly improve on these limitations.

A variant of sparse partial least squares for variable selection and data exploration.

PubMed

Olson Hunt, Megan J; Weissfeld, Lisa; Boudreau, Robert M; Aizenstein, Howard; Newman, Anne B; Simonsick, Eleanor M; Van Domelen, Dane R; Thomas, Fridtjof; Yaffe, Kristine; Rosano, Caterina

2014-01-01

When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed "all-possible" SPLS is proposed, which fits a SPLS model for all tuning parameter values across a set grid. Noted is the percentage of time a given predictor is chosen, as well as the average non-zero parameter estimate. Using a "large" number of multicollinear predictors, simulation confirmed variables not associated with the outcome were least likely to be chosen as sparsity increased across the grid of tuning parameters, while the opposite was true for those strongly associated. Lastly, variables with a weak association were chosen more often than those with no association, but less often than those with a strong relationship to the outcome. Similarly, predictors most strongly related to the outcome had the largest average parameter estimate magnitude, followed by those with a weak relationship, followed by those with no relationship. Across two independent studies regarding the relationship between volumetric MRI measures and a cognitive test score, this method confirmed a priori hypotheses about which brain regions would be selected most often and have the largest average parameter estimates. In conclusion, the percentage of time a predictor is chosen is a useful measure for ordering the strength of the relationship between the independent and dependent variables, serving as a form of inference. The average parameter estimates give further insight regarding the direction and strength of association. As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of potential predictors.

High performance computation of landscape genomic models including local indicators of spatial association.

PubMed

Stucki, S; Orozco-terWengel, P; Forester, B R; Duruz, S; Colli, L; Masembe, C; Negrini, R; Landguth, E; Jones, M R; Bruford, M W; Taberlet, P; Joost, S

2017-09-01

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics - that is the combination of landscape ecology with population genomics - include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental data sets. samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype-environment associations. In addition, samβada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with samβada, bayenv, lfmm and an F ST outlier method (FDIST approach in arlequin) and compare their results. samβada - an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada - outperforms other approaches and better suits whole-genome sequence data processing. © 2016 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Latitude of residence and position in time zone are predictors of cancer incidence, cancer mortality, and life expectancy at birth.

PubMed

Borisenkov, Mikhail F

2011-03-01

According to the hypothesis of circadian disruption, external factors that disturb the function of the circadian system can raise the risk of malignant neoplasm and reduce life span. Recent work has shown that the functionality of the circadian system is dependent not only on latitude of residence but also on the region's position in the time zone. The purpose of the present research was to examine the influence of latitude and time zone on cancer incidence, cancer mortality, and life expectancy at birth. A stepwise multiple regression analysis was carried out on residents of 59 regions of the European part of the Russian Federation (EPRF) using age-standardized parameters (per 100,000) of cancer incidence (CI), cancer mortality (CM), and life expectancy at birth (LE, yrs) as dependent variables. The geographical coordinates (latitude and position in the time zone) of the regions were used as independent variables, controlling for the level of economic development in the regions. The same analysis was carried out for LE in 31 regions in China. Latitude was the strongest predictor of LE in the EPRF population; it explained 48% and 45% of the variability in LE of women and men, respectively. Position within the time zone accounted for an additional 4% and 3% variability of LE in women and men, respectively. The highest values for LE were observed in the southeast of the EPRF. In China, latitude was not a predictor of LE, whereas position in the time zone explained 15% and 18% of the LE variability in women and men, respectively. The highest values of LE were observed in the eastern regions of China. Both latitude and position within the time zone were predictors for CI and CM of the EPRF population. Latitude was the best predictor of stomach CI and CM; this predictor explained 46% and 50% of the variability, respectively. Position within the time zone was the best predictor of female breast CM; it explained 15% of the variability. In most cases, CI and CM increased with increasing latitude of residence, from the eastern to the western border of the time zone, and with increasing level of economic development within the region. The dependence of CI, CM, and LE on the geographical coordinates of residence is in agreement with the hypothesis of circadian disruption.

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

Discovering Cepheid and RR Lyrae Stars: Pan-STARRS Science Archive @ STScI and Robotically Controlled Telescopes

NASA Astrophysics Data System (ADS)

Johnson, Elizabeth; Strolger, Louis-Gregory; Engle, Scott G.; Anderson, Richard I.; Rest, Armin; Calamida, Annalisa; Dosovitz Fox, Ori; Laney, David

2017-01-01

Cepheid and RR Lyrae stars are an integral part of the cosmic distance ladder and are also useful for studying galactic structure and stellar ages. This project aims to greatly expand the number of known periodic variables in our galaxy by identifying candidates in the PanSTARRS-1 3pi catalog, and carrying out systematically targeted characterization with robotically controlled telescopes. Candidate targets are selected from available detection tables based on color and variability indices and are then fully vetted using robotic telescopes: the RCT 1.3 meter (Kitt Peak National Observatory) and RATIR 1.5 meter (Mexico). Here we present work to develop a full, semi-automated prescription for candidate selection, targeted follow-up photometry, cataloging, and classification, which allows the review of approximately 25 variable candidates every two weeks. We make comparisons of our sample selection and purity from a similar study based on Pan-STARRS data (Hernitschek et al. 2016), as well as candidates identified in Gaia DR1. The goal, through continued observation and analysis, is to identify at least 10,000 new variables, hundreds of which will be new Cepheid and RR Lyrae stars.

Modeling the human development index and the percentage of poor people using quantile smoothing splines

NASA Astrophysics Data System (ADS)

Mulyani, Sri; Andriyana, Yudhie; Sudartianto

2017-03-01

Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The parameter estimation in mean regression (with Ordinary Least Square or OLS) generates a problem if we apply it to the data with a symmetric, fat-tailed, or containing outlier. Hence, an alternative method is necessary to be used to that kind of data, for example quantile regression method. The quantile regression is a robust technique to the outlier. This model can explain the relationship between the response variable and the predictor variable, not only on the central tendency of the data (median) but also on various quantile, in order to obtain complete information about that relationship. In this study, a quantile regression is developed with a nonparametric approach such as smoothing spline. Nonparametric approach is used if the prespecification model is difficult to determine, the relation between two variables follow the unknown function. We will apply that proposed method to poverty data. Here, we want to estimate the Percentage of Poor People as the response variable involving the Human Development Index (HDI) as the predictor variable.

Exploring indicators of use of costly health services in community-dwelling adults with multiple chronic conditions.

PubMed

Holland, Diane E; Vanderboom, Catherine E; Lohse, Christine M; Mandrekar, Jay; Targonski, Paul V; Madigan, Elizabeth; Powell, Suzanne K

2015-01-01

Although experts recognize that including patient functional and social variables would improve models predicting risk of using costly health services, these self-reported variables are not widely used. Explore differences in predisposing characteristics, enabling resources, patient-perceived need for care and professionally evaluated need for care variables between patients receiving primary care within a Health Care Home who did and did not use hospital, emergency department, or skilled nursing facility services in a 3-month period of time. Primary care. Guided by the Behavioral Model of Health Service Use, a secondary analysis was conducted on data from a study that included 57 community-dwelling older adults receiving primary care in a Health Care Home. Because of the exploratory nature of the study, group differences in the use of costly care services were compared at the 0.10 level of statistical significance. Seventeen patients (29.8%) experienced costly care services. The greatest number of differences in variables between groups was in the category of patient-perceived need for care (functional impairments, dependencies, difficulties). Targeting case management services using evidence-based decision support tools such as prediction models enhances the opportunity to maximize outcomes and minimize waste of resources. Patient-perceived and clinician-evaluated need for care may need to be combined to fully describe the contextual needs that drive the use of health services. Difficulty with Activities with Daily Living and Instrumental Activities of Daily Living should be considered in future studies as candidate predictor variables for need for case management services in primary care settings.

Is it all worth it? The experiences of new PhDs on the job market, 2007-10.

PubMed

McFall, Brooke Helppie; Murray-Close, Marta; Willis, Robert J; Chen, Uniko

This paper describes the job market experiences of new PhD economists, 2007-10. Using information from PhD programs' job candidate websites and original surveys, the authors present information about job candidates' characteristics, preferences and expectations; how job candidates fared at each stage of the market; and predictors of outcomes at each stage. Some information presented in this paper updates findings of prior studies. However, design features of the data used in this paper may result in more generalizable findings. This paper is unique in comparing pre-market expectations and preferences with post-market outcomes on the new PhD job market. It shows that outcomes tend to align with pre-market preferences, and candidates' expectations are somewhat predictive of their outcomes. Several analyses also shed light on sub-group differences.

Can You Hack It? Validating Predictors for IT Boot Camps

NASA Astrophysics Data System (ADS)

Gear, Courtney C.

Given the large number of information technology jobs open and lack of qualified individuals to fill them, coding boot camps have sprung up in response to this skill gap by offering a specialized training program in an accelerated format. This fast growth has created a need to measure these training programs and understand their effectiveness. In the present study, a series of analyses examined whether specific or combinations of predictors were valid for training performance in this coding academy. Self-rated, daily efficacy scores were used as outcome variables of training success and correlation results showed a positive relationship with efficacy scores and the logic test score as a predictor. Exploratory analyses indicated a Dunning-Kruger effect where students with lower education levels experience higher overall mood during the training program. Limitations of the study included small sample size, severe range restriction in predictor scores, lack of variance in predictor scores, and low variability in training program success. These limitations made identifying jumps between training stages difficult to identify. By identifying which predictors matter most for each stage of skill acquisition, further research should consider more objective variables such as instructor scores which can serve as a guideline to better asses what stage learners join at and how to design curriculum and assignments accordingly (Honken, 2013).

Difficulties with Regression Analysis of Age-Adjusted Rates.

DTIC Science & Technology

1982-09-01

variables used in those analyses, such as death rates in various states, have been age adjusted, whereas the predictor variables have not been age adjusted...The use of crude state death rates as the outcome variable with crude covariates and age as predictors can avoid the problem, at least under some...should be regressed on age-adjusted exposure Z+B+ Although age-specific death rates , Yas+’ may be available, it is often difficult to obtain age

Student-Related Variables as Predictors of Academic Achievement among Some Undergraduate Psychology Students in Barbados

ERIC Educational Resources Information Center

Fayombo, Grace Adebisi

2011-01-01

This study examined some student-related variables (interest in higher education, psychological resilience and study habit) as predictors of academic achievement among 131 (M (mean) = 28.17, SD (standard deviation) = 1.61) first year psychology students in the Introduction to Developmental Psychology class in UWI (The University of the West…

Whistle-Blowing and the Code of Silence in Police Agencies: Policy and Structural Predictors

ERIC Educational Resources Information Center

Rothwell, Gary R.; Baldwin, J. Norman

2007-01-01

This article reports the findings from a study that investigates predictors of police willingness to blow the whistle and police frequency of blowing the whistle on seven forms of misconduct. It specifically investigates the capacity of nine policy and structural variables to predict whistle-blowing. The results indicate that two variables, a…

Item Structural Properties as Predictors of Item Difficulty and Item Association.

ERIC Educational Resources Information Center

Solano-Flores, Guillermo

1993-01-01

Studied the ability of logical test design (LTD) to predict student performance in reading Roman numerals for 211 sixth graders in Mexico City tested on Roman numeral items varying on LTD-related and non-LTD-related variables. The LTD-related variable item iterativity was found to be the best predictor of item difficulty. (SLD)

A Study of the Relationship between Parenting Stress and Spirituality among Mothers of Elementary Children in Selected Korean Churches

ERIC Educational Resources Information Center

Choi, Seong Ji

2012-01-01

Problem: The problem of this study was to determine the relationship between parenting stress and six specified predictor variables of spirituality among mothers of elementary children attending selected Korean Baptist churches located in the Dallas/Ft. Worth area. The specified predictor variables of spirituality were awareness, instability,…

Individualism-Collectivism, Social-Network Orientation, and Acculturation as Predictors of Attitudes toward Seeking Professional Psychological Help among Chinese Americans.

ERIC Educational Resources Information Center

Tata, Shiraz Piroshaw; Leong, Frederick T. L.

1994-01-01

Used several culturally based variables (individualism-collectivism, social support attitudes, acculturation) and gender to predict patterns of help-seeking attitudes among Chinese American college students (n=219). Each of the independent variables was found to be a significant predictor of attitudes toward seeking professional psychological…

Predicting Preservice Music Teachers' Performance Success in Instrumental Courses Using Self-Regulated Study Strategies and Predictor Variables

ERIC Educational Resources Information Center

Ersozlu, Zehra N.; Nietfeld, John L.; Huseynova, Lale

2017-01-01

The purpose of this study was to examine the extent to which self-regulated study strategies and predictor variables predict performance success in instrumental performance college courses. Preservice music teachers (N = 123) from a music education department in two state universities in Turkey completed the Music Self-Regulated Studying…

Predicting Middle School Students' Use of Web 2.0 Technologies out of School Using Home and School Technological Variables

ERIC Educational Resources Information Center

Hughes, Joan E.; Read, Michelle F.; Jones, Sara; Mahometa, Michael

2015-01-01

This study used multiple regression to identify predictors of middle school students' Web 2.0 activities out of school, a construct composed of 15 technology activities. Three middle schools participated, where sixth- and seventh-grade students completed a questionnaire. Independent predictor variables included three demographic and five computer…

Psychosocial Variables as Predictors of School Adjustment of Gifted Students with Learning Disabilities in Nigeria

ERIC Educational Resources Information Center

Fakolade, O. A.; Oyedokun, S. O.

2015-01-01

The paper considered several psychosocial variables as predictors of school adjustment of 40 gifted students with learning disabilities in Junior Secondary School in Ikenne Local Government Council Area of Ogun State, Nigeria. Purposeful random sampling was employed to select four schools from 13 junior secondary schools in the area, six…

Resolution of Unwanted Pregnancy during Adolescence through Abortion versus Childbirth: Individual and Family Predictors and Psychological Consequences

ERIC Educational Resources Information Center

Coleman, Priscilla K.

2006-01-01

Using data from the National Longitudinal Study of Adolescent Health, various demographic, psychological, educational, and family variables were explored as predictors of pregnancy resolution. Only 2 of the 17 variables examined were significantly associated with pregnancy resolution (risk-taking and the desire to leave home). After controlling…

Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments

NASA Astrophysics Data System (ADS)

Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.

Teacher and child predictors of achieving IEP goals of children with autism.

PubMed

Ruble, Lisa; McGrew, John H

2013-12-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in The collaborative model for promoting competence and success for students with ASD. Springer, New York, 2012a) were analyzed. Predictors evaluated against child IEP goal attainment included child, teacher, intervention practice, and implementation practice variables based on an implementation science framework (Dunst and Trivette in J Soc Sci 8:143-148, 2012). Findings revealed one child (engagement), one teacher (exhaustion), two intervention quality (IEP quality for targeted and not targeted elements), and no implementation quality variables accounted for variance in child outcomes when analyzed separately. When the four significant variables were compared against each other in a single regression analysis, IEP quality accounted for one quarter of the variance in child outcomes.

Teacher and Child Predictors of Achieving IEP Goals of Children with Autism

PubMed Central

Ruble, Lisa; McGrew, John H.

2013-01-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in The collaborative model for promoting competence and success for students with ASD. Springer, New York, 2012a) were analyzed. Predictors evaluated against child IEP goal attainment included child, teacher, intervention practice, and implementation practice variables based on an implementation science framework (Dunst and Trivette in J Soc Sci 8:143–148, 2012). Findings revealed one child (engagement), one teacher (exhaustion), two intervention quality (IEP quality for targeted and not targeted elements), and no implementation quality variables accounted for variance in child outcomes when analyzed separately. When the four significant variables were compared against each other in a single regression analysis, IEP quality accounted for one quarter of the variance in child outcomes. PMID:23838728

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

PubMed Central

2013-01-01

Background Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. Results The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Conclusions Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh. PMID:23297699

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

PubMed

Slinker, B K; Glantz, S A

1985-07-01

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

A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers.

PubMed

Suarthana, Eva; Vergouwe, Yvonne; Moons, Karel G; de Monchy, Jan; Grobbee, Diederick; Heederik, Dick; Meijer, Evert

2010-09-01

To develop and validate a prediction model to detect sensitization to wheat allergens in bakery workers. The prediction model was developed in 867 Dutch bakery workers (development set, prevalence of sensitization 13%) and included questionnaire items (candidate predictors). First, principal component analysis was used to reduce the number of candidate predictors. Then, multivariable logistic regression analysis was used to develop the model. Internal validation and extent of optimism was assessed with bootstrapping. External validation was studied in 390 independent Dutch bakery workers (validation set, prevalence of sensitization 20%). The prediction model contained the predictors nasoconjunctival symptoms, asthma symptoms, shortness of breath and wheeze, work-related upper and lower respiratory symptoms, and traditional bakery. The model showed good discrimination with an area under the receiver operating characteristic (ROC) curve area of 0.76 (and 0.75 after internal validation). Application of the model in the validation set gave a reasonable discrimination (ROC area=0.69) and good calibration after a small adjustment of the model intercept. A simple model with questionnaire items only can be used to stratify bakers according to their risk of sensitization to wheat allergens. Its use may increase the cost-effectiveness of (subsequent) medical surveillance.

Mid-Term Results of Surgical Treatment of Atrial Fibrillation in Valvular Heart Disease Assesed by Speckle Tracking Echocardiography

PubMed Central

Lorenzo, Natalia; Mendez, Irene; Taibo, Mikel; Martinis, Gianfranco; Badia, Sara; Reyes, Guillermo; Aguilar, Rio

2018-01-01

Background Atrial fibrillation frequently affects patients with valvular heart disease. Ablation of atrial fibrillation during valvular surgery is an alternative for restoring sinus rhythm. Objectives This study aimed to evaluate mid-term results of successful atrial fibrillation surgical ablation during valvular heart disease surgery, to explore left atrium post-ablation mechanics and to identify predictors of recurrence. Methods Fifty-three consecutive candidates were included. Eligibility criteria for ablation included persistent atrial fibrillation <10 years and left atrium diameter < 6.0 cm. Three months after surgery, echocardiogram, 24-hour Holter monitoring and electrocardiograms were performed in all candidates who maintained sinus rhythm (44 patients). Echo-study included left atrial deformation parameters (strain and strain rate), using 2-dimensional speckle-tracking echocardiography. Simultaneously, 30 healthy individuals (controls) were analyzed with the same protocol for left atrial performance. Significance was considered with a P value of < 0.05. Results After a mean follow up of 17 ± 2 months, 13 new post-operative cases of recurrent atrial fibrillation were identified. A total of 1,245 left atrial segments were analysed. Left atrium was severely dilated in the post-surgery group and, mechanical properties of left atrium did not recover after surgery when compared with normal values. Left atrial volume (≥ 64 mL/m2) was the only independent predictor of atrial fibrillation recurrence (p = 0.03). Conclusions Left atrial volume was larger in patients with atrial fibrillation recurrence and emerges as the main predictor of recurrences, thereby improving the selection of candidates for this therapy; however, no differences were found regarding myocardial deformation parameters. Despite electrical maintenance of sinus rhythm, left atrium mechanics did not recover after atrial fibrillation ablation performed during valvular heart disease surgery. PMID:29561964

Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

PubMed

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

Prognostic value of echocardiographic indices of left atrial morphology and function in dogs with myxomatous mitral valve disease

PubMed Central

Romito, Giovanni; Guglielmini, Carlo; Diana, Alessia; Pelle, Nazzareno G.; Contiero, Barbara; Cipone, Mario

2018-01-01

Background The prognostic relevance of left atrial (LA) morphological and functional variables, including those derived from speckle tracking echocardiography (STE), has been little investigated in veterinary medicine. Objectives To assess the prognostic value of several echocardiographic variables, with a focus on LA morphological and functional variables in dogs with myxomatous mitral valve disease (MMVD). Animals One‐hundred and fifteen dogs of different breeds with MMVD. Methods Prospective cohort study. Conventional morphologic and echo‐Doppler variables, LA areas and volumes, and STE‐based LA strain analysis were performed in all dogs. A survival analysis was performed to test for the best echocardiographic predictors of cardiac‐related death. Results Most of the tested variables, including all LA STE‐derived variables were univariate predictors of cardiac death in Cox proportional hazard analysis. Because of strong correlation between many variables, only left atrium to aorta ratio (LA/Ao > 1.7), mitral valve E wave velocity (MV E vel > 1.3 m/s), LA maximal volume (LAVmax > 3.53 mL/kg), peak atrial longitudinal strain (PALS < 30%), and contraction strain index (CSI per 1% increase) were entered in the univariate analysis, and all were predictors of cardiac death. However, only the MV E vel (hazard ratio [HR], 4.45; confidence interval [CI], 1.76‐11.24; P < .001) and LAVmax (HR, 2.32; CI, 1.10‐4.89; P = .024) remained statistically significant in the multivariable analysis. Conclusions and Clinical Importance The assessment of LA dimension and function provides useful prognostic information in dogs with MMVD. Considering all the LA variables, LAVmax appears the strongest predictor of cardiac death, being superior to LA/Ao and STE‐derived variables. PMID:29572938

Multivariate outcome prediction in traumatic brain injury with focus on laboratory values.

PubMed

Nelson, David W; Rudehill, Anders; MacCallum, Robert M; Holst, Anders; Wanecek, Michael; Weitzberg, Eddie; Bellander, Bo-Michael

2012-11-20

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R². Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R².

Kindergarten predictors of second versus eighth grade reading comprehension impairments.

PubMed

Adlof, Suzanne M; Catts, Hugh W; Lee, Jaehoon

2010-01-01

Multiple studies have shown that kindergarten measures of phonological awareness and alphabet knowledge are good predictors of reading achievement in the primary grades. However, less attention has been given to the early predictors of later reading achievement. This study used a modified best-subsets variable-selection technique to examine kindergarten predictors of early versus later reading comprehension impairments. Participants included 433 children involved in a longitudinal study of language and reading development. The kindergarten test battery assessed various language skills in addition to phonological awareness, alphabet knowledge, naming speed, and nonverbal cognitive ability. Reading comprehension was assessed in second and eighth grades. Results indicated that different combinations of variables were required to optimally predict second versus eighth grade reading impairments. Although some variables effectively predicted reading impairments in both grades, their relative contributions shifted over time. These results are discussed in light of the changing nature of reading comprehension over time. Further research will help to improve the early identification of later reading disabilities.

Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.

Can we predict urinary stress incontinence by using demographic, clinical, imaging and urodynamic data?

PubMed

Wlaźlak, Edyta; Surkont, Grzegorz; Shek, Ka L; Dietz, Hans P

2015-10-01

It has been claimed that urethral hypermobility and resting urethral pressure can largely explain stress incontinence in women. In this study we tried to replicate these findings in an unselected cohort of women seen for urodynamic testing, including as many potential confounders as possible. This study is a retrospective analysis of data obtained from 341 women. They attended for urodynamic testing due to symptoms of pelvic floor dysfunction. We excluded from the analysis women with a history of previous anti-incontinence and prolapse surgery. All patients had a standardised clinical assessment, 4D transperineal pelvic floor ultrasound and multichannel urodynamic testing. Urodynamic stress incontinence (USI) was diagnosed by multichannel urodynamic testing. Its severity was subjectively graded as mild, moderate and severe. Candidate variables were: age, BMI, symptoms of prolapse, vaginal parity, significant prolapse (compartment-specific), levator avulsion, levator hiatal area, Oxford grading, midurethral mobility, maximum urethral pressure (MUP), maximum cough pressure and maximum Valsalva pressure reached. On binary logistic regression, the following parameters were statistically significant in predicting urodynamic stress incontinence: age (P=0.03), significant rectocele (P=0.02), max. abdominal pressure reached (negatively, P<0.0001), midurethral mobility (P=0.0004) and MUP (negatively, P<0.0001). On multivariate analysis, accounting for multiple interdependencies, the following predictors remained significant: max. abdominal pressure reached (negatively, P<0.0001), cough pressure (P=0.006), midurethral mobility (P=0.003) and MUP (negatively, P<0.0001), giving an R(2) of 0.24. Mid-urethral mobility and MUP are the main predictors of USI. Demographic and clinical data are at best weak predictors. Our results suggest the presence of major unrecognised confounders. Copyright © 2015. Published by Elsevier Ireland Ltd.

First-Grade Cognitive Abilities as Long-Term Predictors of Reading Comprehension and Disability Status

PubMed Central

Fuchs, Douglas; Compton, Donald L.; Fuchs, Lynn S.; Bryant, V. Joan; Hamlett, Carol L.; Lambert, Warren

2012-01-01

In a sample of 195 first graders selected for poor reading performance, the authors explored four cognitive predictors of later reading comprehension and reading disability (RD) status. In fall of first grade, the authors measured the children’s phonological processing, rapid automatized naming (RAN), oral language comprehension, and nonverbal reasoning. Throughout first grade, they also modeled the students’ reading progress by means of weekly Word Identification Fluency (WIF) tests to derive December and May intercepts. The authors assessed their reading comprehension in the spring of Grades 1–5. With the four cognitive variables and the WIF December intercept as predictors, 50.3% of the variance in fifth-grade reading comprehension was explained: 52.1% of this 50.3% was unique to the cognitive variables, 13.1% to the WIF December intercept, and 34.8% was shared. All five predictors were statistically significant. The same four cognitive variables with the May (rather than December) WIF intercept produced a model that explained 62.1% of the variance. Of this amount, the cognitive variables and May WIF intercept accounted for 34.5% and 27.7%, respectively; they shared 37.8%. All predictors in this model were statistically significant except RAN. Logistic regression analyses indicated that the accuracy with which the cognitive variables predicted end-of-fifth-grade RD status was 73.9%. The May WIF intercept contributed reliably to this prediction; the December WIF intercept did not. Results are discussed in terms of a role for cognitive abilities in identifying, classifying, and instructing students with severe reading problems. PMID:22539057

First-grade cognitive abilities as long-term predictors of reading comprehension and disability status.

PubMed

Fuchs, Douglas; Compton, Donald L; Fuchs, Lynn S; Bryant, V Joan; Hamlett, Carol L; Lambert, Warren

2012-01-01

In a sample of 195 first graders selected for poor reading performance, the authors explored four cognitive predictors of later reading comprehension and reading disability (RD) status. In fall of first grade, the authors measured the children's phonological processing, rapid automatized naming (RAN), oral language comprehension, and nonverbal reasoning. Throughout first grade, they also modeled the students' reading progress by means of weekly Word Identification Fluency (WIF) tests to derive December and May intercepts. The authors assessed their reading comprehension in the spring of Grades 1-5. With the four cognitive variables and the WIF December intercept as predictors, 50.3% of the variance in fifth-grade reading comprehension was explained: 52.1% of this 50.3% was unique to the cognitive variables, 13.1% to the WIF December intercept, and 34.8% was shared. All five predictors were statistically significant. The same four cognitive variables with the May (rather than December) WIF intercept produced a model that explained 62.1% of the variance. Of this amount, the cognitive variables and May WIF intercept accounted for 34.5% and 27.7%, respectively; they shared 37.8%. All predictors in this model were statistically significant except RAN. Logistic regression analyses indicated that the accuracy with which the cognitive variables predicted end-of-fifth-grade RD status was 73.9%. The May WIF intercept contributed reliably to this prediction; the December WIF intercept did not. Results are discussed in terms of a role for cognitive abilities in identifying, classifying, and instructing students with severe reading problems.

Constrained Stochastic Extended Redundancy Analysis.

PubMed

DeSarbo, Wayne S; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco

2015-06-01

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Constructive Development and Counselor Competence

ERIC Educational Resources Information Center

Eriksen, Karen P.; McAuliffe, Garrett J.

2006-01-01

Developmental predictors of students' ability to learn counseling skills would help counselor educators select candidates and assist admitted students in their learning. The present research examined the relationship between adult development, as measured by the Learning Environment Preferences test (W. S. Moore, 1989) and the Defining Issues…

The SAFARI Score to Assess the Risk of Convulsive Seizure During Admission for Aneurysmal Subarachnoid Hemorrhage.

PubMed

Jaja, Blessing N R; Schweizer, Tom A; Claassen, Jan; Le Roux, Peter; Mayer, Stephan A; Macdonald, R Loch

2018-06-01

Seizure is a significant complication in patients under acute admission for aneurysmal SAH and could result in poor outcomes. Treatment strategies to optimize management will benefit from methods to better identify at-risk patients. To develop and validate a risk score for convulsive seizure during acute admission for SAH. A risk score was developed in 1500 patients from a single tertiary hospital and externally validated in 852 patients. Candidate predictors were identified by systematic review of the literature and were included in a backward stepwise logistic regression model with in-hospital seizure as a dependent variable. The risk score was assessed for discrimination using the area under the receiver operator characteristics curve (AUC) and for calibration using a goodness-of-fit test. The SAFARI score, based on 4 items (age ≥ 60 yr, seizure occurrence before hospitalization, ruptured aneurysm in the anterior circulation, and hydrocephalus requiring cerebrospinal fluid diversion), had AUC = 0.77, 95% confidence interval (CI): 0.73-0.82 in the development cohort. The validation cohort had AUC = 0.65, 95% CI 0.56-0.73. A calibrated increase in the risk of seizure was noted with increasing SAFARI score points. The SAFARI score is a simple tool that adequately stratified SAH patients according to their risk for seizure using a few readily derived predictor items. It may contribute to a more individualized management of seizure following SAH.

Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

PubMed

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.

Active Coping and Perceived Social Support Mediate the Relationship Between Physical Health and Resilience in Liver Transplant Candidates.

PubMed

Swanson, Amelia; Geller, Jessica; DeMartini, Kelly; Fernandez, Anne; Fehon, Dwain

2018-03-15

Without a transplant, end-stage liver disease is associated with significant morbidity and mortality. Transplant candidates endure physical and psychological stress while awaiting surgery, yet little is known about the relationship between physical health and psychological resilience during the wait-list period. This study examined predictors of psychological resilience and mediators of the relationship between physical health and psychological resilience in liver transplant candidates. Wait-listed candidates (N = 120) from a single Northeast transplant center completed assessments of physical functioning, coping, perceived social support, and resilience. Findings revealed that physical functioning, active coping, and perceived social support were positively associated with resilience; maladaptive coping was negatively associated with resilience. Perceived social support and active coping partially mediated the relationship between physical functioning and resilience. Transplant center care providers should promote active coping skills and reinforce the importance of effective social support networks. These interventions could increase psychological resilience among liver transplant candidates.

Predictors of Sustainability of Social Programs

ERIC Educational Resources Information Center

Savaya, Riki; Spiro, Shimon E.

2012-01-01

This article presents the findings of a large scale study that tested a comprehensive model of predictors of three manifestations of sustainability: continuation, institutionalization, and duration. Based on the literature the predictors were arrayed in four groups: variables pertaining to the project, the auspice organization, the community, and…

Estimating the Classification Efficiency of a Test Battery.

ERIC Educational Resources Information Center

De Corte, Wilfried

2000-01-01

Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)

Effects of Internship Predictors on Successful Field Experience.

ERIC Educational Resources Information Center

Beard, Fred; Morton, Linda

1999-01-01

Finds that a majority of advertising and public-relations interns found their internships successful. Indicates that successful internships depend on predictors given the least attention by school programs: quality of supervision was the most important single predictor variable, followed in importance by organizational practices/policies, positive…

Relations among Socioeconomic Status, Age, and Predictors of Phonological Awareness

ERIC Educational Resources Information Center

McDowell, Kimberly D.; Lonigan, Christopher J.; Goldstein, Howard

2007-01-01

Purpose: This study simultaneously examined predictors of phonological awareness within the framework of 2 theories: the phonological distinctness hypothesis and the lexical restructuring model. Additionally, age as a moderator of the relations between predictor variables and phonological awareness was examined. Method: This cross-sectional…

Carrying capacity for species richness as context for conservation: a case study of North American birds

Treesearch

Andrew J. Hansen; Linda Bowers Phillips; Curtis H. Flather; Jim Robinson-Cox

2011-01-01

We evaluated the leading hypotheses on biophysical factors affecting species richness for Breeding Bird Survey routes from areas with little influence of human activities.We then derived a best model based on information theory, and used this model to extrapolate SK across North America based on the biophysical predictor variables. The predictor variables included the...

Cognitive and Affective Variables and Their Relationships to Performance in a Lotus 1-2-3 Class.

ERIC Educational Resources Information Center

Guster, Dennis; Batt, Richard

1989-01-01

Describes study of two-year college students that was conducted to determine whether variables that were predictors of success in a programing class were also predictors of success in a package-oriented computer class using Lotus 1-2-3. Diagraming skill, critical thinking ability, spatial discrimination, and test anxiety level were examined. (11…

A Study of the Relationship between Social Support and Clergy Family Stress among Korean-American Baptist Pastors and Their Wives

ERIC Educational Resources Information Center

Shin, Min Young

2012-01-01

Problem: The first problem of this study was to determine the relationship between the clergy family stress scores as measured by the Clergy Family Inventory (CFLI) and the specified predictor variables of social support among Korean-American Baptist pastors. The specified predictor variables included tangible support, appraisal support,…

Strategic Interviewing to Detect Deception: Cues to Deception across Repeated Interviews

PubMed Central

Masip, Jaume; Blandón-Gitlin, Iris; Martínez, Carmen; Herrero, Carmen; Ibabe, Izaskun

2016-01-01

Previous deception research on repeated interviews found that liars are not less consistent than truth tellers, presumably because liars use a “repeat strategy” to be consistent across interviews. The goal of this study was to design an interview procedure to overcome this strategy. Innocent participants (truth tellers) and guilty participants (liars) had to convince an interviewer that they had performed several innocent activities rather than committing a mock crime. The interview focused on the innocent activities (alibi), contained specific central and peripheral questions, and was repeated after 1 week without forewarning. Cognitive load was increased by asking participants to reply quickly. The liars’ answers in replying to both central and peripheral questions were significantly less accurate, less consistent, and more evasive than the truth tellers’ answers. Logistic regression analyses yielded classification rates ranging from around 70% (with consistency as the predictor variable), 85% (with evasive answers as the predictor variable), to over 90% (with an improved measure of consistency that incorporated evasive answers as the predictor variable, as well as with response accuracy as the predictor variable). These classification rates were higher than the interviewers’ accuracy rate (54%). PMID:27847493

Antecedents of narcotic use and addiction. A study of 898 Vietnam veterans.

PubMed

Helzer, J E; Robins, L N; Davis, D H

1976-02-01

Previous studies of predictors of narcotic abuse have been retrospective and based on samples of long-term addicts obtained from legal or medical channels. There are several methodological problems in this approach. The present study is an attempt to test certain alleged predictors of narcotic use in a cohort of 898 Vietnam veterans. The design overcomes several of the methodological weaknesses of previous studies. Eight variables which have been reported as predictors of drug use or addiction in the drug literature were inquired about during a personal interview which included the premilitary life of each subject. The antecedent variables were socioeconomic background, inner city residence, psychiatric illness, broken home, race, employment history, education and antisocial history. Using information obtained from interviews and military records, we then tested the predictive value of each of these antecedents by comparing narcotic used and addiction in Vietman and use after Vietnam in men differing with respect to each antecedent. Results indicate that some of the variables were very poor, and others very good predictors of the various levels of narcotic involvement. The predictive value and overall importance of each of the variables we tested are discussed.

Childhood Depression: Relation to Adaptive, Clinical and Predictor Variables

PubMed Central

Garaigordobil, Maite; Bernarás, Elena; Jaureguizar, Joana; Machimbarrena, Juan M.

2017-01-01

The study had two goals: (1) to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2) to identify predictor variables of childhood depression. Participants were 420 students aged 7–10 years old (53.3% boys, 46.7% girls). Results revealed: (1) positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and childhood stress; and (2) negative correlations between depression and personal adaptation, global self-concept, social skills, and resilience (sense of competence and affiliation). Linear regression analysis including the global dimensions revealed 4 predictors of childhood depression that explained 50.6% of the variance: high clinical maladjustment, low global self-concept, high level of stress, and poor social skills. However, upon introducing the sub-dimensions, 9 predictor variables emerged that explained 56.4% of the variance: many internalizing problems, low family self-concept, high anxiety, low responsibility, low personal self-assessment, high social stress, few aggressive behaviors toward peers, many health/psychosomatic problems, and external locus of control. The discussion addresses the importance of implementing prevention programs for childhood depression at early ages. PMID:28572787

Do Cognitive Models Help in Predicting the Severity of Posttraumatic Stress Disorder, Phobia, and Depression After Motor Vehicle Accidents? A Prospective Longitudinal Study

PubMed Central

Ehring, Thomas; Ehlers, Anke; Glucksman, Edward

2008-01-01

The study investigated the power of theoretically derived cognitive variables to predict posttraumatic stress disorder (PTSD), travel phobia, and depression following injury in a motor vehicle accident (MVA). MVA survivors (N = 147) were assessed at the emergency department on the day of their accident and 2 weeks, 1 month, 3 months, and 6 months later. Diagnoses were established with the Structured Clinical Interview for DSM–IV. Predictors included initial symptom severities; variables established as predictors of PTSD in E. J. Ozer, S. R. Best, T. L. Lipsey, and D. S. Weiss's (2003) meta-analysis; and variables derived from cognitive models of PTSD, phobia, and depression. Results of nonparametric multiple regression analyses showed that the cognitive variables predicted subsequent PTSD and depression severities over and above what could be predicted from initial symptom levels. They also showed greater predictive power than the established predictors, although the latter showed similar effect sizes as in the meta-analysis. In addition, the predictors derived from cognitive models of PTSD and depression were disorder-specific. The results support the role of cognitive factors in the maintenance of emotional disorders following trauma. PMID:18377119

Medical colleges admission test in Punjab, Pakistan.

PubMed

Khan, Junaid Sarfraz; Biggs, John S G; Bano, Tahira; Mukhtar, Osama; Tabasum, Saima; Mubasshar, Malik Hussain

2013-01-01

Nearly 18,000 candidates securing 60% and above marks in Higher Secondary School Certificate (HSSC) examination contest for admission in Medical Colleges, in Punjab, Pakistan by sitting in the Medical College Admission Test (MCAT) each summer. This cross-sectional study was conducted to identify patterns related to demographic, economic and educational backgrounds, over a two-year-period, in this population, and how HSSC and MCAT marks predict future performance of the selected candidates. Marks obtained by candidates in HSSC, MCAT, and 1st Professional MBBS (Part-I) Examinations over two years 2008-2009, were analysed using parametric tests in SPSS. Total 18,090 candidates in 2008 and 18,486 in 2009 sat in the MCAT. National IHSSC candidates scored higher marks in HSSC and MCAT but lower marks than their foreign qualified HSSC counterparts (e.g., Advanced-levels from Cambridge University, UK) in Part-I overall and in all its subcomponents individually (p < 0.05). Female students scored higher marks than males in HSSC (p > 0.05). MCAT (p > 0.05) and Part-I theory, practical, viva voce, continuous assessment and Objective-Structured Performance Evaluation (OSPE) components (p < 0.05). In both years, students from the Dera Ghazi Khan District scored the highest marks in the HSSC Examinations (p < 0.05) but least marks in MCAT in 2008 (p < 0.05) and in Part-I in 2008 and 2009 (p < 0.05). Students from 'tougher' Boards like Rawalpindi in 2008 and the Federal Board in 2009 who scored least marks in HSSC scored highest marks in MCAT. and in Part-I Examinations (p < 0.05). Linear regression on Part-I by taking HSSC and MCAT marks as independent variables showed that the MCAT marks exerted the greatest positive influence consistently at 0.104 (2008) and 0.106 (2009). In 2009 HSSC marks were shown to exert a negative influence (-0.08) on Part-I. There is need to standardise HSSC education and examination across all Intermediate Boards. MCAT is a better predictor of Medical Students' future performance.

A Socioecological Predication Model of Posttraumatic Stress Disorder in Low-Income, High-Risk Prenatal Native Hawaiian/Pacific Islander Women.

PubMed

Dodgson, Joan E; Oneha, Mary Frances; Choi, Myunghan

2014-01-01

Only recently has perinatal posttraumatic stress disorder (PTSD) been researched in any depth; however, the causes and consequences of this serious illness remain unclear. Most commonly, childbirth trauma and interpersonal violence have been reported as contributing factors. However, not all Native Hawaiian/Pacific Islander (NHPI) women who experience these events experience PTSD. The factors affecting PTSD are many and complex, intertwining individual, family, and community contexts. Using a socioecological framework, 3 levels of contextual variables were incorporated in this study (individual, family, and social/community). The purpose of this study was to determine the socioecological predictors associated with prenatal PTSD among NHPI. A case-control design was used to collect retrospective data about socioecological variables from medical record data. The sample was low-income, high-risk NHPI women receiving perinatal health care at a rural community health center in Hawaii who screened positive (n = 55) or negative (n = 91) for PTSD. Hierarchical logistic regression was conducted to determine socioecological predictors of positive PTSD screening. Although the majority of women (66.4%) experienced some form of interpersonal violence, a constellation of significant predictor variables from all 3 levels of the model were identified: depression (individual level), lack of family support and family stress (family level), and violence (social/community level). Each of the predictor variables has been identified by other researchers as significantly affecting perinatal PTSD. However, it is because these variables occur together that a more complex picture emerges, suggesting the importance of considering multiple variables in context when identifying and caring for these women. Although additional research is needed, it is possible that the significant predictor variables could be useful in identifying women who are at higher risk for PTSD in other similar populations. © 2014 by the American College of Nurse‐Midwives.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

Is it all worth it? The experiences of new PhDs on the job market, 2007–10

PubMed Central

McFall, Brooke Helppie; Murray-Close, Marta; Willis, Robert J; Chen, Uniko

2016-01-01

This paper describes the job market experiences of new PhD economists, 2007-10. Using information from PhD programs' job candidate websites and original surveys, the authors present information about job candidates' characteristics, preferences and expectations; how job candidates fared at each stage of the market; and predictors of outcomes at each stage. Some information presented in this paper updates findings of prior studies. However, design features of the data used in this paper may result in more generalizable findings. This paper is unique in comparing pre-market expectations and preferences with post-market outcomes on the new PhD job market. It shows that outcomes tend to align with pre-market preferences, and candidates' expectations are somewhat predictive of their outcomes. Several analyses also shed light on sub-group differences. PMID:27616783

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

PubMed

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in using RF to develop predictive models with large environmental data sets.

How Binary Skills Obscure the Transition from Non-Mastery to Mastery

ERIC Educational Resources Information Center

Karelitz, Tzur M.

2008-01-01

What is the nature of latent predictors that facilitate diagnostic classification? Rupp and Templin (this issue) suggest that these predictors should be multidimensional, categorical variables that can be combined in various ways. Diagnostic Classification Models (DCM) typically use multiple categorical predictors to classify respondents into…

Examining Preservice Science Teacher Understanding of Nature of Science: Discriminating Variables on the Aspects of Nature of Science

NASA Astrophysics Data System (ADS)

Jones, William I.

This study examined the understanding of nature of science among participants in their final year of a 4-year undergraduate teacher education program at a Midwest liberal arts university. The Logic Model Process was used as an integrative framework to focus the collection, organization, analysis, and interpretation of the data for the purpose of (1) describing participant understanding of NOS and (2) to identify participant characteristics and teacher education program features related to those understandings. The Views of Nature of Science Questionnaire form C (VNOS-C) was used to survey participant understanding of 7 target aspects of Nature of Science (NOS). A rubric was developed from a review of the literature to categorize and score participant understanding of the target aspects of NOS. Participants' high school and college transcripts, planning guides for their respective teacher education program majors, and science content and science teaching methods course syllabi were examined to identify and categorize participant characteristics and teacher education program features. The R software (R Project for Statistical Computing, 2010) was used to conduct an exploratory analysis to determine correlations of the antecedent and transaction predictor variables with participants' scores on the 7 target aspects of NOS. Fourteen participant characteristics and teacher education program features were moderately and significantly ( p < .01) correlated with participant scores on the target aspects of NOS. The 6 antecedent predictor variables were entered into multiple regression analyses to determine the best-fit model of antecedent predictor variables for each target NOS aspect. The transaction predictor variables were entered into separate multiple regression analyses to determine the best-fit model of transaction predictor variables for each target NOS aspect. Variables from the best-fit antecedent and best-fit transaction models for each target aspect of NOS were then combined. A regression analysis for each of the combined models was conducted to determine the relative effect of these variables on the target aspects of NOS. Findings from the multiple regression analyses revealed that each of the fourteen predictor variables was present in the best-fit model for at least 1 of the 7 target aspects of NOS. However, not all of the predictor variables were statistically significant (p < .007) in the models and their effect (beta) varied. Participants in the teacher education program who had higher ACT Math scores, completed more high school science credits, and were enrolled either in the Middle Childhood with a science concentration program major or in the Adolescent/Young Adult Science Education program major were more likely to have an informed understanding on each of the 7 target aspects of NOS. Analyses of the planning guides and the course syllabi in each teacher education program major revealed differences between the program majors that may account for the results.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Response variability in rapid automatized naming predicts reading comprehension

PubMed Central

Li, James J.; Cutting, Laurie E.; Ryan, Matthew; Zilioli, Monica; Denckla, Martha B.; Mahone, E. Mark

2009-01-01

A total of 37 children ages 8 to 14 years, screened for word-reading difficulties (23 with attention-deficit/hyperactivity disorder, ADHD; 14 controls) completed oral reading and rapid automatized naming (RAN) tests. RAN trials were segmented into pause and articulation time and intraindividual variability. There were no group differences on reading or RAN variables. Color- and letter-naming pause times and number-naming articulation time were significant predictors of reading fluency. In contrast, number and letter pause variability were predictors of comprehension. Results support analysis of subcomponents of RAN and add to literature emphasizing intraindividual variability as a marker for response preparation, which has relevance to reading comprehension. PMID:19221923

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Predictors of self-reported negative mood following a depressive mood induction procedure across previously depressed, currently anxious, and control individuals.

PubMed

Scherrer, Martin C; Dobson, Keith S; Quigley, Leanne

2014-09-01

This study identified and examined a set of potential predictors of self-reported negative mood following a depressive mood induction procedure (MIP) in a sample of previously depressed, clinically anxious, and control participants. The examined predictor variables were selected on the basis of previous research and theories of depression, and included symptoms of depression and anxiety, negative and positive affect, negative and positive automatic thoughts, dysfunctional beliefs, rumination, self-concept, and occurrence and perceived unpleasantness of recent negative events. The sample consisted of 33 previously depressed, 22 currently anxious, and 26 non-clinical control participants, recruited from community sources. Participant group status was confirmed through structured diagnostic interviews. Participants completed the Velten negative self-statement MIP as well as self-report questionnaires of affective, cognitive, and psychosocial variables selected as potential predictors of mood change. Symptoms of anxiety were associated with increased self-reported negative mood shift following the MIP in previously depressed participants, but not clinically anxious or control participants. Increased occurrence of recent negative events was a marginally significant predictor of negative mood shift for the previously depressed participants only. None of the other examined variables was significant predictors of MIP response for any of the participant groups. These results identify factors that may increase susceptibility to negative mood states in previously depressed individuals, with implications for theory and prevention of relapse to depression. The findings also identify a number of affective, cognitive, and psychosocial variables that do not appear to influence mood change following a depressive MIP in previously depressed, currently anxious, and control individuals. Limitations of the study and directions for future research are discussed. Current anxiety symptomatology was a significant predictor and occurrence of recent negative events was a marginally significant predictor of greater negative mood shift following the depressive mood induction for previously depressed individuals. None of the examined variables predicted change in mood following the depressive mood induction for currently anxious or control individuals. These results suggest that anxiety symptoms and experience with negative events may increase risk for experiencing depressive mood states among individuals with a vulnerability to depression. The generalizability of the present results to individuals with comorbid depression and anxiety is limited. Future research employing appropriate statistical approaches for confirmatory research is needed to test and confirm the present results. © 2014 The British Psychological Society.

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

Transiting exoplanet candidates from K2 Campaigns 5 and 6

NASA Astrophysics Data System (ADS)

Pope, Benjamin J. S.; Parviainen, Hannu; Aigrain, Suzanne

2016-10-01

We introduce a new transit search and vetting pipeline for observations from the K2 mission, and present the candidate transiting planets identified by this pipeline out of the targets in Campaigns 5 and 6. Our pipeline uses the Gaussian process-based K2SC code to correct for the K2 pointing systematics and simultaneously model stellar variability. The systematics-corrected, variability-detrended light curves are searched for transits with the box-least-squares method, and a period-dependent detection threshold is used to generate a preliminary candidate list. Two or three individuals vet each candidate manually to produce the final candidate list, using a set of automatically generated transit fits and assorted diagnostic tests to inform the vetting. We detect 145 single-planet system candidates and 5 multi-planet systems, independently recovering the previously published hot Jupiters EPIC 212110888b, WASP-55b (EPIC 212300977b) and Qatar-2b (EPIC 212756297b). We also report the outcome of reconnaissance spectroscopy carried out for all candidates with Kepler magnitude Kp ≤ 13, identifying 12 targets as likely false positives. We compare our results to those of other K2 transit search pipelines, noting that ours performs particularly well for variable and/or active stars, but that the results are very similar overall. All the light curves and code used in the transit search and vetting process are publicly available, as are the follow-up spectra.

Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

NASA Astrophysics Data System (ADS)

Mansuy, N. R.; Paré, D.; Thiffault, E.

2015-12-01

Large-scale mapping of soil properties is increasingly important for environmental resource management. Whileforested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.The objective of this study was to generate continuous national maps of selected soil variables (C, N andsoil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using thekNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climaticvariables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. Therelative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave valuesranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered agood imputation in light of the low density of points used in this study. The study demonstrates strong capabilitiesfor mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of CanadaSystem, which is largely oriented towards the agricultural landbase. The methodology used here can potentiallycontribute to the national and international need for spatially explicit soil information in resource managementscience.

Reliability, reference values and predictor variables of the ulnar sensory nerve in disease free adults.

PubMed

Ruediger, T M; Allison, S C; Moore, J M; Wainner, R S

2014-09-01

The purposes of this descriptive and exploratory study were to examine electrophysiological measures of ulnar sensory nerve function in disease free adults to determine reliability, determine reference values computed with appropriate statistical methods, and examine predictive ability of anthropometric variables. Antidromic sensory nerve conduction studies of the ulnar nerve using surface electrodes were performed on 100 volunteers. Reference values were computed from optimally transformed data. Reliability was computed from 30 subjects. Multiple linear regression models were constructed from four predictor variables. Reliability was greater than 0.85 for all paired measures. Responses were elicited in all subjects; reference values for sensory nerve action potential (SNAP) amplitude from above elbow stimulation are 3.3 μV and decrement across-elbow less than 46%. No single predictor variable accounted for more than 15% of the variance in the response. Electrophysiologic measures of the ulnar sensory nerve are reliable. Absent SNAP responses are inconsistent with disease free individuals. Reference values recommended in this report are based on appropriate transformations of non-normally distributed data. No strong statistical model of prediction could be derived from the limited set of predictor variables. Reliability analyses combined with relatively low level of measurement error suggest that ulnar sensory reference values may be used with confidence. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Self Efficacy and Some Demographic Variables as Predictors of Occupational Stress among Primary School Teachers in Delta State of Nigeria

ERIC Educational Resources Information Center

Akpochafo, G. O.

2014-01-01

This study investigated self efficacy and some demographic variables as predictors of occupational stress among primary school teachers in Delta State. Three hypotheses were formulated to guide the study. The study adopted a descriptive survey design that utilized an expost-facto research type. A sample of one hundred and twenty primary school…

Identification of Variables Associated with Group Separation in Descriptive Discriminant Analysis: Comparison of Methods for Interpreting Structure Coefficients

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…

Influence of Selected Personal Characteristics and County Situational Factors on Time Allocated to Dairy Subjects by Extension Agents in Selected Tennessee Counties.

ERIC Educational Resources Information Center

Northcutt, Sherwin Dean; And Others

The study deals with various predictors of time spent on dairy subjects by Extension agents and predictors of contacts made by agents with dairy clientele. Purposes were to determine the relationships, if any, between various independent variables and groups of independent variables (agents' background and training, county dairy situation, agents'…

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

PubMed

Jaime-González, Carlos; Acebes, Pablo; Mateos, Ana; Mezquida, Eduardo T

2017-01-01

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

The screening role of an introductory course in cognitive therapy training.

PubMed

Pehlivanidis, Artemios; Papanikolaou, Katerina; Politis, Antonis; Liossi, Angeliki; Daskalopoulou, Evgenia; Gournellis, Rossetos; Soldatos, Marina; Papakosta, Vasiliki Maria; Zervas, Ioannis; Papakostas, Yiannis G

2006-01-01

This study examines the role of an introductory course in cognitive therapy and the relative importance of trainees' characteristics in the selection process for an advanced course in cognitive therapy. The authors assessed the files of all trainees who completed one academic year introductory course in cognitive therapy over the last seven consecutive years (N = 203). The authors examined variables such as previous training, overall involvement during the course, performance, and ability to relate to others, as well as the trainer's evaluations of their performance. Interaction skills in group situations and performance in written assignments were better predictors for admission into the advanced course. Trainees' abilities to learn and to successfully relate to others in group situations are critical for entering an advanced cognitive therapy training course. These findings question the policy of full-scale training in cognitive therapy based merely on the candidates' professional background, stressing instead the merits of an introductory course as an appropriate screening procedure.

Psychosocial variables and time to injury onset: a hurdle regression analysis model.

PubMed

Sibold, Jeremy; Zizzi, Samuel

2012-01-01

Psychological variables have been shown to be related to athletic injury and time missed from participation in sport. We are unaware of any empirical examination of the influence of psychological variables on time to onset of injury. To examine the influence of orthopaedic and psychosocial variables on time to injury in college athletes. One hundred seventy-seven (men 5 116, women 5 61; age 5 19.45 6 1.39 years) National Collegiate Athletic Association Division II athletes. Hurdle regression analysis (HRA) was used to determine the influence of predictor variables on days to first injury. Worry (z = 2.98, P = .003), concentration disruption (z = -3.95, P < .001), and negative life-event stress (z = 5.02, P < .001) were robust predictors of days to injury. Orthopaedic risk score was not a predictor (z = 1.28, P = .20). These findings support previous research on the stress-injury relationship, and our group is the first to use HRA in athletic injury data. These data support the addition of psychological screening as part of preseason health examinations for collegiate athletes.

Heart rate variability: Pre-deployment predictor of post-deployment PTSD symptoms

PubMed Central

Pyne, Jeffrey M.; Constans, Joseph I.; Wiederhold, Mark D.; Gibson, Douglas P.; Kimbrell, Timothy; Kramer, Teresa L.; Pitcock, Jeffery A.; Han, Xiaotong; Williams, D. Keith; Chartrand, Don; Gevirtz, Richard N.; Spira, James; Wiederhold, Brenda K.; McCraty, Rollin; McCune, Thomas R.

2017-01-01

Heart rate variability is a physiological measure associated with autonomic nervous system activity. This study hypothesized that lower pre-deployment HRV would be associated with higher post-deployment post-traumatic stress disorder (PTSD) symptoms. Three-hundred-forty-three Army National Guard soldiers enrolled in the Warriors Achieving Resilience (WAR) study were analyzed. The primary outcome was PTSD symptom severity using the PTSD Checklist – Military version (PCL) measured at baseline, 3- and 12-month post-deployment. Heart rate variability predictor variables included: high frequency power (HF) and standard deviation of the normal cardiac inter-beat interval (SDNN). Generalized linear mixed models revealed that the pre-deployment PCL*ln(HF) interaction term was significant (p < 0.0001). Pre-deployment SDNN was not a significant predictor of post-deployment PCL. Covariates included age, pre-deployment PCL, race/ethnicity, marital status, tobacco use, childhood abuse, pre-deployment traumatic brain injury, and previous combat zone deployment. Pre-deployment heart rate variability predicts post-deployment PTSD symptoms in the context of higher pre-deployment PCL scores. PMID:27773678

Assessing the accuracy and stability of variable selection ...

EPA Pesticide Factsheets

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used, or stepwise procedures are employed which iteratively add/remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating dataset consists of the good/poor condition of n=1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p=212) of landscape features from the StreamCat dataset. Two types of RF models are compared: a full variable set model with all 212 predictors, and a reduced variable set model selected using a backwards elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors, and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substanti

Role of maternal health and infant inflammation in nutritional and neurodevelopmental outcomes of two-year-old Bangladeshi children.

PubMed

Donowitz, Jeffrey R; Cook, Heather; Alam, Masud; Tofail, Fahmida; Kabir, Mamun; Colgate, E Ross; Carmolli, Marya P; Kirkpatrick, Beth D; Nelson, Charles A; Ma, Jennie Z; Haque, Rashidul; Petri, William A

2018-05-01

Previous studies have shown maternal, inflammatory, and socioeconomic variables to be associated with growth and neurodevelopment in children from low-income countries. However, these outcomes are multifactorial and work describing which predictors most strongly influence them is lacking. We conducted a longitudinal study of Bangladeshi children from birth to two years to assess oral vaccine efficacy. Variables pertaining to maternal and perinatal health, socioeconomic status, early childhood enteric and systemic inflammation, and anthropometry were collected. Bayley-III neurodevelopmental assessment was conducted at two years. As a secondary analysis, we employed hierarchical cluster and random forests techniques to identify and rank which variables predicted growth and neurodevelopment. Cluster analysis demonstrated three distinct groups of predictors. Mother's weight and length-for-age Z score (LAZ) at enrollment were the strongest predictors of LAZ at two years. Cognitive score on Bayley-III was strongly predicted by weight-for-age (WAZ) at enrollment, income, and LAZ at enrollment. Top predictors of language included Rotavirus vaccination, plasma IL 5, sCD14, TNFα, mother's weight, and male gender. Motor function was best predicted by fecal calprotectin, WAZ at enrollment, fecal neopterin, and plasma CRP index. The strongest predictors for social-emotional score included plasma sCD14, income, WAZ at enrollment, and LAZ at enrollment. Based on the random forests' predictions, the estimated percentage of variation explained was 35.4% for LAZ at two years, 34.3% for ΔLAZ, 42.7% for cognitive score, 28.1% for language, 40.8% for motor, and 37.9% for social-emotional score. Birth anthropometry and maternal weight were strong predictors of growth while enteric and systemic inflammation had stronger associations with neurodevelopment. Birth anthropometry was a powerful predictor for all outcomes. These data suggest that further study of stunting in low-income settings should include variables relating to maternal and prenatal health, while investigations focusing on neurodevelopmental outcomes should additionally target causes of systemic and enteric inflammation.

Distribution and predictors of wing shape and size variability in three sister species of solitary bees

PubMed Central

Prunier, Jérôme G.; Dewulf, Alexandre; Kuhlmann, Michael; Michez, Denis

2017-01-01

Morphological traits can be highly variable over time in a particular geographical area. Different selective pressures shape those traits, which is crucial in evolutionary biology. Among these traits, insect wing morphometry has already been widely used to describe phenotypic variability at the inter-specific level. On the contrary, fewer studies have focused on intra-specific wing morphometric variability. Yet, such investigations are relevant to study potential convergences of variation that could highlight micro-evolutionary processes. The recent sampling and sequencing of three solitary bees of the genus Melitta across their entire species range provides an excellent opportunity to jointly analyse genetic and morphometric variability. In the present study, we first aim to analyse the spatial distribution of the wing shape and centroid size (used as a proxy for body size) variability. Secondly, we aim to test different potential predictors of this variability at both the intra- and inter-population levels, which includes genetic variability, but also geographic locations and distances, elevation, annual mean temperature and precipitation. The comparison of spatial distribution of intra-population morphometric diversity does not reveal any convergent pattern between species, thus undermining the assumption of a potential local and selective adaptation at the population level. Regarding intra-specific wing shape differentiation, our results reveal that some tested predictors, such as geographic and genetic distances, are associated with a significant correlation for some species. However, none of these predictors are systematically identified for the three species as an important factor that could explain the intra-specific morphometric variability. As a conclusion, for the three solitary bee species and at the scale of this study, our results clearly tend to discard the assumption of the existence of a common pattern of intra-specific signal/structure within the intra-specific wing shape and body size variability. PMID:28273178

Analysis of PGY-1 Pharmacy Resident Candidate Letters of Recommendation at an Academically Affiliated Residency Program.

PubMed

McLaughlin, Milena M; Masic, Dalila; Gettig, Jacob P

2018-04-01

Letters of recommendation (LORs) are a critical component for differentiating among similarly qualified pharmacy residency candidates. These letters contain information that is difficult to ascertain from curricula vitae and pharmacy school transcripts. LOR writers may use any words or phrases appropriate for each candidate as there is no set framework for LORs. The objective of this study was to characterize descriptive themes in postgraduate year 1 (PGY-1) pharmacy residency candidates' LORs and to examine which themes of PGY-1 pharmacy residency candidates' LORs are predictive of an interview invitation at an academically affiliated residency program. LORs for candidates from the Pharmacy Online Residency Centralized Application System (PhORCAS) from 2013 and 2014 for the Midwestern University PGY-1 Pharmacy Residency were analyzed. LOR characteristics and descriptive themes were collected. All scores for candidate characteristics and overall PhORCAS recommendation were also recorded. A total of 351 LORs for 111 candidates from 2013 (n = 47 candidates) and 2014 (n = 64 candidates) were analyzed; 36 (32.4%) total candidates were offered an interview. Themes that were identified as predictors of an interview included a higher median (interquartile range) number of standout words (3 words [1.3-4] vs 3.8 words [2.5-5.5], P < .01) and teaching references (3.7 words [2.7-6] vs 5.7 words [3.7-7.8], P = .01). For this residency program, standout words and teaching references were important when offering interviews.

Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: A bicenter study from north of Iran.

PubMed

Salehpoor, Ghasem; Rezaei, Sajjad; Hosseininezhad, Mozaffar

2014-11-01

Although studies have demonstrated significant negative relationships between quality of life (QOL), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on QOL is in the relative importance of these predictors. Also, there is lack of adequate knowledge about the actual contribution of each of them in the prediction of QOL dimensions. Thus, the main objective of this study is to assess the role of fatigue, depression, anxiety, and stress in relation to QOL of multiple sclerosis (MS) patients. One hundred and sixty-two MS patients completed the questionnaire on demographic variables, and then they were evaluated by the Persian versions of Short-Form Health Survey Questionnaire (SF-36), Fatigue Survey Scale (FSS), and Depression, Anxiety, Stress Scale-21 (DASS-21). Data were analyzed by Pearson correlation coefficient and hierarchical regression. Correlation analysis showed a significant relationship between QOL elements in SF-36 (physical component summary and mental component summary) and depression, fatigue, stress, and anxiety (P < 0.01). Hierarchical regression analysis indicated that among the predictor variables in the final step, fatigue, depression, and anxiety were identified as the physical component summary predictor variables. Anxiety was found to be the most powerful predictor variable amongst all (β = -0.46, P < 0.001). Furthermore, results have shown depression as the only significant mental component summary predictor variable (β = -0.39, P < 0.001). This study has highlighted the role of anxiety, fatigue, and depression in physical dimensions and the role of depression in psychological dimensions of the lives of MS patients. In addition, the findings of this study indirectly suggest that psychological interventions for reducing fatigue, depression, and anxiety can lead to improved QOL of MS patients.

Predictors of College Retention and Performance between Regular and Special Admissions

ERIC Educational Resources Information Center

Kim, Johyun

2015-01-01

This predictive correlational research study examined the effect of cognitive, demographic, and socioeconomic variables as predictors of regular and special admission students' first-year GPA and retention among a sample of 7,045 students. Findings indicated high school GPA and ACT scores were the two most effective predictors of regular and…

Examination of Predictors and Moderators for Self-Help Treatments of Binge-Eating Disorder

ERIC Educational Resources Information Center

Masheb, Robin M.; Grilo, Carlos M.

2008-01-01

Predictors and moderators of outcomes were examined in 75 overweight patients with binge-eating disorder (BED) who participated in a randomized clinical trial of guided self-help treatments. Age variables, psychiatric and personality disorder comorbidity, and clinical characteristics were tested as predictors and moderators of treatment outcomes.…

Adolescent Mothers and Depression: Predictors of Resilience and Risk through the Toddler Years

ERIC Educational Resources Information Center

Eshbaugh, Elaine M.

2006-01-01

This study investigated predictors of depression in 278 African-American, 206 European-American, and 122 Hispanic teen mothers approximately 36 months after the birth while controlling for depression 14 months after the birth. Predictor variables were age, ethnicity, mastery, knowledge of development, and parental distress. Younger teens were not…

Measuring Teacher Quality: Continuing the Search for Policy-Relevant Predictors of Student Achievement

ERIC Educational Resources Information Center

Knoeppel, Robert C.; Logan, Joyce P.; Keiser, Clare M.

2005-01-01

The purpose of this study was to investigate the potential viability of the variable certification by the National Board for Professional Teaching Standards (NBPTS) as a policy-relevant predictor of student achievement. Because research has identified the teacher as the most important school-related predictor of student achievement, more research…

Physical Activity and Perceived Self-Efficacy in Older Adults.

ERIC Educational Resources Information Center

Langan, Mary E.; Marotta, Sylvia A.

2000-01-01

The purpose of this study was to examine predictors of self-efficacy in older adults, with physical activity, age, and sex as the predictor variables. Regression analyses revealed physical activity to be the only statistically significant predictor of self-efficacy. These findings may be of interest to counselors who work with older people.…

Comparison of Critical Listening Proficiency of Teacher Candidates in Terms of Several Variables

ERIC Educational Resources Information Center

Kazu, Hilal; Demiralp, Demet

2017-01-01

Purpose: The research has been designed to determine the level of critical listening proficiency of the teacher candidates. It aims at finding answers to the following questions: (1) What is the level of critical listening proficiency of teacher candidates? (2) Do the teacher candidates' levels of critical listening proficiency indicate a…

Peer Ratings as Predictors of Success in Military Aviation.

ERIC Educational Resources Information Center

Wahlberg, James L.; And Others

Three experimental peer rating forms were developed for use in research in prediction of the aviation training performance criterion--completion/attrition--from the training program for Aviation Warrant Officer Candidates at the U.S. Army Helicopter School. This paper describes the construction of the ratings, the "Potential Aviator…

Do State Examinations Measure Teacher Quality?

ERIC Educational Resources Information Center

Harrell, Pamela Esprivalo

2009-01-01

This study investigates teacher content knowledge of candidates enrolled in an online graduate teacher certification programme. Descriptive data and linear regression were used to draw conclusions about the content area knowledge of individuals in the sample and the significance of the predictors examined. Descriptive data show 1/3 of the 8-12…

Validity of the air traffic control specialist nonradar screen as a predictor of performance in radar based ATC training.

DOT National Transportation Integrated Search

1994-04-01

Between January 1986 and March 1992, the Federal Aviation Administration's 42-day Nonradar Screen was used to identify Air Traffic Control Specialist (ATCS) candidates with the highest potential to succeed in the rigorous ATCS field training program....

Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics.

PubMed

Guerrero, Jimena; Andrello, Marco; Burgarella, Concetta; Manel, Stephanie

2018-07-01

Spatial differences in environmental selective pressures interact with the genomes of organisms, ultimately leading to local adaptation. Landscape genomics is an emergent research area that uncovers genome-environment associations, thus allowing researchers to identify candidate loci for adaptation to specific environmental variables. In the present study, we used latent factor mixed models (LFMMs) and Moran spectral outlier detection/randomization (MSOD-MSR) to identify candidate loci for adaptation to 10 environmental variables (climatic, soil and atmospheric) among 43 515 single nucleotide polymorphisms (SNPs) from 202 accessions of the model legume Medicago truncatula. Soil variables were associated with a large number of candidate loci identified through both LFMMs and MSOD-MSR. Genes tagged by candidate loci associated with drought and salinity are involved in the response to biotic and abiotic stresses, while those tagged by candidates associated with soil nitrogen and atmospheric nitrogen, participate in the legume-rhizobia symbiosis. Candidate SNPs identified through both LFMMs and MSOD-MSR explained up to 56% of variance in flowering traits. Our findings highlight the importance of soil in driving adaptation in the system and elucidate the basis of evolutionary potential of M. truncatula to respond to global climate change and anthropogenic disruption of the nitrogen cycle. © 2018 The Authors New Phytologist © 2018 New Phytologist Trust.

Serial position effects are sensitive predictors of conversion from MCI to Alzheimer's disease dementia.

PubMed

Egli, Simone C; Beck, Irene R; Berres, Manfred; Foldi, Nancy S; Monsch, Andreas U; Sollberger, Marc

2014-10-01

It is unclear whether the predictive strength of established cognitive variables for progression to Alzheimer's disease (AD) dementia from mild cognitive impairment (MCI) varies depending on time to conversion. We investigated which cognitive variables were best predictors, and which of these variables remained predictive for patients with longer times to conversion. Seventy-five participants with MCI were assessed on measures of learning, memory, language, and executive function. Relative predictive strengths of these measures were analyzed using Cox regression models. Measures of word-list position-namely, serial position scores-together with Short Delay Free Recall of word-list learning best predicted conversion to AD dementia. However, only serial position scores predicted those participants with longer time to conversion. Results emphasize that the predictive strength of cognitive variables varies depending on time to conversion to dementia. Moreover, finer measures of learning captured by serial position scores were the most sensitive predictors of AD dementia. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Prediction of problematic wine fermentations using artificial neural networks.

PubMed

Román, R César; Hernández, O Gonzalo; Urtubia, U Alejandra

2011-11-01

Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.

Predictors of physical performance and functional ability in people 50+ with and without fibromyalgia.

PubMed

Jones, C Jessie; Rutledge, Dana N; Aquino, Jordan

2010-07-01

The purposes of this study were to determine whether people with and without fibromyalgia (FM) age 50 yr and above showed differences in physical performance and perceived functional ability and to determine whether age, gender, depression, and physical activity level altered the impact of FM status on these factors. Dependent variables included perceived function and 6 performance measures (multidimensional balance, aerobic endurance, overall functional mobility, lower body strength, and gait velocity-normal or fast). Independent (predictor) variables were FM status, age, gender, depression, and physical activity level. Results indicated significant differences between adults with and without FM on all physical-performance measures and perceived function. Linear-regression models showed that the contribution of significant predictors was in expected directions. All regression models were significant, accounting for 16-65% of variance in the dependent variables.

Social connectedness and self-esteem: predictors of resilience in mental health among maltreated homeless youth.

PubMed

Dang, Michelle T

2014-03-01

The purpose of this cross-sectional study was to explore social connectedness and self-esteem as predictors of resilience among homeless youth with histories of maltreatment. Connectedness variables included family connectedness, school connectedness, and affiliation with prosocial peers. The sample included 150 homeless youth aged 14 to 21 (mean age = 18 years) with the majority being an ethnic minority. Participants completed surveys using audio-CASI. Results revealed that youth with higher levels of social connectedness and self-esteem reported lower levels of psychological distress. When all predictor variables were controlled in the analysis, self-esteem remained significant for predicting better mental health.

Predictors of workplace violence among female sex workers in Tijuana, Mexico.

PubMed

Katsulis, Yasmina; Durfee, Alesha; Lopez, Vera; Robillard, Alyssa

2015-05-01

For sex workers, differences in rates of exposure to workplace violence are likely influenced by a variety of risk factors, including where one works and under what circumstances. Economic stressors, such as housing insecurity, may also increase the likelihood of exposure. Bivariate analyses demonstrate statistically significant associations between workplace violence and selected predictor variables, including age, drug use, exchanging sex for goods, soliciting clients outdoors, and experiencing housing insecurity. Multivariate regression analysis shows that after controlling for each of these variables in one model, only soliciting clients outdoors and housing insecurity emerge as statistically significant predictors for workplace violence. © The Author(s) 2014.

An issue encountered in solving problems in electricity and magnetism: curvilinear coordinates

NASA Astrophysics Data System (ADS)

Gülçiçek, Çağlar; Damlı, Volkan

2016-11-01

In physics lectures on electromagnetic theory and mathematical methods, physics teacher candidates have some difficulties with curvilinear coordinate systems. According to our experience, based on both in-class interactions and teacher candidates’ answers in test papers, they do not seem to have understood the variables in curvilinear coordinate systems very well. For this reason, the problems that physics teacher candidates have with variables in curvilinear coordinate systems have been selected as a study subject. The aim of this study is to find the physics teacher candidates’ problems with determining the variables of drawn shapes, and problems with drawing shapes based on given variables in curvilinear coordinate systems. Two different assessment tests were used in the study to achieve this aim. The curvilinear coordinates drawing test (CCDrT) was used to discover their problems related to drawing shapes, and the curvilinear coordinates detection test (CCDeT) was used to find out about problems related to determining variables. According to the findings obtained from both tests, most physics teacher candidates have problems with the ϕ variable, while they have limited problems with the r variable. Questions that are mostly answered wrongly have some common properties, such as value. According to inferential statistics, there is no significant difference between the means of the CCDeT and CCDrT scores. The mean of the CCDeT scores is only 4.63 and the mean of the CCDrT is only 4.66. Briefly, we can say that most physics teacher candidates have problems with drawing a shape using the variables of curvilinear coordinate systems or in determining the variables of drawn shapes. Part of this study was presented at the XI. National Science and Mathematics Education Congress (UFBMEK) in 2014.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Mandibular bone structure, bone mineral density, and clinical variables as fracture predictors: a 15-year follow-up of female patients in a dental clinic.

PubMed

Jonasson, Grethe; Billhult, Annika

2013-09-01

To compare three mandibular trabeculation evaluation methods, clinical variables, and osteoporosis as fracture predictors in women. One hundred and thirty-six female dental patients (35-94 years) answered a questionnaire in 1996 and 2011. Using intra-oral radiographs from 1996, five methods were compared as fracture predictors: (1) mandibular bone structure evaluated with a visual radiographic index, (2) bone texture, (3) size and number of intertrabecular spaces calculated with Jaw-X software, (4) fracture probability calculated with a fracture risk assessment tool (FRAX), and (5) osteoporosis diagnosis based on dual-energy-X-ray absorptiometry. Differences were assessed with the Mann-Whitney test and relative risk calculated. Previous fracture, gluco-corticoid medication, and bone texture were significant indicators of future and total (previous plus future) fracture. Osteoporosis diagnosis, sparse trabeculation, Jaw-X, and FRAX were significant predictors of total but not future fracture. Clinical and oral bone variables may identify individuals at greatest risk of fracture. Copyright © 2013 Elsevier Inc. All rights reserved.

Nonsuicidal self-injury in community adolescents: A systematic review of prospective predictors, mediators and moderators.

PubMed

Valencia-Agudo, Fatima; Burcher, Georgina Corbet; Ezpeleta, Lourdes; Kramer, Tami

2018-06-01

Nonsuicidal self-injury (NSSI) usually starts during adolescence and is associated with an array of psychological and psychiatric symptoms and future suicide attempts. The aim of this study is to determine prospective predictors, mediators and moderators of NSSI in adolescent community samples in order to target prevention and treatment strategies. Two team members searched online databases independently. Thirty-nine studies were included in the review. Several variables were seen to prospectively predict NSSI: female gender, family-related variables, peer victimisation, depression, previous NSSI and self-concept. Few studies analysed mediators and moderators. Low self-concept was highlighted as a relevant moderator in the relationship between intra/interpersonal variables and NSSI. Implications of these findings are discussed. The considerable heterogeneity between studies posed a limitation to determine robust predictors of NSSI. Further prospective studies using standardised measures of predictors and outcomes are needed to ascertain the most at risk individuals and develop prevention strategies. Copyright © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Predicting academic success among deaf college students.

PubMed

Convertino, Carol M; Marschark, Marc; Sapere, Patricia; Sarchet, Thomastine; Zupan, Megan

2009-01-01

For both practical and theoretical reasons, educators and educational researchers seek to determine predictors of academic success for students at different levels and from different populations. Studies involving hearing students at the postsecondary level have documented significant predictors of success relating to various demographic factors, school experience, and prior academic attainment. Studies involving deaf and hard-of-hearing students have focused primarily on younger students and variables such as degree of hearing loss, use of cochlear implants, educational placement, and communication factors-although these typically are considered only one or two at a time. The present investigation utilizes data from 10 previous experiments, all using the same paradigm, in an attempt to discern significant predictors of readiness for college (utilizing college entrance examination scores) and classroom learning at the college level (utilizing scores from tests in simulated classrooms). Academic preparation was a clear and consistent predictor in both domains, but the audiological and communication variables examined were not. Communication variables that were significant reflected benefits of language flexibility over skills in either spoken language or American Sign Language.

Ethnic Variables and Negative Life Events as Predictors of Depressive Symptoms and Suicidal Behaviors in Latino College Students: On the Centrality of "Receptivo a los Demás"

ERIC Educational Resources Information Center

Chang, Edward C.; Yu, Elizabeth A.; Yu, Tina; Kahle, Emma R.; Hernandez, Viviana; Kim, Jean M.; Jeglic, Elizabeth L.; Hirsch, Jameson K.

2016-01-01

In the present study, we examined ethnic variables (viz., multigroup ethnic identity and other group orientation) along with negative life events as predictors of depressive symptoms and suicidal behaviors in a sample of 156 (38 male and 118 female) Latino college students. Results of conducting hierarchical regression analyses indicated that the…

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

The influence of physical and cognitive factors on reactive agility performance in men basketball players.

PubMed

Scanlan, Aaron; Humphries, Brendan; Tucker, Patrick S; Dalbo, Vincent

2014-01-01

This study explored the influence of physical and cognitive measures on reactive agility performance in basketball players. Twelve men basketball players performed multiple sprint, Change of Direction Speed Test, and Reactive Agility Test trials. Pearson's correlation analyses were used to determine relationships between the predictor variables (stature, mass, body composition, 5-m, 10-m and 20-m sprint times, peak speed, closed-skill agility time, response time and decision-making time) and reactive agility time (response variable). Simple and stepwise regression analyses determined the individual influence of each predictor variable and the best predictor model for reactive agility time. Morphological (r = -0.45 to 0.19), sprint (r = -0.40 to 0.41) and change-of-direction speed measures (r = 0.43) had small to moderate correlations with reactive agility time. Response time (r = 0.76, P = 0.004) and decision-making time (r = 0.58, P = 0.049) had large to very large relationships with reactive agility time. Response time was identified as the sole predictor variable for reactive agility time in the stepwise model (R(2) = 0.58, P = 0.004). In conclusion, cognitive measures had the greatest influence on reactive agility performance in men basketball players. These findings suggest reaction and decision-making drills should be incorporated in basketball training programmes.

Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables

NASA Astrophysics Data System (ADS)

Whitford, Melinda M.

Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

Childhood maltreatment history as a risk factor for sexual harassment among U.S. Army soldiers.

PubMed

Rosen, L N; Martin, L

1998-01-01

Four different types of childhood maltreatment were examined as predictors of unwanted sexual experiences and acknowledged sexual harassment among male and female active duty soldiers in the United States Army. Predictor variables included childhood sexual abuse, physical-emotional abuse, physical neglect, and emotional neglect. Three types of unwanted sexual experiences in the workplace were examined as outcome variables: gender harassment, unwanted sexual attention, and coercion. Both sexual and physical-emotional abuse during childhood were found to be predictors of unwanted sexual experiences and of acknowledged sexual harassment in the workplace. Among female soldiers, the most severe type of unwanted experience-coercion-was predicted only by childhood physical-emotional abuse. Among male soldiers childhood sexual abuse was the strongest predictor of coercion. A greater variety of types of childhood maltreatment predicted sexual harassment outcomes for male soldiers. Childhood maltreatment and adult sexual harassment were predictors of psychological well-being for soldiers of both genders.

Predictors of outcome for cognitive behaviour therapy in binge eating disorder.

PubMed

Lammers, Mirjam W; Vroling, Maartje S; Ouwens, Machteld A; Engels, Rutger C M E; van Strien, Tatjana

2015-05-01

The aim of this naturalistic study was to identify pretreatment predictors of response to cognitive behaviour therapy in treatment-seeking patients with binge eating disorder (BED; N = 304). Furthermore, we examined end-of-treatment factors that predict treatment outcome 6 months later (N = 190). We assessed eating disorder psychopathology, general psychopathology, personality characteristics and demographic variables using self-report questionnaires. Treatment outcome was measured using the bulimia subscale of the Eating Disorder Inventory 1. Predictors were determined using hierarchical linear regression analyses. Several variables significantly predicted outcome, four of which were found to be both baseline predictors of treatment outcome and end-of-treatment predictors of follow-up: Higher levels of drive for thinness, higher levels of interoceptive awareness, lower levels of binge eating pathology and, in women, lower levels of body dissatisfaction predicted better outcome in the short and longer term. Based on these results, several suggestions are made to improve treatment outcome for BED patients. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Discrimination, Acculturation and Other Predictors of Depression among Pregnant Hispanic Women

PubMed Central

Walker, Janiece L.; Ruiz, R. Jeanne; Chinn, Juanita J.; Marti, Nathan; Ricks, Tiffany N.

2012-01-01

Objective The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. Design A prospective observational design was used. Setting Central and Gulf coast areas of Texas in obstetrical offices. Participants A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22–24 weeks gestation was used to collect data. Measures The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Results Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. Conclusions It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy. PMID:23140083

Metacommunity ecology meets biogeography: effects of geographical region, spatial dynamics and environmental filtering on community structure in aquatic organisms.

PubMed

Heino, Jani; Soininen, Janne; Alahuhta, Janne; Lappalainen, Jyrki; Virtanen, Risto

2017-01-01

Metacommunity patterns and underlying processes in aquatic organisms have typically been studied within a drainage basin. We examined variation in the composition of six freshwater organismal groups across various drainage basins in Finland. We first modelled spatial structures within each drainage basin using Moran eigenvector maps. Second, we partitioned variation in community structure among three groups of predictors using constrained ordination: (1) local environmental variables, (2) spatial variables, and (3) dummy variable drainage basin identity. Third, we examined turnover and nestedness components of multiple-site beta diversity, and tested the best fit patterns of our datasets using the "elements of metacommunity structure" analysis. Our results showed that basin identity and local environmental variables were significant predictors of community structure, whereas within-basin spatial effects were typically negligible. In half of the organismal groups (diatoms, bryophytes, zooplankton), basin identity was a slightly better predictor of community structure than local environmental variables, whereas the opposite was true for the remaining three organismal groups (insects, macrophytes, fish). Both pure basin and local environmental fractions were, however, significant after accounting for the effects of the other predictor variable sets. All organismal groups exhibited high levels of beta diversity, which was mostly attributable to the turnover component. Our results showed consistent Clementsian-type metacommunity structures, suggesting that subgroups of species responded similarly to environmental factors or drainage basin limits. We conclude that aquatic communities across large scales are mostly determined by environmental and basin effects, which leads to high beta diversity and prevalence of Clementsian community types.

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

NASA Astrophysics Data System (ADS)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Stress, anger and Mediterranean diet as predictors of metabolic syndrome.

PubMed

Garcia-Silva, Jaqueline; Navarrete Navarrete, Nuria; Ruano Rodríguez, Ana; Peralta-Ramírez, María Isabel; Mediavilla García, Juan Diego; Caballo, Vicente E

2017-10-30

Metabolic syndrome (MetS) is a cluster of metabolic conditions that include abdominal obesity, reduction in cholesterol concentrations linked to high density lipoproteins (HLDc), elevated triglycerides, increased blood pressure and hyperglycaemia. Given that this is a multicausal disease, the aim of this study is to identify the psychological, emotional and lifestyle variables that can have an influence on the different MetS components. A cross-sectional study with 103 patients with diagnostic criteria for MetS (47 male and 56 female). Anthropometric, clinical and analytical measurements were collected to assess the variables associated with MetS. The main psychological and emotional variables were also assessed. Different multiple linear regression tests were performed to identify which variables were predictive of MetS. The dependent variables were body mass index (BMI), abdominal circumference, HDLc, and quality of life, and the predictive variables were psychological stress, anger and adherence to a Mediterranean diet. The results showed that psychological stress was a predictor of quality of life (β=-0.55, P≤0). Similarly, anger was a predictor of BMI (β=0.23, P=.047) and abdominal circumference (β=0.27, P=.021). As expected, adherence to a Mediterranean diet was a predictor of HDLc (β=0.2, P=.045) and of quality of life (β=-0.18, P=.031). The results confirm a link between adherence to certain dietary habits and lifestyle, however they go one step further and show the importance of psychological and emotional factors like psychological stress and anger in some MetS components. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

Computational Analysis of Candidate Disease Genes and Variants for Salt-Sensitive Hypertension in Indigenous Southern Africans

PubMed Central

Tiffin, Nicki; Meintjes, Ayton; Ramesar, Rajkumar; Bajic, Vladimir B.; Rayner, Brian

2010-01-01

Multiple factors underlie susceptibility to essential hypertension, including a significant genetic and ethnic component, and environmental effects. Blood pressure response of hypertensive individuals to salt is heterogeneous, but salt sensitivity appears more prevalent in people of indigenous African origin. The underlying genetics of salt-sensitive hypertension, however, are poorly understood. In this study, computational methods including text- and data-mining have been used to select and prioritize candidate aetiological genes for salt-sensitive hypertension. Additionally, we have compared allele frequencies and copy number variation for single nucleotide polymorphisms in candidate genes between indigenous Southern African and Caucasian populations, with the aim of identifying candidate genes with significant variability between the population groups: identifying genetic variability between population groups can exploit ethnic differences in disease prevalence to aid with prioritisation of good candidate genes. Our top-ranking candidate genes include parathyroid hormone precursor (PTH) and type-1angiotensin II receptor (AGTR1). We propose that the candidate genes identified in this study warrant further investigation as potential aetiological genes for salt-sensitive hypertension. PMID:20886000

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

PubMed

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P < .001). In the updating process, age, history, and additional candidate predictors did not significantly increase discrimination, being 94%, and leaving only 4 predictors of the original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

A New Analytic Framework for Moderation Analysis --- Moving Beyond Analytic Interactions

PubMed Central

Tang, Wan; Yu, Qin; Crits-Christoph, Paul; Tu, Xin M.

2009-01-01

Conceptually, a moderator is a variable that modifies the effect of a predictor on a response. Analytically, a common approach as used in most moderation analyses is to add analytic interactions involving the predictor and moderator in the form of cross-variable products and test the significance of such terms. The narrow scope of such a procedure is inconsistent with the broader conceptual definition of moderation, leading to confusion in interpretation of study findings. In this paper, we develop a new approach to the analytic procedure that is consistent with the concept of moderation. The proposed framework defines moderation as a process that modifies an existing relationship between the predictor and the outcome, rather than simply a test of a predictor by moderator interaction. The approach is illustrated with data from a real study. PMID:20161453

The nature and use of prediction skills in a biological computer simulation

NASA Astrophysics Data System (ADS)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

Verification of relationships between anthropometric variables among ureteral stents recipients and ureteric lengths: a challenge for Vitruvian-da Vinci theory.

PubMed

Acelam, Philip A

2015-01-01

To determine and verify how anthropometric variables correlate to ureteric lengths and how well statistical models approximate the actual ureteric lengths. In this work, 129 charts of endourological patients (71 females and 58 males) were studied retrospectively. Data were gathered from various research centers from North and South America. Continuous data were studied using descriptive statistics. Anthropometric variables (age, body surface area, body weight, obesity, and stature) were utilized as predictors of ureteric lengths. Linear regressions and correlations were used for studying relationships between the predictors and the outcome variables (ureteric lengths); P-value was set at 0.05. To assess how well statistical models were capable of predicting the actual ureteric lengths, percentages (or ratios of matched to mismatched results) were employed. The results of the study show that anthropometric variables do not correlate well to ureteric lengths. Statistical models can partially estimate ureteric lengths. Out of the five anthropometric variables studied, three of them: body frame, stature, and weight, each with a P<0.0001, were significant. Two of the variables: age (R (2)=0.01; P=0.20) and obesity (R (2)=0.03; P=0.06), were found to be poor estimators of ureteric lengths. None of the predictors reached the expected (match:above:below) ratio of 1:0:0 to qualify as reliable predictors of ureteric lengths. There is not sufficient evidence to conclude that anthropometric variables can reliably predict ureteric lengths. These variables appear to lack adequate specificity as they failed to reach the expected (match:above:below) ratio of 1:0:0. Consequently, selections of ureteral stents continue to remain a challenge. However, height (R (2)=0.68) with the (match:above:below) ratio of 3:3:4 appears suited for use as estimator, but on the basis of decision rule. Additional research is recommended for stent improvements and ureteric length determinations.

Verification of relationships between anthropometric variables among ureteral stents recipients and ureteric lengths: a challenge for Vitruvian-da Vinci theory

PubMed Central

Acelam, Philip A

2015-01-01

Objective To determine and verify how anthropometric variables correlate to ureteric lengths and how well statistical models approximate the actual ureteric lengths. Materials and methods In this work, 129 charts of endourological patients (71 females and 58 males) were studied retrospectively. Data were gathered from various research centers from North and South America. Continuous data were studied using descriptive statistics. Anthropometric variables (age, body surface area, body weight, obesity, and stature) were utilized as predictors of ureteric lengths. Linear regressions and correlations were used for studying relationships between the predictors and the outcome variables (ureteric lengths); P-value was set at 0.05. To assess how well statistical models were capable of predicting the actual ureteric lengths, percentages (or ratios of matched to mismatched results) were employed. Results The results of the study show that anthropometric variables do not correlate well to ureteric lengths. Statistical models can partially estimate ureteric lengths. Out of the five anthropometric variables studied, three of them: body frame, stature, and weight, each with a P<0.0001, were significant. Two of the variables: age (R2=0.01; P=0.20) and obesity (R2=0.03; P=0.06), were found to be poor estimators of ureteric lengths. None of the predictors reached the expected (match:above:below) ratio of 1:0:0 to qualify as reliable predictors of ureteric lengths. Conclusion There is not sufficient evidence to conclude that anthropometric variables can reliably predict ureteric lengths. These variables appear to lack adequate specificity as they failed to reach the expected (match:above:below) ratio of 1:0:0. Consequently, selections of ureteral stents continue to remain a challenge. However, height (R2=0.68) with the (match:above:below) ratio of 3:3:4 appears suited for use as estimator, but on the basis of decision rule. Additional research is recommended for stent improvements and ureteric length determinations. PMID:26317082

Prognostic value of echocardiographic indices of left atrial morphology and function in dogs with myxomatous mitral valve disease.

PubMed

Baron Toaldo, Marco; Romito, Giovanni; Guglielmini, Carlo; Diana, Alessia; Pelle, Nazzareno G; Contiero, Barbara; Cipone, Mario

2018-05-01

The prognostic relevance of left atrial (LA) morphological and functional variables, including those derived from speckle tracking echocardiography (STE), has been little investigated in veterinary medicine. To assess the prognostic value of several echocardiographic variables, with a focus on LA morphological and functional variables in dogs with myxomatous mitral valve disease (MMVD). One-hundred and fifteen dogs of different breeds with MMVD. Prospective cohort study. Conventional morphologic and echo-Doppler variables, LA areas and volumes, and STE-based LA strain analysis were performed in all dogs. A survival analysis was performed to test for the best echocardiographic predictors of cardiac-related death. Most of the tested variables, including all LA STE-derived variables were univariate predictors of cardiac death in Cox proportional hazard analysis. Because of strong correlation between many variables, only left atrium to aorta ratio (LA/Ao > 1.7), mitral valve E wave velocity (MV E vel > 1.3 m/s), LA maximal volume (LAVmax > 3.53 mL/kg), peak atrial longitudinal strain (PALS < 30%), and contraction strain index (CSI per 1% increase) were entered in the univariate analysis, and all were predictors of cardiac death. However, only the MV E vel (hazard ratio [HR], 4.45; confidence interval [CI], 1.76-11.24; P < .001) and LAVmax (HR, 2.32; CI, 1.10-4.89; P = .024) remained statistically significant in the multivariable analysis. The assessment of LA dimension and function provides useful prognostic information in dogs with MMVD. Considering all the LA variables, LAVmax appears the strongest predictor of cardiac death, being superior to LA/Ao and STE-derived variables. Copyright © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Predicting the admission into medical school of African American college students who have participated in summer academic enrichment programs.

PubMed

Hesser, A; Cregler, L L; Lewis, L

1998-02-01

To identify cognitive and noncognitive variables as predictors of the admission into medical school of African American college students who have participated in summer academic enrichment programs (SAEPs). The study sample comprised 309 African American college students who participated in SAEPs at the Medical College of Georgia School of Medicine from 1980 to 1989 and whose educational and occupational statuses were determined by follow-up tracking. A three-step logistic regression was used to analyze the data (with alpha = .05); the criterion variable was admission to medical school. The 17 predictor variables studied were one of two types, cognitive and noncognitive. The cognitive variables were (1) Scholastic Aptitude Test mathematics (SAT-M) score, (2) SAT verbal score, (3) college grade-point average (GPA), (4) college science GPA, (5) SAEP GPA, and (6) SAEP basic science GPA (BSGPA). The noncognitive variables were (1) gender, (2) highest college level at the time of the last SAEP application, (3) type of college attended (historically African American or predominately white), (4) number of SAEPs attended, (5) career aspiration (physician or another health science option) (6) parents who were professionals, (7) parents who were health care role models, (8) evidence of leadership, (9) evidence of community service, (10) evidence of special motivation, and (11) strength of letter of recommendation in the SAEP application. For each student the rating scores for the last four noncognitive variables were determined by averaging the ratings of two judges who reviewed relevant information in each student's file. In step 1, which explained 20% of the admission decision variance, SAT-M score, SAEP BSGPA, and college GPA were the three significant cognitive predictors identified. In step 2, which explained 31% of the variance, the three cognitive predictors identified in step 1 were joined by three noncognitive predictors: career aspiration, type of college, and number of SAEPs attended. In step 3, which explained 29% of the variance, two cognitive variables (SAT-M score and SAEP BSGPA) and two noncognitive variables (career aspiration and strength of recommendation letter) were identified. The results support the concept of using both cognitive and noncognitive variables when selecting African American students for pre-medical school SAEPs.

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

Steve Verrill; Victoria L. Herian; David W. Green

2002-01-01

Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random...

Investigation of Remedial Education Course Scores as a Predictor of Introduction-Level Course Performances

ERIC Educational Resources Information Center

Ulmer, Ward; Means, Darris R.; Cawthon, Tony W.; Kristensen, Sheryl A.

2016-01-01

This study explores whether performance in remedial English and remedial math is a predictor of success in a college-level introduction English or college-level math class; and whether demographic variables increase the likelihood of remedial English and remedial math as a predictor of success in a college-level introduction English or…

Who Is Retained in School, and When? Survival Analysis of Predictors of Grade Retention in Luxembourgish Secondary School

ERIC Educational Resources Information Center

Klapproth, Florian; Schaltz, Paule

2015-01-01

Based on a large longitudinal sample (N?=?9031) of Luxembourgish secondary school students, this study examined whether variables reflecting the sociodemographic background of the students (gender, nationality and socioeconomic status) as well as the school track proved to be predictors of grade retention. These possible predictors of grade…

Eclipsing cataclysmic variables. Deep eclipses in H0928+501. YY Draconis, the whirling dervish. New x ray pulsar candidates from HEAO-1

NASA Technical Reports Server (NTRS)

Patterson, Joseph

1993-01-01

The status report covering the period from 1 June 1992 to 31 May 1993 is included. Areas of research include: (1) eclipsing cataclysmic variables; (2) deep eclipses in H0928+501; (3) YY Draconis, the Whirling Dervish; and (4) new x ray pulsar candidates from HEAO-1.

Near-infrared Variability in the 2MASS Calibration Fields: A Search for Planetary Transit Candidates

NASA Technical Reports Server (NTRS)

Plavchan, Peter; Jura, M.; Kirkpatrick, J. Davy; Cutri, Roc M.; Gallagher, S. C.

2008-01-01

The Two Micron All Sky Survey (2MASS) photometric calibration observations cover approximately 6 square degrees on the sky in 35 'calibration fields,' each sampled in nominal photometric conditions between 562 and 3692 times during the 4 years of the 2MASS mission. We compile a catalog of variables from the calibration observations to search for M dwarfs transited by extrasolar planets. We present our methods for measuring periodic and nonperiodic flux variability. From 7554 sources with apparent K(sub s) magnitudes between 5.6 and 16.1, we identify 247 variables, including extragalactic variables and 23 periodic variables. We have discovered three M dwarf eclipsing systems, including two candidates for transiting extrasolar planets.

Evaluation of Secretion Prediction Highlights Differing Approaches Needed for Oomycete and Fungal Effectors.

PubMed

Sperschneider, Jana; Williams, Angela H; Hane, James K; Singh, Karam B; Taylor, Jennifer M

2015-01-01

The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogen's advantage. Proteinaceous effectors are synthesized intracellularly and must be externalized to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score) and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localization predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes.

Imperfect physician assistant and physical therapist admissions processes in the United States

PubMed Central

2014-01-01

We compared and contrasted physician assistant and physical therapy profession admissions processes based on the similar number of accredited programs in the United States and the co-existence of many programs in the same school of health professions, because both professions conduct similar centralized application procedures administered by the same organization. Many studies are critical of the fallibility and inadequate scientific rigor of the high-stakes nature of health professions admissions decisions, yet typical admission processes remain very similar. Cognitive variables, most notably undergraduate grade point averages, have been shown to be the best predictors of academic achievement in the health professions. The variability of non-cognitive attributes assessed and the methods used to measure them have come under increasing scrutiny in the literature. The variance in health professions students’ performance in the classroom and on certifying examinations remains unexplained, and cognitive considerations vary considerably between and among programs that describe them. One uncertainty resulting from this review is whether or not desired candidate attributes highly sought after by individual programs are more student-centered or graduate-centered. Based on the findings from the literature, we suggest that student success in the classroom versus the clinic is based on a different set of variables. Given the range of positions and general lack of reliability and validity in studies of non-cognitive admissions attributes, we think that health professions admissions processes remain imperfect works in progress. PMID:24810020

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.

Personality, organizational stress, and attitudes toward work as prospective predictors of professional burnout in hospital nurses

PubMed Central

Hudek-Knežević, Jasna; Kalebić Maglica, Barbara; Krapić, Nada

2011-01-01

Aim To examine to what extent personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness), organizational stress, and attitudes toward work and interactions between personality and either organizational stress or attitudes toward work prospectively predict 3 components of burnout. Methods The study was carried out on 118 hospital nurses. Data were analyzed by a set of hierarchical regression analyses, in which personality traits, measures of organizational stress, and attitudes toward work, as well as interactions between personality and either organizational stress or attitudes toward work were included as predictors, while 3 indices of burnout were measured 4 years later as criteria variables. Results Personality traits proved to be significant but weak prospective predictors of burnout and as a group predicted only reduced professional efficacy (R2 = 0.10), with agreeableness being a single negative predictor. Organizational stress was positive, affective-normative commitment negative predictor, while continuance commitment was not related to any dimension of burnout. We found interactions between neuroticism as well as conscientiousness and organizational stress, measured as role conflict and work overload, on reduced professional efficacy (βNRCWO = -0.30; ßcRCWO = -0.26). We also found interactions between neuroticism and affective normative commitment (β = 0.24) and between openness and continuance commitment on reduced professional efficacy (β = -0.23), as well as interactions between conscientiousness and continuance commitment on exhaustion. Conclusion Although contextual variables were strong prospective predictors and personality traits weak predictors of burnout, the results suggested the importance of the interaction between personality and contextual variables in predicting burnout. PMID:21853549

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

PubMed

Nicodemus, Kristin K; Malley, James D; Strobl, Carolin; Ziegler, Andreas

2010-02-27

Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. In the case when both predictor correlation was present and predictors were associated with the outcome (HA), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under HA and was unbiased under H0. Scaled VIMs were clearly biased under HA and H0. Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

PubMed

Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger

2018-04-19

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

Variability in symptom expression among sexually abused girls: developing multivariate models.

PubMed

Spaccarelli, S; Fuchs, C

1997-03-01

Examined which of several apparent risk variables were predictors of internalizing and externalizing problems in 48 girls who were referred for therapy after disclosing sexual abuse. Specifically, the effects of abuse characteristics, support from nonoffending parents, victims' coping strategies, and victims' cognitive appraisals on symptomatology were assessed. As hypothesized, results indicated that internalizing and externalizing problems were associated with different sets of predictor variables. Victims' self-reports of depression and anxiety were related to lower perceived support from nonoffending parents, more use of cognitive avoidance coping, and more negative appraisals of the abuse. These results were partially replicated when using parent-report measures of depression, but were not replicated for parent reports of victim anxiety. Incest was the only variable that was significantly related to parent-reported anxiety. Parent-reported aggressive behaviors were predicted by level of abuse-related stress; and aggression, social problems, and sexual problems were all related to the tendency to cope by controlling others. Social problems were also related to coping by self-distraction. Regression analyses were done for each dependent variable to examine which predictors accounted for unique variance when controlling for other significant zero-order correlates. Implications of these results for understanding variability in symptom expression among sexual abuse victims are discussed.

School and Neighborhood Predictors of Physical Fitness in Elementary School Students.

PubMed

Kahan, David; McKenzie, Thomas L

2017-06-01

We assessed the associations of 5 school and 7 neighborhood variables with fifth-grade students achieving Healthy Fitness Zone (HFZ) or Needs Improvement-Health Risk (NI-HR) on aerobic capacity (AC) and body composition (BC) physical fitness components of the state-mandated FITNESSGRAM ® physical fitness test. Data for outcome (physical fitness) and predictor (school and neighborhood) variables were extracted from various databases (eg, Data Quest, Walk Score ® ) for 160 schools located in San Diego, California. Predictor variables that were at least moderately correlated (|r| ≥ .30) with ≥1 outcome variables in univariate analyses were retained for ordinary least squares regression analyses. The mean percentages of students achieving HFZ AC (65.7%) and BC (63.5%) were similar (t = 1.13, p = .26), while those for NI-HR zones were significantly different (AC = 6.0% vs BC = 18.6%; t = 12.60, p < .001). Correlations were greater in magnitude for school than neighborhood demographics and stronger for BC than AC. The school variables free/reduced-price lunch (negative) and math achievement (positive) predicted fitness scores. Among neighborhood variables, percent Hispanic predicted failure of meeting the HFZ BC criterion. Creating school and neighborhood environments conducive to promoting physical activity and improving fitness is warranted. © 2017, American School Health Association.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

PubMed

Reid, Colleen E; Jerrett, Michael; Petersen, Maya L; Pfister, Gabriele G; Morefield, Philip E; Tager, Ira B; Raffuse, Sean M; Balmes, John R

2015-03-17

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Examining Epistemological Beliefs of Teacher Candidates According to Various Variables

ERIC Educational Resources Information Center

Aslan, Cengiz

2017-01-01

Purpose: Epistemological beliefs of teachers are important factors on their perceptions of subject area and their classroom practices. This research aims to define epistemological beliefs of teacher candidates and investigates whether or not epistemological beliefs change according to teacher candidates' gender, fields of study, year of study, and…

Investigating Academic Achievements and Critical Thinking Dispositions of Teacher Candidates

ERIC Educational Resources Information Center

Karagöl, Ibrahim; Bekmezci, Sinan

2015-01-01

The aim of this study is to examine the relationship between academic achievements and critical thinking dispositions of teacher candidates in Faculty of Education and to find out whether critical thinking dispositions and academic achievements scores of teacher candidates differ according to different variables. The population consists of the…

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

PubMed

Marill, Keith A

2004-01-01

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

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis.

PubMed

Zador, Zsolt; Sperrin, Matthew; King, Andrew T

2016-01-01

Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance. We analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90-100%. Probabilistic associations were depicted using Bayesian networks. Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138-0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402-0.6641 and pAUCSE = 0.6332, 95% CI: 0.62-0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543-0.679, pAUCSE = 0.6436, 95% CI: 0.6289-0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did. We demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

Investigation of nutritional status using the Mini Nutritional Assessment-Short Form and analysis of the relevant factors in patients with head and neck tumour.

PubMed

Yanagi, Ayaka; Murase, Mai; Sumita, Yuka I; Taniguchi, Hisashi

2017-06-01

The aims of this study were to reveal the nutritional status of patients after head and neck tumour treatment by using the Mini Nutritional Assessment-Short Form (MNA-SF) and to analyse the factors affecting nutritional status in patients with head and neck tumour. Elderly patients with loss of teeth and maxillary/mandibular bone due to head and neck tumour treatment could be at high risk of malnutrition. However, there are few reports on the nutritional status of these patients. Forty-six participants (average age 74.7 years) were selected from patients who visited the maxillofacial prosthetics clinic of Tokyo Medical and Dental University Hospital Faculty of Dentistry in Japan. Nutritional status was evaluated using the MNA-SF. Multiple regression analysis was used to identify predictors affecting MNA-SF score. The candidate explanatory variables were age, sex, maxillofacial prosthesis use, number of residual teeth, resection side, neck dissection and treatment option. The results showed that approximately half of the patients were at risk of malnutrition, and a regression equation for MNA-SF score was developed using two predictors: maxillofacial prosthesis use and neck dissection. Use of a maxillofacial prosthesis can improve nutritional status. On the other hand, a medical history of neck dissection can decrease nutritional status. © 2016 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

Genetic factors contribute to bleeding after cardiac surgery.

PubMed

Welsby, I J; Podgoreanu, M V; Phillips-Bute, B; Mathew, J P; Smith, P K; Newman, M F; Schwinn, D A; Stafford-Smith, M

2005-06-01

Postoperative bleeding remains a common, serious problem for cardiac surgery patients, with striking inter-patient variability poorly explained by clinical, procedural, and biological markers. We tested the hypothesis that genetic polymorphisms of coagulation proteins and platelet glycoproteins are associated with bleeding after cardiac surgery. Seven hundred and eighty patients undergoing aortocoronary surgery with cardiopulmonary bypass were studied. Clinical covariates previously associated with bleeding were recorded and DNA isolated from preoperative blood. Matrix Assisted Laser Desorption/Ionization, Time-Of-Flight (MALDI-TOF) mass spectroscopy or polymerase chain reaction were used for genotype analysis. Multivariable linear regression modeling, including all genetic main effects and two-way gene-gene interactions, related clinical and genetic predictors to bleeding from the thorax and mediastinum. Nineteen candidate polymorphisms were assessed; seven [GPIaIIa-52C>T and 807C>T, GPIb alpha 524C>T, tissue factor-603A>G, prothrombin 20210G>A, tissue factor pathway inhibitor-399C>T, and angiotensin converting enzyme (ACE) deletion/insertion] demonstrate significant association with bleeding (P < 0.01). Adding genetic to clinical predictors results improves the model, doubling overall ability to predict bleeding (P < 0.01). We identified seven genetic polymorphisms associated with bleeding after cardiac surgery. Genetic factors appear primarily independent of, and explain at least as much variation in bleeding as clinical covariates; combining genetic and clinical factors double our ability to predict bleeding after cardiac surgery. Accounting for genotype may be necessary when stratifying risk of bleeding after cardiac surgery.

Tumble Graphs: Avoiding Misleading End Point Extrapolation When Graphing Interactions From a Moderated Multiple Regression Analysis

ERIC Educational Resources Information Center

Bodner, Todd E.

2016-01-01

This article revisits how the end points of plotted line segments should be selected when graphing interactions involving a continuous target predictor variable. Under the standard approach, end points are chosen at ±1 or 2 standard deviations from the target predictor mean. However, when the target predictor and moderator are correlated or the…

The cognitive foundations of reading and arithmetic skills in 7- to 10-year-olds.

PubMed

Durand, Marianne; Hulme, Charles; Larkin, Rebecca; Snowling, Margaret

2005-06-01

A range of possible predictors of arithmetic and reading were assessed in a large sample (N=162) of children between ages 7 years 5 months and 10 years 4 months. A confirmatory factor analysis of the predictors revealed a good fit to a model consisting of four latent variables (verbal ability, nonverbal ability, search speed, and phonological memory) and two manifest variables (digit comparison and phoneme deletion). A path analysis showed that digit comparison and verbal ability were unique predictors of variations in arithmetic skills, whereas phoneme deletion and verbal ability were unique predictors of variations in reading skills. These results confirm earlier findings that phoneme deletion ability appears to be a critical foundation for learning to read (decode). In addition, variations in the speed of accessing numerical quantity information appear to be a critical foundation for the development of arithmetic skills.

Examination of the Mathematical Problem-Solving Beliefs and Success Levels of Primary School Teacher Candidates through the Variables of Mathematical Success and Gender

ERIC Educational Resources Information Center

Bal, Ayten Pinar

2015-01-01

The aim of this study is to examine the mathematical problem-solving beliefs and problem-solving success levels of primary school teacher candidates through the variables of academic success and gender. The research was designed according to the mixed methods technique in which qualitative and quantitative methods are used together. The working…

Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.

X-Ray Flare Candidates in Short Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Margutti, R.; Chincarini, G.; Granot, J.; Guidorzi, C.; Berger, E.; Bernardini, M. G.; Geherls, N.; Soderberg, A. M.; Stamatikos, M.; Zaninoni, E.

2012-01-01

We present the first systematic study of X-ray flare candidates in short gamma-ray bursts (SGRBs) exploiting the large 6-year Swift database with the aim to constrain the physical nature of such fluctuations. We find that flare candidates appear in different types of SGRB host galaxy environments and show no clear correlation with the X-ray afterglow lifetime; flare candidates are detected both in SGRBs with a bright extended emission in the soft gamma-rays and in SGRBs which do not show such component. We furthermore show that SGRB X-ray flare candidates only partially share the set of observational properties of long GRB (LGRB) flares. In particular, the main parameter driving the duration evolution of X-ray variability episodes in both classes is found to be the elapsed time from the explosion, with very limited dependence on the different progenitors, environments, central engine life-times, prompt variability time-scales and energy budgets. On the contrary, SGRB flare candidates significantly differ from LGRB flares in terms of peak luminosity, isotropic energy, flare-to-prompt luminosity ratio and relative variability flux. However, these differences disappear when the central engine time-scales and energy budget are accounted for, suggesting that (i) flare candidates and prompt pulses in SGRBs likely have a common origin; (ii) similar dissipation and/or emission mechanisms are responsible for the prompt and flare emission in long and short GRBs, with SGRBs being less energetic albeit faster evolving versions of the long class. Finally, we show that in strict analogy to the SGRB prompt emission, flares candidates fall off the lag-luminosity relation defined by LGRBs, thus strengthening the SGRB flare-prompt pulse connection.

Dispersal Ability Determines the Role of Environmental, Spatial and Temporal Drivers of Metacommunity Structure

PubMed Central

Padial, André A.; Ceschin, Fernanda; Declerck, Steven A. J.; De Meester, Luc; Bonecker, Cláudia C.; Lansac-Tôha, Fabio A.; Rodrigues, Liliana; Rodrigues, Luzia C.; Train, Sueli; Velho, Luiz F. M.; Bini, Luis M.

2014-01-01

Recently, community ecologists are focusing on the relative importance of local environmental factors and proxies to dispersal limitation to explain spatial variation in community structure. Albeit less explored, temporal processes may also be important in explaining species composition variation in metacommunities occupying dynamic systems. We aimed to evaluate the relative role of environmental, spatial and temporal variables on the metacommunity structure of different organism groups in the Upper Paraná River floodplain (Brazil). We used data on macrophytes, fish, benthic macroinvertebrates, zooplankton, periphyton, and phytoplankton collected in up to 36 habitats during a total of eight sampling campaigns over two years. According to variation partitioning results, the importance of predictors varied among biological groups. Spatial predictors were particularly important for organisms with comparatively lower dispersal ability, such as aquatic macrophytes and fish. On the other hand, environmental predictors were particularly important for organisms with high dispersal ability, such as microalgae, indicating the importance of species sorting processes in shaping the community structure of these organisms. The importance of watercourse distances increased when spatial variables were the main predictors of metacommunity structure. The contribution of temporal predictors was low. Our results emphasize the strength of a trait-based analysis and of better defining spatial variables. More importantly, they supported the view that “all-or- nothing” interpretations on the mechanisms structuring metacommunities are rather the exception than the rule. PMID:25340577

Prediction of first episode of panic attack among white-collar workers.

PubMed

Watanabe, Akira; Nakao, Kazuhisa; Tokuyama, Madoka; Takeda, Masatoshi

2005-04-01

The purpose of the present study was to elucidate a longitudinal matrix of the etiology for first-episode panic attack among white-collar workers. A path model was designed for this purpose. A 5-year, open-cohort study was carried out in a Japanese company. To evaluate the risk factors associated with the onset of a first episode of panic attack, the odds ratios of a new episode of panic attack were calculated by logistic regression. The path model contained five predictor variables: gender difference, overprotection, neuroticism, lifetime history of major depression, and recent stressful life events. The logistic regression analysis indicated that a person with a lifetime history of major depression and recent stressful life events had a fivefold and a threefold higher risk of panic attacks at follow up, respectively. The path model for the prediction of a first episode of panic attack fitted the data well. However, this model presented low accountability for the variance in the ultimate dependent variables, the first episode of panic attack. Three predictors (neuroticism, lifetime history of major depression, and recent stressful life events) had a direct effect on the risk for a first episode of panic attack, whereas gender difference and overprotection had no direct effect. The present model could not fully predict first episodes of panic attack in white-collar workers. To make a path model for the prediction of the first episode of panic attack, other strong predictor variables, which were not surveyed in the present study, are needed. It is suggested that genetic variables are among the other strong predictor variables. A new path model containing genetic variables (e.g. family history etc.) will be needed to predict the first episode of panic attack.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.

Heart Rate Recovery After 6-Min Walk Test Predicts Survival in Patients With Idiopathic Pulmonary Fibrosis

PubMed Central

Swigris, Jeffrey J.; Swick, Jeff; Wamboldt, Frederick S.; Sprunger, David; du Bois, Roland; Fischer, Aryeh; Cosgrove, Gregory P.; Frankel, Stephen K.; Fernandez-Perez, Evans R.; Kervitsky, Dolly; Brown, Kevin K.

2009-01-01

Background: In patients with idiopathic pulmonary fibrosis (IPF), our objectives were to identify predictors of abnormal heart rate recovery (HRR) at 1 min after completion of a 6-min walk test (6MWT) [HRR1] and 2 min after completion of a 6MWT (HRR2), and to determine whether abnormal HRR predicts mortality. Methods: From 2003 to 2008, we identified IPF patients who had been evaluated at our center (n = 76) with a pulmonary physiologic examination and the 6MWT. We used logistic regression to identify predictors of abnormal HRR, the product-limit method to compare survival in the sample stratified on HRR, and Cox proportional hazards analysis to estimate the prognostic capability of abnormal HRR. Results: Cutoff values were 13 beats for abnormal HRR1 and 22 beats for HRR2. In a multivariable model, predictors of abnormal HRR1 were diffusing capacity of the lung for carbon monoxide (odds ratio [OR], 0.4 per 10% predicted; 95% confidence interval [CI], 0.2 to 0.7; p = 0.003), change in heart rate from baseline to maximum (OR, 0.9; 95% CI, 0.8 to 0.97; p = 0.01), and having a right ventricular systolic pressure > 35 mm Hg as determined by transthoracic echocardiogram (OR, 12.7; 95% CI, 2.0 to 79.7; p = 0.01). Subjects with an abnormal HRR had significantly worse survival than subjects with a normal HRR (for HRR1, p = 0.0007 [log-rank test]; for HRR2, p = 0.03 [log-rank test]); these results held for the subgroup of 30 subjects without resting pulmonary hypertension (HRR1, p = 0.04 [log-rank test]). Among several candidate variables, abnormal HRR1 appeared to be the most potent predictor of mortality (hazard ratio, 5.2; 95% CI, 1.8 to 15.2; p = 0.004). Conclusion: Abnormal HRR after 6MWT predicts mortality in IPF patients. Research is needed to confirm these findings prospectively and to examine the mechanisms of HRR in IPF patients. PMID:19395579

A study of the factors affecting advancement and graduation for engineering students

NASA Astrophysics Data System (ADS)

Fletcher, John Thomas

The purpose of this study was, first, to determine whether a set of predictor variables could be identified from pre-enrollment and post-enrollment data that would differentiate students who advance to a major in engineering from non-advancers and, further, to determine if the predictor variables would differentiate students who graduate from the College of Engineering from non-graduates and graduates of other colleges at Auburn University. A second purpose was to determine if the predictor variables would correctly identify male and female students with the same degree of accuracy. The third purpose was to determine if there were significant relationships between the predictor variables studied and grades earned in a set of 15 courses that have enrollments over 100 students and are part of the pre-engineering curriculum. The population for this study was the 868 students who entered the pre-engineering program at Auburn University as freshmen during the Summer and Fall Quarters of 1991. The variables selected to differentiate the different groups were ACT scores, high school grade indices, and first quarter college grade point average. Two sets of classification matrices were developed using analysis and holdout samples that were divided based on sex. With respect to the question about advancement to the professional engineering program, structure coefficients derived from discriminant analysis procedures performed on all the cases combined indicated that first quarter college grade point average, high school math index, ACT math score, and high school science grade index were important predictor variables in classifying students who advanced to the professional engineering program and those who did not. Further, important structure coefficients with respect to graduation with a degree from the College of Engineering were first quarter college grade point average, high school math index, ACT math score, and high school science grade index. The results of this study indicated that significant differences existed in the model's ability to predict advancement and graduation for male and female students. This difference was not unexpected based on the male-dominated population. However, the models identified predicted at a high rate for both male and female students. Finally, many significant relationships were found to exist between the predictor variables and the 15 pre-engineering courses that were selected. The strength of the relationships ranged from a high of .82, p < .001 (Chemistry 103 grade with total high school grade index) to a low of .07, p > .05 (Chemistry 102 with ACT science score).

Predictors of Parent-Teacher Agreement in Youth with Autism Spectrum Disorder and Their Typically Developing Siblings.

PubMed

Stratis, Elizabeth A; Lecavalier, Luc

2017-08-01

This study evaluated the magnitude of informant agreement and predictors of agreement on behavior and emotional problems and autism symptoms in 403 children with autism and their typically developing siblings. Parent-teacher agreement was investigated on the Child Behavior Checklist (CBCL) and Social Responsiveness Scale (SRS). Agreement between parents and teachers fell in the low to moderate range. Multiple demographic and clinical variables were considered as predictors, and only some measures of parent broad autism traits were associated with informant agreement. Parent report on the SRS was a positive predictor of agreement, while teacher report was a negative predictor. Parent report on the CBCL emerged as a positive predictor of agreement, while teacher report emerged as a negative predictor.

Combining climatic and soil properties better predicts covers of Brazilian biomes.

PubMed

Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km 2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Combining climatic and soil properties better predicts covers of Brazilian biomes

NASA Astrophysics Data System (ADS)

Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Insight, rumination, and self-reflection as predictors of well-being.

PubMed

Harrington, Rick; Loffredo, Donald A

2011-01-01

Dispositional private self-focused attention variables such as insight, internal self-awareness (ISA), and self-reflectiveness (SR) have been found to relate to well-being. The present study sought to determine which dispositional private self-focused attention variables have the most predictive power for subjective well-being as measured by the Satisfaction With Life Scale (E. Diener, R. A. Emmons, R. J. Larsen, & S. Griffin, 1985) and for a eudaemonic form of well-being as measured by the Psychological Well-Being Scale (C. D. Ryff, 1989). A total of 121 college student participants completed an online version of the Self-Consciousness Scale-Revised, the Rumination-Reflection Questionnaire, the Self-Reflection and Insight Scale, the Satisfaction With Life Scale, and the Psychological WellBeing Scale. Results of a multivariate regression analysis using the Self-Consciousness Scale-Revised's (M. F. Scheier & C. S. Carver, 1985) subfactors of SR and ISA, the Rumination-Reflection Questionnaire's (P. D. Trapnell & J. D. Campbell, 1999) subscales of Rumination and Reflection, and the Self-Reflection and Insight Scale's (A. M. Grant, J. Franklin, & P. Langford, 2002) Self-Reflection and Insight subscales revealed that the Insight subscale was the only statistically significant predictor (a positive predictor) for all 6 dimensions of psychological well-being. Insight was also the only significant positive predictor for satisfaction with life. The Rumination subscale was a significant negative predictor for 3 dimensions of psychological well-being, and the Reflection subscale was a significant positive predictor for 1 dimension. Implications of dispositional self-awareness variables and their relation to dimensions of well-being are discussed.

Patient expectations and experiences of multiple sclerosis interferon β-1a treatment: a longitudinal, observational study in routine UK clinical practice

PubMed Central

Syed, Mehmood; Rog, David; Parkes, Laura; Shepherd, Gillian L

2014-01-01

Background Premature discontinuation and poor treatment adherence are problems in chronic conditions, such as multiple sclerosis in which patients must take long-term treatment in order to receive maximum benefit from their medication. The Assessing needs In Multiple Sclerosis (AIMS) study explored factors related to premature treatment discontinuation and patients’ experiences of subcutaneous (sc) interferon (IFN) β-1a treatment in the UK. Methods A questionnaire-based survey was integrated into the Bupa Home Healthcare patient-support program, which delivers sc IFN β-1a to patients in their home. Data were collected via patient questionnaires incorporated into routine clinical care and administered upon registration of a new patient by the coordinator, following initial delivery of treatment, prior to each delivery during therapy and at the end of treatment. Univariate and multivariate analyses were performed to identify factors associated with premature discontinuation. Results Data were collected from 2,390 patients (1,267 new; 1,123 existing) from 59 UK prescribing centers (November 2006–April 2011). Following the first delivery of sc IFN β-1a, 94% (1,149/1,225) of patients had received training, and 73% (818/1,120) reported that they had no concerns. In total, 24% of new patients discontinued therapy by the end of the study. In the univariate model, none of the candidate variables tested were significant predictors of treatment discontinuation. The strongest predictors of discontinuation in multivariate analyses were lack of information prior to starting treatment and patients feeling unwell on treatment and geographic region (P<0.05 for each variable). Conclusion This study suggests that patients feeling well on treatment and provision of high-quality information are the main determinants of persistence with sc IFN β-1a therapy. A package of care that targets these issues should therefore be considered when initiating sc IFN β-1a therapy. PMID:24570582

Multivariate analyses of tinnitus complaint and change in tinnitus complaint: a masker study.

PubMed

Jakes, S; Stephens, S D

1987-11-01

Multivariate statistical techniques were used to re-analyse the data from the recent DHSS multi-centre masker study. These analyses were undertaken to three ends. First, to clarify and attempt to replicate the previously found factor structure of complaints about tinnitus. Secondly, to attempt to identify common factors in the change or improvement measures pre- and post-masker treatment. Thirdly, to identify predictors of any such outcome factors. Two complaint factors were identified; 'Distress' and 'intrusiveness'. A series of analyses were conducted on change measures using different numbers of subjects and variables. When only semantic differential scales were used, the change factors were very similar to the complaint factors noted above. When variables measuring other aspects of improvement were included, several other factors were identified. These included; 'tinnitus helped', 'masking effects', 'residual inhibition' and 'matched loudness'. Twenty-five conceptually distinct predictors of outcome were identified. These predictor variables were quite different for different outcome factors. For example, high-frequency hearing loss was a predictor of tinnitus being helped by the masker, and a low frequency match and a low masking threshold predicted therapeutic success on residual inhibition. Decrease in matched loudness was predicted by louder tinnitus initially.

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

Student performance on levels 1 and 2-CE of COMLEX-USA: do elective upper-level undergraduate science courses matter?

PubMed

Wong, Stanley K; Ramirez, Juan R; Helf, Scott C

2009-11-01

The effect of a variety of preadmission variables, including the number of elective preadmission upper-level science courses, on academic achievement is not well established. To investigate the relationship between number of preadmission variables and overall student academic achievement in osteopathic medical school. Academic records of osteopathic medical students in the 2008 and 2009 graduating classes of Western University of Health Sciences College of Osteopathic Medicine of the Pacific in Pomona, California, were analyzed. Multivariate linear regression analyses were performed to identify predictors of academic achievement based on Medical College Admission Test (MCAT) subscores, undergraduate grade point average (GPA), GPA in medical school basic science (preclinical GPA) and clinical clerkship (clinical GPA), and scores on the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Cognitive Evaluation (CE). Records of 358 osteopathic medical students were evaluated. Analysis of beta coefficients suggested that undergraduate science GPA was the most important predictor of overall student academic achievement (P<.01). Biological sciences MCAT subscore was a more modest but still statistically significant predictor of preclinical GPA and COMLEX-USA Level 1 score (P<.01). Physical sciences MCAT subscore was also a statistically significant predictor of preclinical GPA, and verbal reasoning MCAT subscore was a statistically significant predictor of COMLEX-USA Level 2-CE score (both P<.01). Women had statistically significantly higher preclinical GPA and COMLEX-USA Level 2-CE scores than men (P<.05). Differences in some outcome variables were also associated with racial-ethnic background and age. Number of preadmission elective upper-level science courses taken by students before matriculation was not significantly correlated with any academic achievement variable. Although undergraduate science GPA and MCAT biological sciences subscore were significant predictors of overall academic achievement for osteopathic medical students, the number of elective upper-level science courses taken preadmission had no predictive value.

Predictors of relapse in patients with major depressive disorder in a 52-week, fixed dose, double blind, randomized trial of selegiline transdermal system (STS).

PubMed

Jang, Saeheon; Jung, Sungwon; Pae, Chiun; Kimberly, Blanchard Portland; Craig Nelson, J; Patkar, Ashwin A

2013-12-01

We investigated patient and disease characteristics predictive of relapse of MDD during a 52-week placebo controlled trial of selegiline transdermal system (STS) to identify patient characteristics relevant for STS treatment. After 10 weeks of open-label stabilization with STS, 322 remitted patients with MDD were randomized to 52-weeks of double-blind treatment with STS (6 mg/24h) or placebo (PLB). Relapse was defined as Hamilton Depression Rating Scale (HAMD-17) score of ≥ 14 and a CGI-S score of ≥ 3 with at least 2-point increase from the beginning of the double blind phase on 2 consecutive visits. Cox's proportional hazards regression was used to examine the effect of potential predictors (age, sex, age at onset of first MDD, early response pattern, number of previous antidepressant trials, severity of index episode, number of previous episodes, melancholic features, atypical features and anxious feature) on outcome. Exploratory analyses examined additional clinical variables (medical history, other psychiatric history, and individual items of HAM-D 28) on relapse. For all predictor variables analyzed, treatment Hazard Ratio (HR=0.48~0.54) was significantly in favor of STS (i.e., lower relapse risk than PLB). Age of onset was significantly predictive of relapse. Type, duration, and severity of depressive episodes, previous antidepressant trials, or demographic variables did not predict relapse. In additional exploratory analysis, eating disorder history and suicidal ideation were significant predictors of relapse after controlling for the effect of treatment in individual predictor analysis. While age of onset, eating disorder history and suicidal ideation were significant predictors, the majority of clinical and demographic variables were not predictive of relapse. Given the post-hoc nature of analysis, the findings need confirmation from a prospective study. It appears that selegiline transdermal system was broadly effective in preventing relapse across different subtypes and symptoms clusters of MDD. © 2013 Published by Elsevier B.V.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

PubMed

Yu, Su Jong; Kim, Hyunsoo; Min, Hophil; Sohn, Areum; Cho, Young Youn; Yoo, Jeong-Ju; Lee, Dong Hyeon; Cho, Eun Ju; Lee, Jeong-Hoon; Gim, Jungsoo; Park, Taesung; Kim, Yoon Jun; Kim, Chung Yong; Yoon, Jung-Hwan; Kim, Youngsoo

2017-03-03

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

One-minute heart rate recovery after cycloergometer exercise testing as a predictor of mortality in a large cohort of exercise test candidates: substantial differences with the treadmill-derived parameter.

PubMed

Gaibazzi, Nicola; Petrucci, Nicola; Ziacchi, Vigilio

2004-03-01

Previous work showed a strong inverse association between 1-min heart rate recovery (HRR) after exercising on a treadmill and all-cause mortality. The aim of this study was to determine whether the results could be replicated in a wide population of real-world exercise ECG candidates in our center, using a standard bicycle exercise test. Between 1991 and 1997, 1420 consecutive patients underwent ECG exercise testing performed according to our standard cycloergometer protocol. Three pre-specified cut-point values of 1-min HRR, derived from previous studies in the medical literature, were tested to see whether they could identify a higher-risk group for all-cause mortality; furthermore, we tested the possible association between 1-min HRR as a continuous variable and mortality using logistic regression. Both methods showed a lack of a statistically significant association between 1-min HRR and all-cause mortality. A weak trend toward an inverse association, although not statistically significant, could not be excluded. We could not validate the clear-cut results from some previous studies performed using the treadmill exercise test. The results in our study may only "not exclude" a mild inverse association between 1-min HRR measured after cycloergometer exercise testing and all-cause mortality. The 1-min HRR measured after cycloergometer exercise testing was not clinically useful as a prognostic marker.

Lung function and left ventricular hypertrophy in morbidly obese candidates for bariatric surgery

PubMed Central

Müller, Paulo de Tarso; Domingos, Hamilton; Patusco, Luiz Armando Pereira; Rapello, Gabriel Victor Guimarães

2015-01-01

Objective: To look for correlations between lung function and cardiac dimension variables in morbidly obese patients, in order to test the hypothesis that the relative size of the small airways is independently correlated with left ventricular hypertrophy. Methods: This was a retrospective study involving 192 medical records containing a clinical protocol employed in candidates for bariatric surgery between January of 2006 and December of 2010. Results: Of the 192 patients evaluated, 39 (10 males and 29 females) met the inclusion criteria. The mean BMI of the patients was 49.2 ± 7.6 kg/m2, and the mean age was 35.5 ± 7.7 years. The FEF25-75/FVC, % correlated significantly with left ventricular posterior wall thickness and relative left ventricular posterior wall thickness, those correlations remaining statistically significant (r = −0.355 and r = −0.349, respectively) after adjustment for weight, gender, and history of systemic arterial hypertension. Stepwise multivariate linear regression analysis showed that FVC and FEV1 were the major determinants of left ventricular mass (in grams or indexed to body surface area). Conclusions: A reduction in the relative size of the small airways appears to be independently correlated with obesity-related cardiac hypertrophy, regardless of factors affecting respiratory mechanics (BMI and weight), gender, or history of systemic arterial hypertension. However, FEV1 and FVC might be important predictors of left ventricular mass in morbidly obese individuals. PMID:26578134

Selecting AGN through Variability in SN Datasets

NASA Astrophysics Data System (ADS)

Boutsia, K.; Leibundgut, B.; Trevese, D.; Vagnetti, F.

2010-07-01

Variability is a main property of Active Galactic Nuclei (AGN) and it was adopted as a selection criterion using multi epoch surveys conducted for the detection of supernovae (SNe). We have used two SN datasets. First we selected the AXAF field of the STRESS project, centered in the Chandra Deep Field South where, besides the deep X-ray surveys also various optical catalogs exist. Our method yielded 132 variable AGN candidates. We then extended our method including the dataset of the ESSENCE project that has been active for 6 years, producing high quality light curves in the R and I bands. We obtained a sample of ˜4800 variable sources, down to R=22, in the whole 12 deg2 ESSENCE field. Among them, a subsample of ˜500 high priority AGN candidates was created using as secondary criterion the shape of the structure function. In a pilot spectroscopic run we have confirmed the AGN nature for nearly all of our candidates.

Preformulation considerations for controlled release dosage forms. Part III. Candidate form selection using numerical weighting and scoring.

PubMed

Chrzanowski, Frank

2008-01-01

Two numerical methods, Decision Analysis (DA) and Potential Problem Analysis (PPA) are presented as alternative selection methods to the logical method presented in Part I. In DA properties are weighted and outcomes are scored. The weighted scores for each candidate are totaled and final selection is based on the totals. Higher scores indicate better candidates. In PPA potential problems are assigned a seriousness factor and test outcomes are used to define the probability of occurrence. The seriousness-probability products are totaled and forms with minimal scores are preferred. DA and PPA have never been compared to the logical-elimination method. Additional data were available for two forms of McN-5707 to provide complete preformulation data for five candidate forms. Weight and seriousness factors (independent variables) were obtained from a survey of experienced formulators. Scores and probabilities (dependent variables) were provided independently by Preformulation. The rankings of the five candidate forms, best to worst, were similar for all three methods. These results validate the applicability of DA and PPA for candidate form selection. DA and PPA are particularly applicable in cases where there are many candidate forms and where each form has some degree of unfavorable properties.

Insecticide treated bednet strategy in rural settings: can we exploit women's decision making power?

PubMed

Tilak, Rina; Tilak, V W; Bhalwar, R

2007-01-01

Use of insecticide treated bednets in prevention of malaria is a widely propagated global strategy, however, its use has been reported to be influenced and limited by many variables especially gender bias. A cross sectional field epidemiological study was conducted in a rural setting with two outcome variables, 'Bednet use'(primary outcome variable) and 'Women's Decision Making Power' which were studied in reference to various predictor variables. Analysis reveals a significant effect on the primary outcome variable 'Bednet use' of the predictor variables- age, occupation, bednet purchase decision, women's decision making power, husband's education and knowledge about malaria and its prevention. The study recommends IEC on treated bednets to be disseminated through TV targeting the elderly women who have better decision making power and mobilizing younger women who were found to prefer bednets for prevention of mosquito bites for optimizing the use of treated bednets in similar settings.

Use of generalized regression tree models to characterize vegetation favoring Anopheles albimanus breeding.

PubMed

Hernandez, J E; Epstein, L D; Rodriguez, M H; Rodriguez, A D; Rejmankova, E; Roberts, D R

1997-03-01

We propose the use of generalized tree models (GTMs) to analyze data from entomological field studies. Generalized tree models can be used to characterize environments with different mosquito breeding capacity. A GTM simultaneously analyzes a set of predictor variables (e.g., vegetation coverage) in relation to a response variable (e.g., counts of Anopheles albimanus larvae), and how it varies with respect to a set of criterion variables (e.g., presence of predators). The algorithm produces a treelike graphical display with its root at the top and 2 branches stemming down from each node. At each node, conditions on the value of predictors partition the observations into subgroups (environments) in which the relation between response and criterion variables is most homogeneous.

Candidate Teachers? Views on Implementation and Adoption of Democracy: Istanbul University Sample

ERIC Educational Resources Information Center

Serin, Hüseyin

2017-01-01

The aim of this study is to determine the candidate teachers' views, who have pedagogical proficiency at Hasan Ali Yucel education faculty, on implementation of organizational democracy according to gender and education variable. 370 of the candidate teachers who have graduate degree and continue undergraduate study at Istanbul University…

Variables that Affect Math Teacher Candidates' Intentions to Integrate Computer-Assisted Mathematics Education (CAME)

ERIC Educational Resources Information Center

Erdogan, Ahmet

2010-01-01

Based on Social Cognitive Carier Theory (SCCT) (Lent, Brown, & Hackett, 1994, 2002), this study tested the effects of mathematics teacher candidates' self-efficacy in, outcome expectations from, and interest in CAME on their intentions to integrate Computer-Assisted Mathematics Education (CAME). While mathematics teacher candidates' outcome…

Supermassive Black Hole Binary Candidates from the Pan-STARRS1 Medium Deep Survey

NASA Astrophysics Data System (ADS)

Liu, Tingting; Gezari, Suvi

2018-01-01

Supermassive black hole binaries (SMBHBs) should be a common product of the hierarchal growth of galaxies and gravitational wave sources at nano-Hz frequencies. We have performed a systematic search in the Pan-STARRS1 Medium Deep Survey for periodically varying quasars, which are predicted manifestations of SMBHBs, and identified 26 candidates that are periodically varying on the timescale of ~300-1000 days over the 4-year baseline of MDS. We continue to monitor them with the Discovery Channel Telescope and the LCO network telescopes and thus are able to extend the baseline to 3-8 cycles and break false positive signals due to stochastic, normal quasar variability. From our imaging campaign, five candidates show persistent periodic variability and remain strong SMBHB candidates for follow-up observations. We calculate the cumulative number rate of SMBHBs and compare with previous work. We also compare the gravitational wave strain amplitudes of the candidates with the capability of pulsar timing arrays and discuss the future capabilities to detect periodic quasar and SMBHB candidates with the Large Synoptic Survey Telescope.

Dreams Fulfilled and Shattered: Determinants of Segmented Assimilation in the Second Generation*

PubMed Central

Haller, William; Portes, Alejandro; Lynch, Scott M.

2013-01-01

We summarize prior theories on the adaptation process of the contemporary immigrant second generation as a prelude to presenting additive and interactive models showing the impact of family variables, school contexts and academic outcomes on the process. For this purpose, we regress indicators of educational and occupational achievement in early adulthood on predictors measured three and six years earlier. The Children of Immigrants Longitudinal Study (CILS), used for the analysis, allows us to establish a clear temporal order among exogenous predictors and the two dependent variables. We also construct a Downward Assimilation Index (DAI), based on six indicators and regress it on the same set of predictors. Results confirm a pattern of segmented assimilation in the second generation, with a significant proportion of the sample experiencing downward assimilation. Predictors of the latter are the obverse of those of educational and occupational achievement. Significant interaction effects emerge between these predictors and early school contexts, defined by different class and racial compositions. Implications of these results for theory and policy are examined. PMID:24223437

A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer

PubMed Central

2010-01-01

Introduction Various multigene predictors of breast cancer clinical outcome have been commercialized, but proved to be prognostic only for hormone receptor (HR) subsets overexpressing estrogen or progesterone receptors. Hormone receptor negative (HRneg) breast cancers, particularly those lacking HER2/ErbB2 overexpression and known as triple-negative (Tneg) cases, are heterogeneous and generally aggressive breast cancer subsets in need of prognostic subclassification, since most early stage HRneg and Tneg breast cancer patients are cured with conservative treatment yet invariably receive aggressive adjuvant chemotherapy. Methods An unbiased search for genes predictive of distant metastatic relapse was undertaken using a training cohort of 199 node-negative, adjuvant treatment naïve HRneg (including 154 Tneg) breast cancer cases curated from three public microarray datasets. Prognostic gene candidates were subsequently validated using a different cohort of 75 node-negative, adjuvant naïve HRneg cases curated from three additional datasets. The HRneg/Tneg gene signature was prognostically compared with eight other previously reported gene signatures, and evaluated for cancer network associations by two commercial pathway analysis programs. Results A novel set of 14 prognostic gene candidates was identified as outcome predictors: CXCL13, CLIC5, RGS4, RPS28, RFX7, EXOC7, HAPLN1, ZNF3, SSX3, HRBL, PRRG3, ABO, PRTN3, MATN1. A composite HRneg/Tneg gene signature index proved more accurate than any individual candidate gene or other reported multigene predictors in identifying cases likely to remain free of metastatic relapse. Significant positive correlations between the HRneg/Tneg index and three independent immune-related signatures (STAT1, IFN, and IR) were observed, as were consistent negative associations between the three immune-related signatures and five other proliferation module-containing signatures (MS-14, ONCO-RS, GGI, CSR/wound and NKI-70). Network analysis identified 8 genes within the HRneg/Tneg signature as being functionally linked to immune/inflammatory chemokine regulation. Conclusions A multigene HRneg/Tneg signature linked to immune/inflammatory cytokine regulation was identified from pooled expression microarray data and shown to be superior to other reported gene signatures in predicting the metastatic outcome of early stage and conservatively managed HRneg and Tneg breast cancer. Further validation of this prognostic signature may lead to new therapeutic insights and spare many newly diagnosed breast cancer patients the need for aggressive adjuvant chemotherapy. PMID:20946665

Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: A bicenter study from north of Iran

PubMed Central

Salehpoor, Ghasem; Rezaei, Sajjad; Hosseininezhad, Mozaffar

2014-01-01

Background: Although studies have demonstrated significant negative relationships between quality of life (QOL), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on QOL is in the relative importance of these predictors. Also, there is lack of adequate knowledge about the actual contribution of each of them in the prediction of QOL dimensions. Thus, the main objective of this study is to assess the role of fatigue, depression, anxiety, and stress in relation to QOL of multiple sclerosis (MS) patients. Materials and Methods: One hundred and sixty-two MS patients completed the questionnaire on demographic variables, and then they were evaluated by the Persian versions of Short-Form Health Survey Questionnaire (SF-36), Fatigue Survey Scale (FSS), and Depression, Anxiety, Stress Scale-21 (DASS-21). Data were analyzed by Pearson correlation coefficient and hierarchical regression. Results: Correlation analysis showed a significant relationship between QOL elements in SF-36 (physical component summary and mental component summary) and depression, fatigue, stress, and anxiety (P < 0.01). Hierarchical regression analysis indicated that among the predictor variables in the final step, fatigue, depression, and anxiety were identified as the physical component summary predictor variables. Anxiety was found to be the most powerful predictor variable amongst all (β = −0.46, P < 0.001). Furthermore, results have shown depression as the only significant mental component summary predictor variable (β = −0.39, P < 0.001). Conclusions: This study has highlighted the role of anxiety, fatigue, and depression in physical dimensions and the role of depression in psychological dimensions of the lives of MS patients. In addition, the findings of this study indirectly suggest that psychological interventions for reducing fatigue, depression, and anxiety can lead to improved QOL of MS patients. PMID:25558256

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

PubMed Central

Galvão, Franklin; Villalobos, Fabricio; De Marco Júnior, Paulo

2017-01-01

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist. PMID:29049298

Prediction of Postoperative Clinical Recovery of Drop Foot Attributable to Lumbar Degenerative Diseases, via a Bayesian Network.

PubMed

Takenaka, Shota; Aono, Hiroyuki

2017-03-01

Drop foot resulting from degenerative lumbar diseases can impair activities of daily living. Therefore, predictors of recovery of this symptom have been investigated using univariate or/and multivariate analyses. However, the conclusions have been somewhat controversial. Bayesian network models, which are graphic and intuitive to the clinician, may facilitate understanding of the prognosis of drop foot resulting from degenerative lumbar diseases. (1) To show a layered correlation among predictors of recovery from drop foot resulting from degenerative lumbar diseases; and (2) to develop support tools for clinical decisions to treat drop foot resulting from lumbar degenerative diseases. Between 1993 and 2013, we treated 141 patients with decompressive lumbar spine surgery who presented with drop foot attributable to degenerative diseases. Of those, 102 (72%) were included in this retrospective study because they had drop foot of recent development and had no diseases develop that affect evaluation of drop foot after surgery. Specifically, 28 (20%) patients could not be analyzed because their records were not available at a minimum of 2 years followup after surgery and 11 (8%) were lost owing to postoperative conditions that affect the muscle strength evaluation. Eight candidate variables were sex, age, herniated soft disc, duration of the neurologic injury (duration), preoperative tibialis anterior muscle strength (pretibialis anterior), leg pain, cauda equina syndrome, and number of involved levels. Manual muscle testing was used to assess the tibialis anterior muscle strength. Drop foot was defined as a tibialis anterior muscle strength score of less than 3 of 5 (5 = movement against gravity and full resistance, 4 = movement against gravity and moderate resistance, 3 = movement against gravity through full ROM, 3- = movement against gravity through partial ROM, 2 = movement with gravity eliminated through full ROM, 1 = slight contraction but no movement, and 0 = no contraction). The two outcomes of interest were postoperative tibialis anterior muscle strength (posttibialis anterior) of 3 or greater and posttibialis anterior strength of 4 or greater at 2 years after surgery. We developed two separate Bayesian network models with outcomes of interest for posttibialis anterior strength of 3 or greater and posttibialis anterior strength of 4 or greater. The two outcomes correspond to "good" and "excellent" results based on previous reports, respectively. Direct predictors are defined as variables that have the tail of the arrow connecting the outcome of interest, whereas indirect predictors are defined as variables that have the tail of the arrow connecting either direct predictors or other indirect predictors that have the tail of the arrow connecting direct predictors. Sevenfold cross validation and receiver-operating characteristic (ROC) curve analyses were performed to evaluate the accuracy and robustness of the Bayesian network models. Both of our Bayesian network models showed that weaker muscle power before surgery (pretibialis anterior ≤ 1) and longer duration of neurologic injury before treatment (> 30 days) were associated with a decreased likelihood of return of function by 2 years. The models for posttibialis anterior muscle strength of 3 or greater and posttibialis anterior muscle strength of 4 or greater were the same in terms of the graphs, showing that the two direct predictors were pretibialis anterior muscle strength (score ≤ 1 or ≥ 2) and duration (≤ 30 days or > 30 days). Age, herniated soft disc, and leg pain were identified as indirect predictors. We developed a decision-support tool in which the clinician can enter pretibialis anterior muscle strength and duration, and from this obtain the probability estimates of posttibialis anterior muscle strength. The probability estimates of posttibialis anterior muscle strength of 3 or greater and posttibialis anterior muscle strength of 4 or greater were 94% and 85%, respectively, in the most-favorable conditions (pretibialis anterior ≥ 2; duration ≤ 30 days) and 18% and 14%, respectively, in the least-favorable conditions (pretibialis anterior ≤ 1; duration > 30 days). On the sevenfold cross validation, the area under the ROC curve yielded means of 0.78 (95% CI, 0.68-0.87) and 0.74 (95% CI, 0.64-0.84) for posttibialis anterior muscle strength of 3 or greater and posttibialis anterior muscle strength of 4 or greater, respectively. The results of this study suggest that the clinician can understand intuitively the layered correlation among predictors by Bayesian network models. Based on the models, the decision-support tool successfully provided the probability estimates of posttibialis anterior muscle strength to treat drop foot attributable to lumbar degenerative diseases. These models were shown to be robust on the internal validation but should be externally validated in other populations. Level III, therapeutic study.

Predictors and moderators of response to cognitive behavioral therapy and medication for the treatment of binge eating disorder.

PubMed

Grilo, Carlos M; Masheb, Robin M; Crosby, Ross D

2012-10-01

To examine predictors and moderators of response to cognitive behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout, and posttreatment. Demographic factors, psychiatric and personality disorder comorbidity, eating disorder psychopathology, psychological features, and 2 subtyping methods (negative affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge eating and 4 secondary dimensional outcomes (binge-eating frequency, eating disorder psychopathology, depression, and body mass index). Mixed-effects models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication only (younger participants had greater binge-eating reductions), whereas several demographic and clinical variables (lower self-esteem, negative affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with vs. 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication only (10% vs. 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: Participants with overvaluation had significantly greater reductions in eating disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative affect. Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013

PubMed Central

Robles, Brenda; Kuo, Tony

2017-01-01

Background Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Methods Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). Results 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. Conclusions These findings have implications for public health practice and community planning in local health jurisdictions. PMID:28087545

[Cost analysis of radiotherapy provided in inpatient setting -  testing potential predictors for a new prospective payment system].

PubMed

Sedo, J; Bláha, M; Pavlík, T; Klika, P; Dušek, L; Büchler, T; Abrahámová, J; Srámek, V; Slampa, P; Komínek, L; Pospíšil, P; Sláma, O; Vyzula, R

2014-01-01

As a part of the development of a new prospective payment model for radiotherapy we analyzed data on costs of care provided by three comprehensive cancer centers in the Czech Republic. Our aim was to find a combination of variables (predictors) which could be used to sort hospitalization cases into groups according to their costs, with each group having the same reimbursement rate. We tested four variables as possible predictors -  number of fractions, stage of disease, radiotherapy technique and diagnostic group. We analyzed 7,440 hospitalization cases treated in three comprehensive cancer centers from 2007 to 2011. We acquired data from the I COP database developed by Institute of Biostatistics and Analyses of Masaryk University in cooperation with oncology centers that contains records from the National Oncological Registry along with data supplied by healthcare providers to insurance companies for the purpose of retrospective reimbursement. When comparing the four variables mentioned above we found that number of fractions and radiotherapy technique were much stronger predictors than the other two variables. Stage of disease did not prove to be a relevant indicator of cost distinction. There were significant differences in costs among diagnostic groups but these were mostly driven by the technique of radiotherapy and the number of fractions. Within the diagnostic groups, the distribution of costs was too heterogeneous for the purpose of the new payment model. The combination of number of fractions and radiotherapy technique appears to be the most appropriate cost predictors to be involved in the prospective payment model proposal. Further analysis is planned to test the predictive value of intention of radiotherapy in order to determine differences in costs between palliative and curative treatment.

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013.

PubMed

Robles, Brenda; Kuo, Tony

2017-01-13

Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. These findings have implications for public health practice and community planning in local health jurisdictions. 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/.

Prediction of employer-employee relationships from sociodemographic variables and social values in Brunei public and private sector workers.

PubMed

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer-employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer-employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer-employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer-employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer-employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer-employee relationship problems. Other significant social value correlates, but not predictors of employer-employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer-employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions.

Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

PubMed Central

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer–employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions. PMID:28769597

Predictors and Moderators of Response to Cognitive Behavioral Therapy and Medication for the Treatment of Binge Eating Disorder

PubMed Central

Grilo, Carlos. M.; Masheb, Robin M.; Crosby, Ross D.

2012-01-01

Objective To examine predictors and moderators of response to cognitive-behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). Method 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout-, and post-treatment. Demographic factors, psychiatric and personality-disorder co-morbidity, eating-disorder psychopathology, psychological features, and two sub-typing methods (negative-affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge-eating and four secondary dimensional outcomes (binge-eating frequency, eating-disorder psychopathology, depression, and body mass index). Mixed-effects-models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Results Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication-only (younger participants had greater binge-eating reductions) whereas several demographic and clinical variables (lower self-esteem, negative-affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with versus 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication-only (10% versus 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: participants with overvaluation had significantly greater reductions in eating-disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative-affect. Conclusions Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. PMID:22289130

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis.

PubMed

Vucicevic, Jelica; Nikolic, Katarina; Dobričić, Vladimir; Agbaba, Danica

2015-02-20

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood-Brain Barrier (BBB) permeability (P(e)) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH = 7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

INDIVIDUO: Results from a patient-centered lifestyle intervention for obesity surgery candidates.

PubMed

Camolas, José; Santos, Osvaldo; Moreira, Pedro; do Carmo, Isabel

Preoperative nutritional counseling provides an opportunity to ameliorate patients' clinical condition and build-up adequate habits and perception of competence. Study aimed to evaluate: (a) the effect of INDIVIDUO on weight and metabolic control; (b) the impact of INDIVIDUO on psychosocial variables associated with successful weight-control. Two-arms randomised controlled single-site study, with six-month duration. Patients were recruited from an Obesity Treatment Unit's waiting list. For the intervention group (IG), an operating procedure manual was used, nutritionists received training/supervision regarding INDIVIDUO's procedures. Control group (CG) received health literacy-promoting intervention. Intention-to-treat and per-control analysis were used. Outcomes included weight, metabolic control variables (blood pressure, glycemia, insulinemia, triglycerides, cholesterol), measures of eating and physical activity patterns, hedonic hunger, autonomous/controlled regulation, perceived competence for diet (PCS-diet) and quality of life. Primary outcomes were weight and metabolic control. Effect size was estimated by odds ratio and Cohens'd coefficient. Overall, 94 patients participated (IG:45; CG:49) and 60 completed the study (IG:29; CG:31). Intervention patients lost an excess 9.68% body weight (%EWL), vs. 0.51% for CG. Adjusting for age and baseline BMI, allocation group remained an independent predictor of %EWL (B=8.43, 95%CI: 2.79-14.06). IG had a six-fold higher probability (OR: 6.35, 95%CI: 1.28-31.56) of having adequate/controlled fasting glycemia at final evaluation. PCS-diet at final evaluation was independently predicted by baseline PCS-diet (B=0.31, 95%CI: 0.06-0.64), variation in autonomous regulation (B=0.43, 95%CI: 0.15-0.71) and allocation group (B=0.26, 95%CI: 0.04-1.36). Results on weight and metabolic control support INDIVIDUO as a valuable clinical tool for obesity surgery candidates counseling. Additionally, intervention associated with perceived competence for weight-control behaviours and autonomous regulation. Copyright © 2016 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

X-ray spectral variability of Seyfert 2 galaxies

NASA Astrophysics Data System (ADS)

Hernández-García, L.; Masegosa, J.; González-Martín, O.; Márquez, I.

2015-07-01

Context. Variability across the electromagnetic spectrum is a property of active galactic nuclei (AGN) that can help constrain the physical properties of these galaxies. Nonetheless, the way in which the changes happen and whether they occur in the same way in every AGN are still open questions. Aims: This is the third in a series of papers with the aim of studying the X-ray variability of different families of AGN. The main purpose of this work is to investigate the variability pattern(s) in a sample of optically selected Seyfert 2 galaxies. Methods: We use the 26 Seyfert 2s in the Véron-Cetty and Véron catalog with data available from Chandra and/or XMM-Newton public archives at different epochs, with timescales ranging from a few hours to years. All the spectra of the same source were simultaneously fitted, and we let different parameters vary in the model. Whenever possible, short-term variations from the analysis of the light curves and/or long-term UV flux variations were studied. We divided the sample into Compton-thick and Compton-thin candidates to account for the degree of obscuration. When transitions between Compton-thick and thin were obtained for different observations of the same source, we classified it as a changing-look candidate. Results: Short-term variability at X-rays was studied in ten cases, but variations are not found. From the 25 analyzed sources, 11 show long-term variations. Eight (out of 11) are Compton-thin, one (out of 12) is Compton-thick, and the two changing-look candidates are also variable. The main driver for the X-ray changes is related to the nuclear power (nine cases), while variations at soft energies or related to absorbers at hard X-rays are less common, and in many cases these variations are accompanied by variations in the nuclear continuum. At UV frequencies, only NGC 5194 (out of six sources) is variable, but the changes are not related to the nucleus. We report two changing-look candidates, MARK 273 and NGC 7319. Conclusions: A constant reflection component located far away from the nucleus plus a variable nuclear continuum are able to explain most of our results. Within this scenario, the Compton-thick candidates are dominated by reflection, which suppresses their continuum, making them seem fainter, and they do not show variations (except MARK 3), while the Compton-thin and changing-look candidates do. Appendices are available in electronic form at http://www.aanda.org

The Prediction of Doctorate Attainment in Psychology, Mathematics and Chemistry: Preliminary Report.

ERIC Educational Resources Information Center

Educational Testing Service, Princeton, NJ.

Data from the National Science Foundation Fellowship applicant records and the NRC Office of Scientific Personnel Doctorate Records File were utilized to evaluate the potential of GRE Aptitude and Advanced Tests as predictors of whether or not the candidate attained the doctorate within a period of from seven to ten years. In addition, the study…

Examining Students' Perception of Classroom Openness as a Predictor of Civic Knowledge: A Cross-National Analysis of 38 Countries

ERIC Educational Resources Information Center

Lin, Alex Romeo

2014-01-01

Civic knowledge is critical to interpreting various policy and candidate issues that are necessary to participating in certain political activities, such as voting in elections or attending public demonstrations. Various studies have examined students' perceptions of classroom openness, which reflects perceived levels of political discussion…

Attitudes to Technology, Perceived Computer Self-Efficacy and Computer Anxiety as Predictors of Computer Supported Education

ERIC Educational Resources Information Center

Celik, Vehbi; Yesilyurt, Etem

2013-01-01

There is a large body of research regarding computer supported education, perceptions of computer self-efficacy, computer anxiety and the technological attitudes of teachers and teacher candidates. However, no study has been conducted on the correlation between and effect of computer supported education, perceived computer self-efficacy, computer…

Encke-Beta Predictor for Orion Burn Targeting and Guidance

NASA Technical Reports Server (NTRS)

Robinson, Shane; Scarritt, Sara; Goodman, John L.

2016-01-01

The state vector prediction algorithm selected for Orion on-board targeting and guidance is known as the Encke-Beta method. Encke-Beta uses a universal anomaly (beta) as the independent variable, valid for circular, elliptical, parabolic, and hyperbolic orbits. The variable, related to the change in eccentric anomaly, results in integration steps that cover smaller arcs of the trajectory at or near perigee, when velocity is higher. Some burns in the EM-1 and EM-2 mission plans are much longer than burns executed with the Apollo and Space Shuttle vehicles. Burn length, as well as hyperbolic trajectories, has driven the use of the Encke-Beta numerical predictor by the predictor/corrector guidance algorithm in place of legacy analytic thrust and gravity integrals.

Self-care resources and activity as predictors of quality of life in persons after myocardial infarction.

PubMed

Baas, Linda S

2004-01-01

An ex post facto correlational study was conducted to examine predictors of quality of life in persons 3 to 6 months after a myocardial infarction. Self-care resources, self-care knowledge (needs), activity level, and selected demographic variables were examined as predictor variables. A convenience sample of 86 subjects with a mean age of 61 years, was recruited for participation in this study. The study that explained 35% of the variance in quality of life included self-care resources available, activity level, and self-care needs. Modeling and Role Modeling Paradigm provided a useful explanation of how self-care resources and self-care knowledge can be applied to persons recovering from myocardial infarction.

Work stress, role conflict, social support, and psychological burnout among teachers.

PubMed

Burke, R J; Greenglass, E

1993-10-01

This study examined a research model developed to understand psychological burnout among school-based educators. Data were collected from 833 school-based educators using questionnaires completed anonymously. Four groups of predictor variables identified in previous research were considered: individual demographic and situational variables, work stressors, role conflict, and social support. Some support for the model was found. Work stressors were strong predictors of psychological burnout. Individual demographic characteristics, role conflict, and social support had little effect on psychological burnout.

Connections between Narrow Line Seyfert 1 Galaxies and Stellar Black Hole Candidates

NASA Astrophysics Data System (ADS)

Negoro, H.

Connections between narrow line Seyfert 1 galaxies (NLS1s) and black hole candidates are described. It has been pointed out that X-ray properties of NLS1s are simlar to those of stellar black hole candidates (BHCs). It is, however, not clear that NLS1s are corresponding to what `state' in the BHCs. Recently, rapid spectral variations during X-ray flares in a few NLS1s have been discovered using ASCA data. The properties of the spectral variations are very similar to those seen in stellar black hole candidates in the hard state. Such temporal variability accompanying the spectral change has not been recognized in black hole candidates in other states. These and recent theoretical progress based on a time variability model of the BHCs in the hard state imply that the advection plays an important role in the accretion process not only in the BHCs in the hard state, but also in NLS1s.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

PubMed Central

Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis. PMID:26214802

Predictor variables for a half marathon race time in recreational male runners

PubMed Central

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r2 = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m2) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable. PMID:24198577

Predictor variables for a half marathon race time in recreational male runners.

PubMed

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the 'Half Marathon Basel' completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = -0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r(2) = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m(2)) -3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Teacher Candidates' Learning Strategies and Academic Self-Efficacy Levels: Is There a Relation between the Two?

ERIC Educational Resources Information Center

Akçaoglu, Mustafa Öztürk

2016-01-01

The current study aimed to identify teacher candidates' learning strategies and academic self-efficacy levels. Furthermore, the correlations between these variables and gender and departments were looked into. The study was mainly descriptive and correlational. The sample of the study consisted of 256 teacher candidates enrolled at a faculty of…

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Predictors and Moderators of Treatment Response in Childhood Anxiety Disorders: Results from the CAMS Trial

PubMed Central

Compton, Scott N.; Peris, Tara S.; Almirall, Daniel; Birmaher, Boris; Sherrill, Joel; Kendall, Phillip C.; March, John S.; Gosch, Elizabeth A.; Ginsburg, Golda S.; Rynn, Moira A.; Piacentini, John C.; McCracken, James T.; Keeton, Courtney P.; Suveg, Cynthia M.; Aschenbrand, Sasha G.; Sakolsky, Dara; Iyengar, Satish; Walkup, John T.; Albano, Anne Marie

2014-01-01

Objective To examine predictors and moderators of treatment outcomes among 488 youth ages 7-17 years (50% female; 74% ≤ 12 years) with DSM-IV diagnoses of separation anxiety disorder, social phobia, or generalized anxiety disorder who were randomly assigned to receive either cognitive behavior therapy (CBT), sertraline (SRT), their combination (COMB), or medication management with pill placebo (PBO) in the Child/Adolescent Anxiety Multimodal Study (CAMS). Method Six classes of predictor and moderator variables (22 variables) were identified from the literature and examined using continuous (Pediatric Anxiety Ratings Scale; PARS) and categorical (Clinical Global Impression Scale-Improvement; CGI-I) outcome measures. Results Three baseline variables predicted better outcomes (independent of treatment condition) on the PARS, including low anxiety severity (as measured by parents and independent evaluators) and caregiver strain. No baseline variables were found to predict week 12 responder status (CGI-I). Participant's principal diagnosis moderated treatment outcomes, but only on the PARS. No baseline variables were found to moderate treatment outcomes on week 12 responder status (CGI-I). Discussion Overall, anxious children responded favorably to CAMS treatments. However, having more severe and impairing anxiety, greater caregiver strain, and a principal diagnosis of social phobia were associated with less favorable outcomes. Clinical implications of these findings are discussed. PMID:24417601

Religiousness as a Predictor of Alcohol Use in High School Students.

ERIC Educational Resources Information Center

Park, Hae-Seong; Bauer, Scott; Oescher, Jeffrey

2001-01-01

Examines the relationship between religiousness and alcohol use of adolescents based on a sample of high school seniors. Results provide support for examining religiousness variables as predictors of alcohol use patterns of adolescents. (Contains 16 references and 4 tables.) (GCP)

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

Baseline Predictors for Success Following Strategy-Based Cognitive Remediation Group Training in Schizophrenia.

PubMed

Farreny, Aida; Aguado, Jaume; Corbera, Silvia; Ochoa, Susana; Huerta-Ramos, Elena; Usall, Judith

2016-08-01

Our aim was to examine predictive variables associated with the improvement in cognitive, clinical, and functional outcomes after outpatient participation in REPYFLEC strategy-based Cognitive Remediation (CR) group training. In addition, we investigated which factors might be associated with some long-lasting effects at 6 months' follow-up. Predictors of improvement after CR were studied in a sample of 29 outpatients with schizophrenia. Partial correlations were computed between targeted variables and outcomes of response to explore significant associations. Subsequently, we built linear regression models for each outcome variable and predictors of improvement. The improvement in negative symptoms at posttreatment was linked to faster performance in the Trail Making Test B. Disorganization and cognitive symptoms were related to changes in executive function at follow-up. Lower levels of positive symptoms were related to durable improvements in life skills. Levels of symptoms and cognition were associated with improvements following CR, but the pattern of resulting associations was nonspecific.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Anthropometry as a predictor of high speed performance.

PubMed

Caruso, J F; Ramey, E; Hastings, L P; Monda, J K; Coday, M A; McLagan, J; Drummond, J

2009-07-01

To assess anthropometry as a predictor of high-speed performance, subjects performed four seated knee- and hip-extension workouts with their left leg on an inertial exercise trainer (Impulse Technologies, Newnan GA). Workouts, done exclusively in either the tonic or phasic contractile mode, entailed two one-minute sets separated by a 90-second rest period and yielded three performance variables: peak force, average force and work. Subjects provided the following anthropometric data: height, weight, body mass index, as well as total, upper and lower left leg lengths. Via multiple regression, anthropometry attempted to predict the variance per performance variable. Anthropometry explained a modest (R2=0.27-0.43) yet significant degree of variance from inertial exercise trainer workouts. Anthropometry was a better predictor of peak force variance from phasic workouts, while it accounted for a significant degree of average force and work variance solely from tonic workouts. Future research should identify variables that account for the unexplained variance from high-speed exercise performance.

Predictors of Nursing Students' Performance in a One-Semester Organic and Biochemistry Course

NASA Astrophysics Data System (ADS)

van Lanen, Robert J.; Lockie, Nancy M.; McGannon, Thomas

2000-06-01

In an effort to empower nursing students to successfully persist in chemistry, predictors of success for undergraduate nursing students enrolled in a one-semester organic and biochemistry course were identified. The sample consisted of 308 undergraduate nursing students enrolled in Chemistry 108 (Principles of Organic and Biochemistry) during a period of seven semesters. In this study, Supplemental Instruction (SI) is a nonremedial academic support program offered for Chemistry 108 students. Placement tests in Mathematics, Reading, and English are required of all entering students. The English Placement Test assesses proficiency in analytical reading and writing; the Nelson Denny Reading Test (Form E) assesses the student's understanding of written vocabulary and the mastery of reading comprehension, and the Mathematics Placement Test measures the student's mastery of arithmetic and algebraic calculations. Both demographic and academic variables were examined. For the entire sample, five predictor variables were identified: Mathematics Placement Test score, Chemistry 107 grade (a prerequisite), total number of SI sessions attended, Nelson Denny Reading Test (Form E) score, and age. Predictors for various subpopulations of the sample were also identified. Predictors for students of traditional age were Mathematics Placement Test score, total number of SI sessions attended, and Chemistry 107 grade. The best predictors for continuing education students were Chemistry 107 grade and Nelson Denny Test score.

Predictors of posttreatment drinking outcomes in patients with alcohol dependence.

PubMed

Flórez, Gerardo; Saiz, Pilar A; García-Portilla, Paz; De Cos, Francisco J; Dapía, Sonia; Alvarez, Sandra; Nogueiras, Luis; Bobes, Julio

2015-01-01

This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors influence outcomes up to 1 year after treatment. Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors.

Influence of economic and demographic factors on quality of life in renal transplant recipients.

PubMed

Chisholm, Marie A; Spivey, Christina A; Nus, Audrey Van

2007-01-01

The purpose of this study was to determine the influence of annual income, Medicare status, and demographic variables on the health-related quality of life (HQoL) of renal transplant recipients. A cross-sectional survey was mailed to 146 Georgia renal transplant recipients who had functional grafts. Data were collected using the SF-12 Health Survey (version 2), a demographics survey, and 2003 tax documents. One-way ANOVAs and Pearson's R correlations were used to examine relationships between annual income, Medicare status, demographic variables and SF-12 scores. Significant variables were included in stepwise multiple regression analyses. Data from 130 participants (89% response rate) were collected. Recipients with no Medicare coverage had significantly higher scores on the Physical Functioning and Role Physical SF-12 scales (p = 0.005) compared to recipients with Medicare. Annual income was positively correlated with General Health (p < 0.05). Age and race were significant predictors of Vitality (p = 0.004) and Physical Component Summary (p < 0.001) scores. Age, race, and Medicare status were significant predictors of Physical Functioning and Role Physical scores (p < 0.001). Age, annual income, race, and years post-transplant were significant predictors of General Health score (p < 0.001). Age was the sole predictor of Bodily Pain score (p = 0.002), and marital status was the sole predictor of Social Functioning score (p = 0.005). Interventions designed to offset financial barriers may be needed to bolster renal transplant recipients' HQoL.

Moderation analysis with missing data in the predictors.

PubMed

Zhang, Qian; Wang, Lijuan

2017-12-01

The most widely used statistical model for conducting moderation analysis is the moderated multiple regression (MMR) model. In MMR modeling, missing data could pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a nonlinear function of the involved variables. In this study, we consider a simple MMR model, where the effect of the focal predictor X on the outcome Y is moderated by a moderator U. The primary interest is to find ways of estimating and testing the moderation effect with the existence of missing data in X. We mainly focus on cases when X is missing completely at random (MCAR) and missing at random (MAR). Three methods are compared: (a) Normal-distribution-based maximum likelihood estimation (NML); (b) Normal-distribution-based multiple imputation (NMI); and (c) Bayesian estimation (BE). Via simulations, we found that NML and NMI could lead to biased estimates of moderation effects under MAR missingness mechanism. The BE method outperformed NMI and NML for MMR modeling with missing data in the focal predictor, missingness depending on the moderator and/or auxiliary variables, and correctly specified distributions for the focal predictor. In addition, more robust BE methods are needed in terms of the distribution mis-specification problem of the focal predictor. An empirical example was used to illustrate the applications of the methods with a simple sensitivity analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Work life and mental wellbeing of single and non-single working mothers in Scandinavia.

PubMed

Bull, Torill; Mittelmark, Maurice B

2009-08-01

This study examined levels and predictors of mental wellbeing in Scandinavian working single and non-single mothers, with a special focus on financial stress, job characteristics and work-family conflict. The European Social Survey Round 2 (2005) provided questionnaire data from 73 single and 432 non-single working mothers in Denmark, Sweden and Norway. Respondents answered questions about the outcome variables life satisfaction, happiness, and positive affect, and predictor variables financial stress, job characteristics, work-family conflict, and social support. Hierarchical multiple regression was used to assess the relationships between predictor variables and mental wellbeing outcomes. Single working mothers scored significantly lower on life satisfaction and happiness, but not on positive affect, than did non-single mothers. Financial stress was higher in the single mother group. There were no significant differences in levels of enriching or stressful job characteristics, or in levels of social support. While financial stress and work-family conflict were important predictors in both groups, the relationship between financial stress and wellbeing was far stronger in the single mother group. Confidant support was a significant predictor only in the single mother group, and social participation only in the non-single mothers group. This study suggests that the Scandinavian welfare democracies have not yet been successful in relieving the financial pressure experienced by single working mothers. Development of efficient financial support systems should be prioritized. Ways to reduce work-family conflict in both single and non-single mothers in Scandinavia should also be given increased attention.

Predictors of health-related and global quality of life among young adults with difficult-to-treat epilepsy and mild intellectual disability.

PubMed

Endermann, Michael

2013-02-01

This study evaluated predictors of health-related quality of life (HRQOL) and global quality of life (QOL) among young adults with difficult-to-treat epilepsy and mild intellectual disability. One hundred and forty-two persons with epilepsy and cognitive problems were routinely screened on HRQOL, global QOL, and psychological distress four weeks after admission to a time-limited residential rehabilitation unit. The PESOS scales (PE = PErformance, SO = SOciodemographic aspects, S = Subjective evaluation/estimation) on epilepsy-specific problems were administered as measures of HRQOL; a questionnaire on life satisfaction and an item on overall QOL were used as measures of global QOL. Psychological distress was captured with the Symptom Checklist 90-R. Further data were gained from medical files. Quality-of- life predictors were identified using univariate methods and stepwise regression analyses. Psychological distress was the only predictor of all HRQOL and global QOL parameters. Seizure frequency was a predictor of most HRQOL variables. Other epilepsy variables affected only some HRQOL variables but were not associated with global QOL. Health-related quality of life did not seem to be strongly impaired. Only low correlations were found between HRQOL and global QOL. The notion of psychological distress as the most influential predictor of all QOL measures is in line with most findings on QOL in epilepsy. Former observations of weak associations between HRQOL and global QOL among patients with epilepsy and mild intellectual disability are supported. Thus, interventions to reduce psychological distress, besides epilepsy treatment, seem to be of great importance to improve QOL. Copyright © 2012 Elsevier Inc. All rights reserved.

A Survey of Phase Variable Candidates of Human Locomotion

PubMed Central

Villarreal, Dario J.; Gregg, Robert D.

2014-01-01

Studies show that the human nervous system is able to parameterize gait cycle phase using sensory feedback. In the field of bipedal robots, the concept of a phase variable has been successfully used to mimic this behavior by parameterizing the gait cycle in a time-independent manner. This approach has been applied to control a powered transfemoral prosthetic leg, but the proposed phase variable was limited to the stance period of the prosthesis only. In order to achieve a more robust controller, we attempt to find a new phase variable that fully parameterizes the gait cycle of a prosthetic leg. The angle with respect to a global reference frame at the hip is able to monotonically parameterize both the stance and swing periods of the gait cycle. This survey looks at multiple phase variable candidates involving the hip angle with respect to a global reference frame across multiple tasks including level-ground walking, running, and stair negotiation. In particular, we propose a novel phase variable candidate that monotonically parameterizes the whole gait cycle across all tasks, and does so particularly well across level-ground walking. In addition to furthering the design of robust robotic prosthetic leg controllers, this survey could help neuroscientists and physicians study human locomotion across tasks from a time-independent perspective. PMID:25570873

Continuation Power Flow with Variable-Step Variable-Order Nonlinear Predictor

NASA Astrophysics Data System (ADS)

Kojima, Takayuki; Mori, Hiroyuki

This paper proposes a new continuation power flow calculation method for drawing a P-V curve in power systems. The continuation power flow calculation successively evaluates power flow solutions through changing a specified value of the power flow calculation. In recent years, power system operators are quite concerned with voltage instability due to the appearance of deregulated and competitive power markets. The continuation power flow calculation plays an important role to understand the load characteristics in a sense of static voltage instability. In this paper, a new continuation power flow with a variable-step variable-order (VSVO) nonlinear predictor is proposed. The proposed method evaluates optimal predicted points confirming with the feature of P-V curves. The proposed method is successfully applied to IEEE 118-bus and IEEE 300-bus systems.

Pursuit of STEM: Factors shaping degree completion for African American females in STEM

NASA Astrophysics Data System (ADS)

Wilkins, Ashlee N.

The primary purpose of the study was to examine secondary data from the Cooperative Institutional Research Program (CIRP) Freshman and College Senior Surveys to investigate factors shaping degree aspirations for African American female undergraduates partaking in science, technology, engineering, and mathematics (STEM) majors. Hierarchical multiple regression was used to analyze the data and identify relationships between independent variables in relation to the dependent variable. The findings of the study reveal four key variables that were predictive of degree completion for African American females in STEM. Father's education, SAT composite, highest degree planned, and self-perception were positive predictors for females; while independent variable overall sense of community among students remained a negative predictor. Lastly implications for education and recommendations for future research were discussed.

Interobserver agreement in retrospective chart reviews for factors associated with cervical spine injuries in children.

PubMed

Olsen, Cody S; Kuppermann, Nathan; Jaffe, David M; Brown, Kathleen; Babcock, Lynn; Mahajan, Prashant V; Leonard, Julie C

2015-04-01

The objective was to describe the interobserver agreement between trained chart reviewers and physician reviewers in a multicenter retrospective chart review study of children with cervical spine injuries (CSIs). Medical records of children younger than 16 years old with cervical spine radiography from 17 Pediatric Emergency Care Applied Research Network (PECARN) hospitals from years 2000 through 2004 were abstracted by trained reviewers for a study aimed to identify predictors of CSIs in children. Independent physician-reviewers abstracted patient history and clinical findings from a random sample of study patient medical records at each hospital. Interobserver agreement was assessed using percent agreement and the weighted kappa (κ) statistic, with lower 95% confidence intervals. Moderate or better agreement (κ > 0.4) was achieved for most candidate CSI predictors, including altered mental status (κ = 0.87); focal neurologic findings (κ = 0.74); posterior midline neck tenderness (κ = 0.74); any neck tenderness (κ = 0.89); torticollis (κ = 0.79); complaint of neck pain (κ = 0.83); history of loss of consciousness (κ = 0.89); nonambulatory status (κ = 0.74); and substantial injuries to the head (κ = 0.50), torso/trunk (κ = 0.48), and extremities (κ = 0.59). High-risk mechanisms showed near-perfect agreement (diving, κ = 1.0; struck by car, κ = 0.93; other motorized vehicle crash, κ = 0.93; fall, κ = 0.92; high-risk motor vehicle collision, κ = 0.89; hanging, κ = 0.80). Fair agreement was found for clotheslining mechanisms (κ = 0.36) and substantial face injuries (κ = 0.40). Most retrospectively assessed variables thought to be predictive of CSIs in blunt trauma-injured children had at least moderate interobserver agreement, suggesting that these data are sufficiently valid for use in identifying potential predictors of CSI. © 2015 by the Society for Academic Emergency Medicine.

Treatment processes and demographic variables as predictors of dropout from trauma-focused cognitive behavioral therapy (TF-CBT) for youth.

PubMed

Yasinski, Carly; Hayes, Adele M; Alpert, Elizabeth; McCauley, Thomas; Ready, C Beth; Webb, Charles; Deblinger, Esther

2018-05-22

Premature dropout is a significant concern in trauma-focused psychotherapy for youth. Previous studies have primarily examined pre-treatment demographic and symptom-related predictors of dropout, but few consistent findings have been reported. The current study examined demographic, symptom, and in-session process variables as predictors of dropout from Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) for youth. Participants were a diverse sample of Medicaid-eligible youth (ages 7-17; n = 108) and their nonoffending caregivers (n = 86), who received TF-CBT through an effectiveness study in a community setting. In-session process variables were coded from audio-recorded sessions, and these and pre-treatment demographic variables and symptom levels were examined as predictors of dropout prior to receiving an adequate dose of TF-CBT (<7 sessions). Twenty-nine children were classified as dropouts and 79 as completers. Binary logistic regression analyses revealed that higher levels of child and caregiver avoidance expressed during early sessions, as well as greater relationship difficulties between the child and therapist, predicted dropout. Those children who were in foster care during treatment were less likely to drop out than children living with parents or relatives. No other demographic or symptom-related factors predicted dropout. These findings highlight the importance of addressing avoidance and therapeutic relationship difficulties in early sessions of TF-CBT to help reduce dropout, and they have implications for improving efforts to disseminate evidence-based trauma-focused treatments. Copyright © 2018 Elsevier Ltd. All rights reserved.

In vivo serial MRI-based models and statistical methods to quantify sensitivity and specificity of mechanical predictors for carotid plaque rupture: location and beyond.

PubMed

Wu, Zheyang; Yang, Chun; Tang, Dalin

2011-06-01

It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked "ulcer" or "nonulcer" using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.

Impact of marriage on HIV/AIDS risk behaviors among impoverished, at-risk couples: a multilevel latent variable approach.

PubMed

Stein, Judith A; Nyamathi, Adeline; Ullman, Jodie B; Bentler, Peter M

2007-01-01

Studies among normative samples generally demonstrate a positive impact of marriage on health behaviors and other related attitudes. In this study, we examine the impact of marriage on HIV/AIDS risk behaviors and attitudes among impoverished, highly stressed, homeless couples, many with severe substance abuse problems. A multilevel analysis of 368 high-risk sexually intimate married and unmarried heterosexual couples assessed individual and couple-level effects on social support, substance use problems, HIV/AIDS knowledge, perceived HIV/AIDS risk, needle-sharing, condom use, multiple sex partners, and HIV/AIDS testing. More variance was explained in the protective and risk variables by couple-level latent variable predictors than by individual latent variable predictors, although some gender effects were found (e.g., more alcohol problems among men). The couple-level variable of marriage predicted lower perceived risk, less deviant social support, and fewer sex partners but predicted more needle-sharing.

Crisis Management Research Summaries

ERIC Educational Resources Information Center

Brock, Stephen E., Ed.; Zhe, Elizabeth; Torem, Chris; Comeaux, Natashia; Dempsey, Allison

2010-01-01

This article presents a summary of recent crisis management publications. The first research report summarized, "Predictors of PTSD," was a study of predictor variables for responses to the World Trade Center attack. The second paper, "Effective Mental Health Response to Catastrophic Events," looked at effective responses following Hurricane…

Predictors of Immigrant Children's School Achievement: A Comparative Study

ERIC Educational Resources Information Center

Moon, Sung Seek; Kang, Suk-Young; An, Soonok

2009-01-01

This paper examines the predictors and indicators of immigrant children's school achievement, using the two of the most predominant groups of American immigrants (103 Koreans and 100 Mexicans). Regression analyses were conducted to determine which independent variables (acculturation, parenting school involvement, parenting style, parent…

Molecular classification of idiopathic pulmonary fibrosis: personalized medicine, genetics and biomarkers.

PubMed

Hambly, Nathan; Shimbori, Chiko; Kolb, Martin

2015-10-01

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrotic lung disease associated with high morbidity and poor survival. Characterized by substantial disease heterogeneity, the diagnostic considerations, clinical course and treatment response in individual patients can be variable. In the past decade, with the advent of high-throughput proteomic and genomic technologies, our understanding of the pathogenesis of IPF has greatly improved and has led to the recognition of novel treatment targets and numerous putative biomarkers. Molecular biomarkers with mechanistic plausibility are highly desired in IPF, where they have the potential to accelerate drug development, facilitate early detection in susceptible individuals, improve prognostic accuracy and inform treatment recommendations. Although the search for candidate biomarkers remains in its infancy, attractive targets such as MUC5B and MPP7 have already been validated in large cohorts and have demonstrated their potential to improve clinical predictors beyond that of routine clinical practices. The discovery and implementation of future biomarkers will face many challenges, but with strong collaborative efforts among scientists, clinicians and the industry the ultimate goal of personalized medicine may be realized. © 2015 Asian Pacific Society of Respirology.

Multiscale assemblage of an ectomycorrhizal fungal community: the influence of host functional traits and soil properties in a 10-ha miombo forest.

PubMed

Bauman, David; Raspé, Olivier; Meerts, Pierre; Degreef, Jérôme; Ilunga Muledi, Jonathan; Drouet, Thomas

2016-10-01

Ectomycorrhizal fungi (EMF) are highly diversified and dominant in a number of forest ecosystems. Nevertheless, their scales of spatial distribution and the underlying ecological processes remain poorly understood. Although most EMF are considered to be generalists regarding host identity, a preference toward functional strategies of host trees has never been tested. Here, the EMF community was characterised by DNA sequencing in a 10-ha tropical dry season forest-referred to as miombo-an understudied ecosystem from a mycorrhizal perspective. We used 36 soil parameters and 21 host functional traits (FTs) as candidate explanatory variables in spatial constrained ordinations for explaining the EMF community assemblage. Results highlighted that the community variability was explained by host FTs related to the 'leaf economics spectrum' (adjusted R(2) = 11%; SLA, leaf area, foliar Mg content), and by soil parameters (adjusted R(2) = 17%), notably total forms of micronutrients or correlated available elements (Al, N, K, P). Both FTs and soil generated patterns in the community at scales ranging from 75 to 375 m. Our results indicate that soil is more important than previously thought for EMF in miombo woodlands, and show that FTs of host species can be better predictors of symbiont distribution than taxonomical identity. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Trait and perceived environmental competitiveness in achievement situations.

PubMed

Elliot, Andrew J; Jury, Mickaël; Murayama, Kou

2018-06-01

Trait and perceived environmental competitiveness are typically studied separately, but they undoubtedly have a joint influence on goal pursuit and behavior in achievement situations. The present research was designed to study them together. We tested the relation between trait and perceived environmental competitiveness, and we tested these variables as separate and sequential predictors of both performance-based goals and performance attainment. In Studies 1a (N = 387 U.S. undergraduates) and 1b (N = 322 U.S. undergraduates), we assessed participants' trait and perceived environmental competitiveness, as well as third variable candidates. In Study 2 (N = 434 MTurk workers), we sought to replicate and extend Study 1 by adding reports of performance-based goal pursuit. In Study 3 (N = 403 U.S. undergraduates), we sought to replicate and extend Study 2 by adding real-world performance attainment. The studies focused on both the classroom and the workplace. Trait and perceived environmental competitiveness were shown to be positively related and to positively predict separate variance in performance-approach and performance-avoidance goal pursuit. Perceived environmental competitiveness and performance-based goal pursuit were shown to be sequential mediators of the indirect relation between trait competitiveness and performance attainment. These studies highlight the importance of attending to the interplay of the person and the (perceived) situation in analyses of competitive striving. © 2017 Wiley Periodicals, Inc.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method

USGS Publications Warehouse

Eng, K.; Milly, P.C.D.; Tasker, Gary D.

2007-01-01

To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis. ?? 2007 ASCE.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss.

PubMed

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. © 2016 S. Karger GmbH, Freiburg.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715

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

PubMed

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

2018-02-12

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

Predicting the onset of smoking in boys and girls.

PubMed

Charlton, A; Blair, V

1989-01-01

The problem of the high prevalence of smoking among girls and young women is of great concern. In an attempt to identify the factors which influence girls and boys respectively to attempt smoking, the study examines social background, advertising and brand awareness, knowledge, teaching and personal beliefs in conjunction as predictors of smoking. In this study which involved the administration of identical pre- and post-test questionnaires to a sample of boys and girls aged 12 and 13 years, nine variables expressed by never-smokers at pre-test stage were assessed as predictors of immediate future smoking. The two tests were administered 4 months apart to 1125 boys and 1213 girls in northern England. The nine variables included were parental smoking, best friends' smoking, perceived positive values of smoking, perceived negative values of smoking, correct health knowledge, cigarette-brand awareness, having a favourite cigarette advertisement, having a cigarette-brand sponsored sport in four top favourites on television. One group received teaching about smoking between the pre- and post-tests and this was also included as a variable. For boys, no variable investigated had any consistently statistically significant correlation with the uptake of smoking. The most important predictor of smoking for boys, having a best friend who smoked, was significant on application of the chi 2 test (P 0.037), although it was non-significant when included singly in a logistic regression model (0.094); the discrepancy was probably due to the small number of best friends known to smoke. For girls, four variables were found to be significant predictors of smoking when included singly in a logistic regression.(ABSTRACT TRUNCATED AT 250 WORDS)

SCD-HeFT: Use of RR Interval Statistics for Long-term Risk Stratification for Arrhythmic Sudden Cardiac Death

PubMed Central

Au-yeung, Wan-tai M.; Reinhall, Per; Poole, Jeanne E.; Anderson, Jill; Johnson, George; Fletcher, Ross D.; Moore, Hans J.; Mark, Daniel B.; Lee, Kerry L.; Bardy, Gust H.

2015-01-01

Background In the SCD-HeFT a significant fraction of the congestive heart failure (CHF) patients ultimately did not die suddenly from arrhythmic causes. CHF patients will benefit from better tools to identify if ICD therapy is needed. Objective To identify predictor variables from baseline SCD-HeFT patients’ RR intervals that correlate to arrhythmic sudden cardiac death (SCD) and mortality and to design an ICD therapy screening test. Methods Ten predictor variables were extracted from pre-randomization Holter data from 475 patients enrolled in the SCD-HeFT ICD arm using novel and traditional heart rate variability methods. All variables were correlated to SCD using Mann Whitney-Wilcoxon test and receiver operating characteristic analysis. ICD therapy screening tests were designed by minimizing the cost of false classifications. Survival analysis, including log-rank test and Cox models, was also performed. Results α1 and α2 from detrended fluctuation analysis, the ratio of low to high frequency power, the number of PVCs per hour and heart rate turbulence slope are all statistically significant for predicting the occurrences of SCD (p<0.001) and survival (log-rank p<0.01). The most powerful multivariate predictor tool using the Cox Proportional Hazards was α2 with a hazard ratio of 0.0465 (95% CI: 0.00528 – 0.409, p<0.01). Conclusion Predictor variables from RR intervals correlate to the occurrences of SCD and distinguish survival among SCD-HeFT ICD patients. We believe SCD prediction models should incorporate Holter based RR interval analysis to refine ICD patient selection especially in removing patients who are unlikely to benefit from ICD therapy. PMID:26096609

Short-term dynamics of indoor and outdoor endotoxin exposure: Case of Santiago, Chile, 2012.

PubMed

Barraza, Francisco; Jorquera, Héctor; Heyer, Johanna; Palma, Wilfredo; Edwards, Ana María; Muñoz, Marcelo; Valdivia, Gonzalo; Montoya, Lupita D

2016-01-01

Indoor and outdoor endotoxin in PM2.5 was measured for the very first time in Santiago, Chile, in spring 2012. Average endotoxin concentrations were 0.099 and 0.094 [EU/m(3)] for indoor (N=44) and outdoor (N=41) samples, respectively; the indoor-outdoor correlation (log-transformed concentrations) was low: R=-0.06, 95% CI: (-0.35 to 0.24), likely owing to outdoor spatial variability. A linear regression model explained 68% of variability in outdoor endotoxins, using as predictors elemental carbon (a proxy of traffic emissions), chlorine (a tracer of marine air masses reaching the city) and relative humidity (a modulator of surface emissions of dust, vegetation and garbage debris). In this study, for the first time a potential source contribution function (PSCF) was applied to outdoor endotoxin measurements. Wind trajectory analysis identified upwind agricultural sources as contributors to the short-term, outdoor endotoxin variability. Our results confirm an association between combustion particles from traffic and outdoor endotoxin concentrations. For indoor endotoxins, a predictive model was developed but it only explained 44% of endotoxin variability; the significant predictors were tracers of indoor PM2.5 dust (Si, Ca), number of external windows and number of hours with internal doors open. Results suggest that short-term indoor endotoxin variability may be driven by household dust/garbage production and handling. This would explain the modest predictive performance of published models that use answers to household surveys as predictors. One feasible alternative is to increase the sampling period so that household features would arise as significant predictors of long-term airborne endotoxin levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

PubMed

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

Predictors of stroke in patients with impaired glucose tolerance: results from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research trial.

PubMed

Preiss, David; Giles, Thomas D; Thomas, Laine E; Sun, Jie-Lena; Haffner, Steven M; Holman, Rury R; Standl, Eberhard; Mazzone, Theodore; Rutten, Guy E; Tognoni, Gianni; Chiang, Fu-Tien; McMurray, John J V; Califf, Robert M

2013-09-01

Risk factors for stroke are well-established in general populations but sparsely studied in individuals with impaired glucose tolerance. We identified predictors of stroke among participants with impaired glucose tolerance in the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial. Cox proportional-hazard regression models were constructed using baseline variables, including the 2 medications studied, valsartan and nateglinide. Among 9306 participants, 237 experienced a stroke over 6.4 years. Predictors of stroke included classical risk factors such as existing cerebrovascular and coronary heart disease, higher pulse pressure, higher low-density lipoprotein cholesterol, older age, and atrial fibrillation. Other factors, including previous venous thromboembolism, higher waist circumference, lower estimated glomerular filtration rate, lower heart rate, and lower body mass index, provided additional important predictive information, yielding a C-index of 0.72. Glycemic measures were not predictive of stroke. Variables associated with stroke were similar in participants with no prior history of cerebrovascular disease at baseline. The most powerful predictors of stroke in patients with impaired glucose tolerance included a combination of established risk factors and novel variables, such as previous venous thromboembolism and elevated waist circumference, allowing moderately effective identification of high-risk individuals.

Predictors of intelligence at the age of 5: family, pregnancy and birth characteristics, postnatal influences, and postnatal growth.

PubMed

Eriksen, Hanne-Lise Falgreen; Kesmodel, Ulrik Schiøler; Underbjerg, Mette; Kilburn, Tina Røndrup; Bertrand, Jacquelyn; Mortensen, Erik Lykke

2013-01-01

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003-2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence - Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child's sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child's head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude.

Preinjury somatization symptoms contribute to clinical recovery after sport-related concussion.

PubMed

Nelson, Lindsay D; Tarima, Sergey; LaRoche, Ashley A; Hammeke, Thomas A; Barr, William B; Guskiewicz, Kevin; Randolph, Christopher; McCrea, Michael A

2016-05-17

To determine the degree to which preinjury and acute postinjury psychosocial and injury-related variables predict symptom duration following sport-related concussion. A total of 2,055 high school and collegiate athletes completed preseason evaluations. Concussed athletes (n = 127) repeated assessments serially (<24 hours and days 8, 15, and 45) postinjury. Cox proportional hazard modeling was used to predict concussive symptom duration (in days). Predictors considered included demographic and history variables; baseline psychological, neurocognitive, and balance functioning; acute injury characteristics; and postinjury clinical measures. Preinjury somatic symptom score (Brief Symptom Inventory-18 somatization scale) was the strongest premorbid predictor of symptom duration. Acute (24-hour) postconcussive symptom burden (Sport Concussion Assessment Tool-3 symptom severity) was the best injury-related predictor of recovery. These 2 predictors were moderately correlated (r = 0.51). Path analyses indicated that the relationship between preinjury somatization symptoms and symptom recovery was mediated by postinjury concussive symptoms. Preinjury somatization symptoms contribute to reported postconcussive symptom recovery via their influence on acute postconcussive symptoms. The findings highlight the relevance of premorbid psychological factors in postconcussive recovery, even in a healthy athlete sample relatively free of psychopathology or medical comorbidities. Future research should elucidate the neurobiopsychosocial mechanisms that explain the role of this individual difference variable in outcome following concussive injury. © 2016 American Academy of Neurology.

Period 3 gene polymorphism and sleep adaptation to stressful urban environments.

PubMed

Anderson, Maxwell R; Akeeb, Ameenat; Lavela, Joseph; Chen, Yuanxiu; Mellman, Thomas A

2017-02-01

This study's objective was to investigate the relationship between a variable-number tandem-repeat (VNTR) Period 3 gene (PER3) polymorphism and sleep adaptation to stressful urban environments. Seventy-five (49 female) African American participants (ages 18-35 years) living in neighbourhoods with high rates of violent crime were selected for the study based on converging criteria for good or poor sleep. Categorization of sleep quality was based on the Insomnia Severity Index (ISI), estimates of typical sleep duration and sleep efficiency. Other assessments included the Fear of Sleep Index (FOSI) and City Stress Inventory (CSI). Whole blood DNA was analysed for the 4 and 5 VNTR alleles using polymerase chain reaction (PCR) and restrictive enzyme digestion. Fifty-seven per cent of those who were homo- or heterozygous for the 4-repeat allele were poor sleepers versus 25% of those homozygous for the 5-repeat allele; χ 2  = 4.17, P = 0.041. In a logistic regression model with all the variables with significant bivariate relationships to sleep quality group, FOSI was the only significant predictor (χ 2  = 5.68, P = 0.017). FOSI scores were higher among those with the 4-repeat allele (t = 2.66, P = 0.013). The PER3 4 and 5 VNTR polymorphisms appear to influence sensitivity to the effects of stressful urban environments on sleep. While FOSI was the only variable associated independently with sleep quality category, the candidate vulnerability allele was also associated with greater 'fear of sleep'. © 2016 European Sleep Research Society.

Genetic variation in alpha2-adrenoreceptors and heart rate recovery after exercise

PubMed Central

Kohli, Utkarsh; Diedrich, André; Kannankeril, Prince J.; Muszkat, Mordechai; Sofowora, Gbenga G.; Hahn, Maureen K.; English, Brett A.; Blakely, Randy D.; Stein, C. Michael

2015-01-01

Heart rate recovery (HRR) after exercise is an independent predictor of adverse cardiovascular outcomes. HRR is mediated by both parasympathetic reactivation and sympathetic withdrawal and is highly heritable. We examined whether common genetic variants in adrenergic and cholinergic receptors and transporters affect HRR. In our study 126 healthy subjects (66 Caucasians, 56 African Americans) performed an 8 min step-wise bicycle exercise test with continuous computerized ECG recordings. We fitted an exponential curve to the postexercise R-R intervals for each subject to calculate the recovery constant (kr) as primary outcome. Secondary outcome was the root mean square residuals averaged over 1 min (RMS1min), a marker of parasympathetic tone. We used multiple linear regressions to determine the effect of functional candidate genetic variants in autonomic pathways (6 ADRA2A, 1 ADRA2B, 4 ADRA2C, 2 ADRB1, 3 ADRB2, 2 NET, 2 CHT, and 1 GRK5) on the outcomes before and after adjustment for potential confounders. Recovery constant was lower (indicating slower HRR) in ADRA2B 301–303 deletion carriers (n = 54, P = 0.01), explaining 3.6% of the interindividual variability in HRR. ADRA2A Asn251Lys, ADRA2C rs13118771, and ADRB1 Ser49Gly genotypes were associated with RMS1min. Genetic variability in adrenergic receptors may be associated with HRR after exercise. However, most of the interindividual variability in HRR remained unexplained by the variants examined. Noncandidate gene-driven approaches to study genetic contributions to HRR in larger cohorts will be of interest. PMID:26058836

An assessment of environmental literacy and analysis of predictors of responsible environmental behavior held by secondary teachers in Hualien County of Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Shih-Jang

The major purpose of this study was to determine the relative contribution of nine variables in predicting teachers' responsible environmental behavior (REB). The theoretic framework of this study was based on the Hines model, the Hungerford and Volk model, and the environmental literacy framework proposed by Environmental Literacy Assessment Consortium. A nine-page instrument was administered by mailed questionnaire to 300 randomly selected secondary teachers in Hualien County of Taiwan with a 78.7% response rate. Correlation and stepwise multiple regression analyses were conducted. The following conclusions were drawn: (1) For all the respondents, all the nine environmental literacy variables were significant correlates of REB. These correlates included: perceived knowledge of environmental action strategies (KNOW; r =.46), intention to act (IA; r =.46), perceived skill in using environmental action strategies (SKILL; r =.45), perceived knowledge of environmental problems and issues (KISSU; r =.34), environmental sensitivity (r =.28), environmental responsibility (r =.27), perceived knowledge of ecology and environmental science (r =.27), locus of control (r =.27), and environmental attitudes (r =.21). (2) When only the nine environmental literacy variables were considered, the most parsimonious set of predictors of REB for all the teachers included: (a) KNOW, (Rsp2 =.2116); (b) IA, (Rsp2 =.0916); and (c) SKILL, (Rsp2 =.0205). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.2559); (b) SKILL (Rsp2.0926); and (c) environmental responsibility (Rsp2 =.0219). For the rural teachers, the most parsimonious set of predictors included: (a) KNOW (Rsp2 =.1872); (b) IA (Rsp2 =.0816); and (c) KISSU (Rsp2 =.0318). (3) When the environmental literacy variables as well as demographic and experience variables were considered, the most parsimonious set of predictors for all the teachers included: (a) KNOW, (Rsp2 =.2834); (b) IA, (Rsp2 =.0696); (c) area of residence, (Rsp2 =.0174); and (d) SKILL, (Rsp2 =.0163). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.3199); (b) SKILL (Rsp2 =.0840); (c) major sources of environmental information (Rsp2 =.0432); and (d) membership in environmental organizations, (Rsp2 =.0240). Implications for environmental education program development and instructional practice were presented. Recommendations for further research were also provided.

Impression management and achievement motivation: Investigating substantive links.

PubMed

Elliot, Andrew J; Aldhobaiban, Nawal; Murayama, Kou; Kobeisy, Ahmed; Gocłowska, Małgorzata A; Khyat, Aber

2018-02-01

In this research, we investigate impression management (IM) as a substantive personality variable by linking it to differentiated achievement motivation constructs, namely achievement motives (workmastery, competitiveness, fear of failure) and achievement goals (mastery-approach, mastery-avoidance, performance-approach, performance-avoidance). Study 1 revealed that IM was a positive predictor of workmastery and a negative predictor of competitiveness (with and without self-deceptive enhancement (SDE) controlled). Studies 2a and 2b revealed that IM was a positive predictor of mastery-approach goals and mastery-avoidance goals (without and, in Study 2b, with SDE controlled). These findings highlight the value of conceptualising and utilising IM as a personality variable in its own right and shed light on the nature of the achievement motive and achievement goal constructs. © 2016 International Union of Psychological Science.

Predictors of Outcomes in Autism Early Intervention: Why Don’t We Know More?

PubMed Central

Vivanti, Giacomo; Prior, Margot; Williams, Katrina; Dissanayake, Cheryl

2014-01-01

Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on “what works for whom and why” in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies. PMID:24999470

The Investigation of Teachers Candidates' Occupational Anxiety Levels and Their Views on Occupational Anxiety

ERIC Educational Resources Information Center

Teke, Aziz; Koc, Hayri

2017-01-01

This research was carried out in order to examine the occupational anxiety levels of the teacher candidates in terms of some variables and to determine their opinions about the occupational concerns of the teacher candidates. The research was carried out in the 2016-2017 Academic Year with the participation of a total of 377 teacher candidates…

A Comparison of the Life Satisfaction and Hopelessness Levels of Teacher Candidates in Turkey

ERIC Educational Resources Information Center

Gencay, Selcuk; Gencay, Okkes Alpaslan

2011-01-01

This study aims to explore the level of hopelessness and life satisfaction of teacher candidates from the view points of gender and branch variables. With this aim, the "Beck Hopelessness Scale and Life Satisfaction Scale" has been applied to a total of 278 teacher candidates, of which 133 were females and 145 were males. According to…

Pre-Service Teachers' Use of Dynamic Discourse Variables during Classroom Teaching

ERIC Educational Resources Information Center

Kaya, Sibel; Ceviz, Asli Elgun

2017-01-01

The aim of this study is to examine the nature of questioning in primary classrooms taught by teacher candidates. The participants were 39 teacher candidates enrolled in the Department of Primary Education at a large university in Western Turkey as well as 3rd and 4th-grade students in four schools located in the area. Each teacher candidate has…

An Analysis of Self-Efficacy Perceptions of Physical Education and Sport Teacher Candidates and Other Teacher Candidates on Teaching Profession

ERIC Educational Resources Information Center

Ozer, Tugce; Demirel, Duygu H.

2017-01-01

Aim of this research is to identify the self-efficacy perception levels of teacher candidates studying at department of Physical Education and Sport and other teaching departments towards teaching profession, to present whether these the self-efficacy perceptions differ or not depending on independent variables acquired from the personal…

Examination of the Self-Efficacy of Primary School Teacher Candidates towards First Reading-Writing Education

ERIC Educational Resources Information Center

Bayraktar, Hatice Vatansever

2016-01-01

The aim of this study is to examine the levels of the self-efficacy of primary school teacher candidates towards first reading-writing education and whether they differentiate by various variables. The study is prepared in accordance with the screening model. The universe of this study consists of the primary school teacher candidates who receive…

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

A SIGNIFICANCE TEST FOR THE LASSO1

PubMed Central

Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert

2014-01-01

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model). Our proof of this result for the special case of the first predictor to enter the model (i.e., testing for a single significant predictor variable against the global null) requires only weak assumptions on the predictor matrix X. On the other hand, our proof for a general step in the lasso path places further technical assumptions on X and the generative model, but still allows for the important high-dimensional case p > n, and does not necessarily require that the current lasso model achieves perfect recovery of the truly active variables. Of course, for testing the significance of an additional variable between two nested linear models, one typically uses the chi-squared test, comparing the drop in residual sum of squares (RSS) to a χ12 distribution. But when this additional variable is not fixed, and has been chosen adaptively or greedily, this test is no longer appropriate: adaptivity makes the drop in RSS stochastically much larger than χ12 under the null hypothesis. Our analysis explicitly accounts for adaptivity, as it must, since the lasso builds an adaptive sequence of linear models as the tuning parameter λ decreases. In this analysis, shrinkage plays a key role: though additional variables are chosen adaptively, the coefficients of lasso active variables are shrunken due to the l1 penalty. Therefore, the test statistic (which is based on lasso fitted values) is in a sense balanced by these two opposing properties—adaptivity and shrinkage—and its null distribution is tractable and asymptotically Exp(1). PMID:25574062

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

Risk factors for psychological maladjustment of parents of children with cancer.

PubMed

Hoekstra-Weebers, J E; Jaspers, J P; Kamps, W A; Klip, E C

1999-12-01

To examine risk variables for future, more immediate, and persistent psychological distress of parents of pediatric cancer patients. Parents (n = 128) completed questionnaires at the time of diagnosis (T1) and 12 months later (T2). Multiple regression analyses were performed using the following as predictors: demographics, illness-related variables, other life events, personality, coping styles, and social support. Trait anxiety was the strongest predictor of both fathers' and mothers' future distress. Changes in trait anxiety during the year also accompanied changes in both parents' levels of distress. Additional prospective predictors for fathers were the coping style "social support-seeking" and dissatisfaction with support. Dissatisfaction with support also had short-term effects for fathers. An additional prospective predictor for mothers was the number of pleasant events they had experienced prior to diagnosis, while a short-term effect was found for performance in assertiveness. No predictors for the persistence of distress were found. These results underscore the importance of personality anxiety in predicting parents' risk for adjustment difficulties associated with the experience of cancer in one's child. An additional risk factor for fathers was social support. For mothers, previously experienced life events and the frequency of assertive behavior were additional risk factors.

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Pharmacogenetics of risperidone therapy in autism: association analysis of eight candidate genes with drug efficacy and adverse drug reactions.

PubMed

Correia, C T; Almeida, J P; Santos, P E; Sequeira, A F; Marques, C E; Miguel, T S; Abreu, R L; Oliveira, G G; Vicente, A M

2010-10-01

Little has been reported on the factors, genetic or other, that underlie the variability in individual response, particularly for autism. In this study we simultaneously explored the effects of multiple candidate genes on clinical improvement and occurrence of adverse drug reactions, in 45 autistic patients who received monotherapy with risperidone up to 1 year. Candidate genes involved in the pharmacokinetics (CYP2D6 and ABCB1) and pharmacodynamics (HTR2A, HTR2C, DRD2, DRD3, HTR6) of the drug, and the brain-derived neurotrophic factor (BDNF) gene, were analysed. Using the generalized estimating equation method these genes were tested for association with drug efficacy, assessed with the Autism Treatment Evaluation Checklist, and with safety and tolerability measures, such as prolactin levels, body mass index (BMI), waist circumference and neurological adverse effects, including extrapyramidal movements. Our results confirm that risperidone therapy was very effective in reducing some autism symptoms and caused few serious adverse effects. After adjusting for confounding factors, the HTR2A c.-1438G>A, DRD3 Ser9Gly, HTR2C c.995G>A and ABCB1 1236C>T polymorphisms were predictors for clinical improvement with risperidone therapy. The HTR2A c.-1438G>A, HTR2C c.68G>C (p.C33S), HTR6 c.7154-2542C>T and BDNF c.196G>A (p.V66M) polymorphisms influenced prolactin elevation. HTR2C c.68G>C and CYP2D6 polymorphisms were associated with risperidone-induced increase in BMI or waist circumference. We thus identified for the first time several genes implicated in risperidone efficacy and safety in autism patients. Although association results require replication, given the small sample size, the study makes a preliminary contribution to the personalized therapy of risperidone in autism.

Predictors of Early Termination in a University Counseling Training Clinic

ERIC Educational Resources Information Center

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

Verbal and Nonverbal Predictors of Spelling Performance

ERIC Educational Resources Information Center

Sadoski, Mark; Willson, Victor L.; Holcomb, Angelia; Boulware-Gooden, Regina

2005-01-01

Verbal and nonverbal predictors of spelling performance in Grades 1-12 were investigated using the national norming data from a standardized spelling test. Verbal variables included number of letters, phonemes, syllables, digraphs, blends, silent markers, r-controlled vowels, and the proportion of grapheme-phoneme correspondence. The nonverbal…

Predictors of Secondary Traumatic Stress among Children's Advocacy Center Forensic Interviewers

ERIC Educational Resources Information Center

Bonach, Kathryn; Heckert, Alex

2012-01-01

This study examined various predictor variables that were hypothesized to impact secondary traumatic stress in forensic interviewers (n = 257) from children's advocacy centers across the United States. Data were examined to investigate the relationship between organizational satisfaction, organizational buffers, and job support with secondary…

Relationship between Graphical Device Comprehension and Overall Text Comprehension for Third-Grade Children

ERIC Educational Resources Information Center

Roberts, Kathryn L.; Norman, Rebecca R.; Cocco, Jaime

2015-01-01

This study examined relationships between reading comprehension, known predictors of reading comprehension (i.e., cognitive flexibility, fluency, reading motivation and attitude, vocabulary), and graphical device comprehension. One-hundred fifty-six third graders completed assessments of known predictor variables and an assessment tapping…

A Study of Predictors of Environmental Behaviour using U.S. Samples.

ERIC Educational Resources Information Center

Sia, Archibald P.; And Others

1986-01-01

Reports on a study done with the intentions of determining the relative contribution of eight variables in predicting environmental behavior. Concluded that the major predictors were skill in using environmental action strategies, level of environmental sensitivity, and percieved knowledge of environmental action strategies. (TW)

Predictors of Life Satisfaction in Individuals with Intellectual Disabilities

ERIC Educational Resources Information Center

Miller, S. M.; Chan, F.

2008-01-01

Background: The purpose of this study was to examine factors that predict life satisfaction in individuals with intellectual disabilities (ID). Two groups of variables were studied: life skills (interpersonal, instrumental and leisure) and higher-order predictors (social support, self-determination and productivity). Method: Fifty-six participants…

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115). Conclusions Our proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules. PMID:23802742

Recovery of Work-Related Stress: Complaint Reduction and Work-Resumption are Relatively Independent Processes.

PubMed

de Vente, Wieke; Kamphuis, Jan Henk; Blonk, Roland W B; Emmelkamp, Paul M G

2015-09-01

The process of recovery from work-related stress, consisting of complaint reduction and work-resumption, is not yet fully understood. The aim of this study was to investigate predictors of complaint reduction and work-resumption, as well as testing complaint reduction as a mediator in the association between predictors and work-resumption. Seventy-one patients on sickness-leave because of work-related stress complaints were followed over a period of 13 months. Predictors comprised personal (demographics, coping, cognitions), work-related (job-characteristics, social support), and illness-related (complaint duration, absence duration) variables. Dependent variables were distress complaints, burnout complaints, and work-resumption. Complaints reduced considerably over time to borderline clinical levels and work-resumption increased to 68% at 13 months. Predictors of stronger reduction of distress complaints were male gender, less working hours, less decision authority, more co-worker support, and shorter absence duration. Predictors of stronger reduction of burnout complaints were male gender, lower age, high education, less avoidant coping, less decision authority, more job security, and more co-worker support. Predictors of work-resumption were lower age and stronger reduction of burnout complaints. No indication for a mediating role of burnout complaints between the predictor age and work-resumption was found. Complaint reduction and work-resumption are relatively independent processes. Symptom reduction is influenced by individual and work-related characteristics, which holds promise for a multidisciplinary treatment approach for work-related stress.

Species-environment relationships and potential for distribution modelling in coastal waters

NASA Astrophysics Data System (ADS)

Snickars, M.; Gullström, M.; Sundblad, G.; Bergström, U.; Downie, A.-L.; Lindegarth, M.; Mattila, J.

2014-01-01

Due to increasing pressure on the marine environment there is a growing need to understand species-environment relationships. To provide background for prioritising among variables (predictors) for use in distribution models, the relevance of predictors for benthic species was reviewed using the coastal Baltic Sea as a case-study area. Significant relationships for three response groups (fish, macroinvertebrates, macrovegetation) and six predictor categories (bottom topography, biotic features, hydrography, wave exposure, substrate and spatiotemporal variability) were extracted from 145 queried peer-reviewed field-studies covering three decades and six subregions. In addition, the occurrence of interaction among predictors was analysed. Hydrography was most often found in significant relationships, had low level of interaction with other predictors, but also had the most non-significant relationships. Depth and wave exposure were important in all subregions and are readily available, increasing their applicability for cross-regional modelling efforts. Otherwise, effort to model species distributions may prove challenging at larger scale as the relevance of predictors differed among both response groups and regions. Fish and hard bottom macrovegetation have the largest modelling potential, as they are structured by a set of predictors that at the same time are accurately mapped. A general importance of biotic features implies that these need to be accounted for in distribution modelling, but the mapping of most biotic features is challenging, which currently lowers the applicability. The presence of interactions suggests that predictive methods allowing for interactive effects are preferable. Detailing these complexities is important for future distribution modelling.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

PubMed

Heddam, Salim; Kisi, Ozgur

2017-07-01

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction

NASA Technical Reports Server (NTRS)

Huikuri, H. V.; Makikallio, T. H.; Peng, C. K.; Goldberger, A. L.; Hintze, U.; Moller, M.

2000-01-01

BACKGROUND: Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. METHODS AND RESULTS: Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Observations of candidate oscillating eclipsing binaries and two newly discovered pulsating variables

NASA Astrophysics Data System (ADS)

Liakos, A.; Niarchos, P.

2009-03-01

CCD observations of 24 eclipsing binary systems with spectral types ranging between A0-F0, candidate for containing pulsating components, were obtained. Appropriate exposure times in one or more photometric filters were used so that short-periodic pulsations could be detected. Their light curves were analyzed using the Period04 software in order to search for pulsational behaviour. Two new variable stars, namely GSC 2673-1583 and GSC 3641-0359, were discov- ered as by-product during the observations of eclipsing variables. The Fourier analysis of the observations of each star, the dominant pulsation frequencies and the derived frequency spectra are also presented.

Predictors of Binge Eating among Bariatric Surgery Candidates: Disinhibition as a Mediator of the Relationship Between Depressive Symptoms and Binge Eating.

PubMed

Cox, Stephanie; Brode, Cassie

2018-02-07

Current and lifetime psychopathology is common in adult patients seeking bariatric surgery, with major depressive disorder and binge eating disorder affecting a higher proportion of this group than the general population. While depressive symptoms have been previously associated with eating pathology, potential mediators of this relationship are not well understood. This study used a naturalistic, retrospective design to investigate cognitive and behavioral aspects of eating behavior (cognitive restraint, disinhibition, and hunger) as potential mediators of the relationship between depressive symptoms and binge eating within a sample of 119 adult patients (82.4% female; 96.6% white; mean age = 47 years) seeking bariatric surgery (Roux-en-Y and sleeve gastrectomy) at a large university medical center. Patients completed a standardized presurgical psychological evaluation to determine appropriateness for bariatric surgery as part of routine clinical practice. Binge eating was assessed via clinician rating (number of binge eating episodes per week) based on DSM-IV diagnostic criteria and self-report measures (Binge Eating Scale) in order to account for potential methodological differences. Depressive symptoms were assessed using the Beck Depression Inventory. Depressive symptoms were a significant predictor of binge eating, disinhibition, and hunger. However, only disinhibition emerged as a significant mediator of the relationship between depressive symptoms and binge eating. Behavioral disinhibition, or a tendency toward overconsumption of food and challenges restraining impulses associated with a loss of control eating, may represent an important variable in determining the relation between depressive symptoms and binge eating, in bariatric surgery patients.

Development of a model with which to predict the life expectancy of patients with spinal epidural metastasis.

PubMed

Bartels, Ronald H M A; Feuth, Ton; van der Maazen, Richard; Verbeek, André L M; Kappelle, Arnoud C; André Grotenhuis, J; Leer, Jan Willem

2007-11-01

The surgical treatment of spinal epidural metastasis is evolving. To be a surgical candidate, a patient should have a life expectancy of at least 3 months. Estimation of survival by experienced specialists has proven to be unreliable. The Cox proportional hazards model was used to make a prediction model. To validate the model, Efron optimism correction by bootstrapping was performed. Retrospective data of patients treated for a spinal metastasis were used. Possible predictive factors were defined based on clinical experience and the literature. Statistical methods and clinical knowledge were also used to reveal an optimal set of predictors of survival. Data from patients treated at the Department of Radiation Oncology for spinal metastasis between 1998 and 2005 were evaluated. The case notes of 219 patients form the base of this study. In the final model, only 5 variables were required to predict the survival of a patient with spinal metastasis: sex, location of the primary lesion, intentional curative treatment of the primary tumor, cervical location of the spinal metastasis, and Karnofsky performance score. Examples with different predictors are given. The R(2) (N) index of Nagelkerke was 0.36 (95% confidence interval [95% CI], 0.28-0.48) and the c-index 0.72 (95% CI, 0.68-0.77). A reliable and simple model with which to predict the survival of a patient with spinal epidural metastasis is presented. Without the need for extensive investigations, survival can be predicted and only 5 easily obtainable parameters are required.

Predictors of post-traumatic stress symptoms in Oklahoma City: exposure, social support, peri-traumatic responses.

PubMed

Tucker, P; Pfefferbaum, B; Nixon, S J; Dickson, W

2000-11-01

Eighty-five adults seeking mental health assistance six months after the Oklahoma City bombing were assessed to determine which of three groups of variables (exposure, peri-traumatic responses, and social support) predicted development of post-traumatic stress disorder (PTSD) symptoms. Variables most highly associated with subsequent PTSD symptoms included having been injured (among exposure variables), feeling nervous or afraid (peri-traumatic responses), and responding that counseling helped (support variables). Combining primary predictors in the three areas, PTSD symptoms were more likely to occur in those reporting counseling to help and those feeling nervous or afraid at the time of the bombing. Implications of these findings are discussed for behavioral health administrators and clinicians planning service delivery to groups of victims seeking mental health intervention after terrorist attacks and other disasters.

A Cross-Language Study of Acoustic Predictors of Speech Intelligibility in Individuals With Parkinson's Disease

PubMed Central

Choi, Yaelin

2017-01-01

Purpose The present study aimed to compare acoustic models of speech intelligibility in individuals with the same disease (Parkinson's disease [PD]) and presumably similar underlying neuropathologies but with different native languages (American English [AE] and Korean). Method A total of 48 speakers from the 4 speaker groups (AE speakers with PD, Korean speakers with PD, healthy English speakers, and healthy Korean speakers) were asked to read a paragraph in their native languages. Four acoustic variables were analyzed: acoustic vowel space, voice onset time contrast scores, normalized pairwise variability index, and articulation rate. Speech intelligibility scores were obtained from scaled estimates of sentences extracted from the paragraph. Results The findings indicated that the multiple regression models of speech intelligibility were different in Korean and AE, even with the same set of predictor variables and with speakers matched on speech intelligibility across languages. Analysis of the descriptive data for the acoustic variables showed the expected compression of the vowel space in speakers with PD in both languages, lower normalized pairwise variability index scores in Korean compared with AE, and no differences within or across language in articulation rate. Conclusions The results indicate that the basis of an intelligibility deficit in dysarthria is likely to depend on the native language of the speaker and listener. Additional research is required to explore other potential predictor variables, as well as additional language comparisons to pursue cross-linguistic considerations in classification and diagnosis of dysarthria types. PMID:28821018

Nutritional Deficiencies in Chinese Patients Undergoing Gastric Bypass and Sleeve Gastrectomy: Prevalence and Predictors.

PubMed

Guan, Bingsheng; Yang, Jingge; Chen, Yanya; Yang, Wah; Wang, Cunchuan

2018-05-12

Nutritional deficiencies have been reported in bariatric surgery patients with inconsistent results. However, scarce data exist for Chinese patients. We aimed to assess nutritional deficiencies in Chinese patients undergoing Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG), and to identify predictors of postoperative nutritional status. A retrospective review of a prospectively collected database was conducted in the patients undergoing RYGB and SG in our hospital between June 2013 and January 2017. Anthropometric data and nutritional data were collected before surgery, at 6 and 12 months postoperatively. This study enrolled 269 patients (120 RYGB, 149 SG). Nutritional deficiencies were common in Chinese bariatric candidates, with vitamin D deficiency the most serious (78.8%), followed by vitamin B1 (39.2%), vitamin B6 (28.0%), folate (26.8%), vitamin C (18.0%) albumin (13.4%), transferrin (11.6%), and phosphorus (11.5%). Despite postoperative routine multivitamin and calcium supplements, nutritional deficiencies were still obvious for RYGB and SG patients. The prevalence of hemoglobin and vitamin B12 deficiencies increased remarkably in the RYGB group; the levels of hemoglobin, globin, vitamin B12, and ferritin decreased significantly (P < 0.05). Preoperative hemoglobin, vitamin B12, and ferritin levels were independently associated with postoperative decrease, respectively. Deficiencies of vitamin D, vitamin B1, vitamin B6, vitamin C, and albumin before surgery were predictors for deficiencies 1 year after surgery, respectively. Nutritional deficiencies are common in Chinese bariatric surgery candidates. Similar deficiencies were also seen after RYGB and SG. Routine evaluation and related corrections of preoperative nutritional abnormity could contribute to postoperative nutrient balance.

Bullying by Definition: An Examination of Definitional Components of Bullying

ERIC Educational Resources Information Center

Goldsmid, Susan; Howie, Pauline

2014-01-01

Lack of definitional consensus remains an important unresolved issue within bullying research. This study examined the ability of definitional variables to predict overall level of victimisation (distress, power inequity, and provocation as predictors) and bullying (intention to harm, power inequity, and provocation as predictors) in 246…

VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making

ERIC Educational Resources Information Center

Alverson, Charlotte Y.; Yamamoto, Scott H.

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school,…

Oral Reading Fluency in Second Language Reading

ERIC Educational Resources Information Center

Jeon, Eun Hee

2012-01-01

This study investigated the role of oral reading fluency in second language reading. Two hundred and fifty-five high school students in South Korea were assessed on three oral reading fluency (ORF) variables and six other reading predictors. The relationship between ORF and other reading predictors was examined through an exploratory factor…

Predictors of Employment Outcomes for State-Federal Vocational Rehabilitation Consumers with HIV/AIDS

ERIC Educational Resources Information Center

Jung, Youngoh; Schaller, James; Bellini, James

2010-01-01

In this study, the authors investigated the effects of demographic, medical, and vocational rehabilitation service variables on employment outcomes of persons living with HIV/AIDS. Binary logistic regression analyses were conducted to determine predictors of employment outcomes using two groups drawn from Rehabilitation Services Administration…

Predictors of Care-Giver Stress in Families of Preschool-Aged Children with Developmental Disabilities

ERIC Educational Resources Information Center

Plant, K. M.; Sanders, M. R.

2007-01-01

Background: This study examined the predictors, mediators and moderators of parent stress in families of preschool-aged children with developmental disability. Method: One hundred and five mothers of preschool-aged children with developmental disability completed assessment measures addressing the key variables. Results: Analyses demonstrated that…

Using Situational Factors to Predict Types of Prison Violence.

ERIC Educational Resources Information Center

Steinke, Pamela

1991-01-01

Tested situational factors as predictors of types of individual aggressive incidents in male prison population. Categorized incidents of violence by whether occurrence of infraction involved aggressive behavior directed at staff, another inmate, self, or property. Found that situational variables did serve as predictors of these categories of…

Predictors of Confidence and Competence among Early Childhood Interventionists

ERIC Educational Resources Information Center

Bruder, Mary Beth; Dunst, Carl J.; Wilson, Cristina; Stayton, Vicki

2013-01-01

The preservice and in-service predictors of 1,668 Part C early intervention and Part B(619) preschool special practitioners' perceived self-efficacy beliefs are reported. The preservice variables were type of degree (discipline), years of formal postsecondary education, licensure, and participants' judgment of how well their preservice training…

Beyond Health and Wealth: Predictors of Women's Retirement Satisfaction

ERIC Educational Resources Information Center

Price, Christine A.; Balaswamy, Shantha

2009-01-01

Despite empirical support for the positive effects of health and wealth on retirement satisfaction, alternative variables also play a key role in helping to shape women's assessment of retirement. In the present study, we explore personal and psychosocial predictors of women's retirement satisfaction while controlling for financial security and…

What Good Predictors of Marijuana Use Are Good For: A Synthesis of Research.

ERIC Educational Resources Information Center

Derzon, James H.; Lipsey, Mark W.

1999-01-01

Analyzes correlates of marijuana use based on 3,690 effect sizes coded from 86 prospective longitudinal studies. Summarizes findings on strength of relationships for categorizing predictor variables, and implications of these relationships. Findings are relevant for intervention programmers and policymakers since they identify characteristics of…

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Predictors of Recidivism to a Juvenile Assessment Center: An Expanded Analysis.

ERIC Educational Resources Information Center

Dembo, Richard; And Others

1996-01-01

Over 5,200 youths processed through a Juvenile Assessment Center during a 20-month period were involved in this study of recidivism predictors. Significant relationships were found between the youths' demographics, dependency referral factors, delinquency referral history variables, and recidivism. Direct implications for service delivery and…

Examining Postsecondary Education Predictors and Participation for Students with Learning Disabilities

ERIC Educational Resources Information Center

Joshi, Gauri S.; Bouck, Emily C.

2017-01-01

Given the history of poor postschool outcomes for students with disabilities, researchers repeatedly sought to demonstrate the links between predictor variables and postschool outcomes for students with disabilities. This secondary data analysis used the National Longitudinal Transition Study-2 to examine the relationship between postsecondary…

A Longitudinal Investigation of Employment among Low-Income Youth: Patterns, Predictors, and Correlates

ERIC Educational Resources Information Center

Purtell, Kelly M.; McLoyd, Vonnie C.

2013-01-01

Drawing on previous research linking patterns of adolescent employment--defined in terms of duration and intensity--to educational and occupational outcomes later in life (Staff & Mortimer, 2008), the present study (a) examined positive social behavior and academic variables as longitudinal predictors of patterns of adolescent employment…

Noncognitive Predictors of Student Athletes' Academic Performance.

ERIC Educational Resources Information Center

Simons, Herbert D.; Van Rheenen, Derek

2000-01-01

Examines the role of four noncognitive variables in predicting academic performance in 200 Division I athletes. Studies the noncognitive variables of athletic-academic commitment, feelings of being exploited, academic self-worth, self-handicapping excuses as well as several background and academic preparation variables. Finds all four noncognitive…

Self-Efficacy of Teacher Candidates for Teaching First Reading and Writing

ERIC Educational Resources Information Center

Gündogmus, Hatice Degirmenci

2018-01-01

The purpose of this study is to determine by different variables the self-efficacy of a teacher candidate for teaching first reading and writing in their 3rd and 4th year in the department of primary school teaching. In line with the purpose of the study, the self-efficacy levels of teacher candidates for teaching first reading and writing were…

Fear of childbirth and obstetrical events as predictors of postnatal symptoms of depression and post-traumatic stress disorder.

PubMed

Fairbrother, Nichole; Woody, Sheila R

2007-12-01

This prospective study examined psychological and obstetrical predictors of enduring postpartum symptoms of depression and post-traumatic stress disorder. Contrary to prediction, prenatal fear of childbirth did not significantly predict symptoms of depression or post-traumatic stress disorder at one month postpartum, but anxiety sensitivity was an unexpected predictor that merits further investigation. Several obstetrical and neonatal variables significantly predicted symptoms of post-traumatic disorder, but not depression.

Personality variables as predictors of Facebook usage.

PubMed

Caci, Barbara; Cardaci, Maurizio; Tabacchi, Marco E; Scrima, Fabrizio

2014-04-01

This study investigates the role of personality factors as predictors of Facebook usage. Data concerning Facebook usage and personality factors from 654 Facebook users were gathered using a web survey. Using path analysis, the results showed Openness was a predictor of Facebook early adoption, Conscientiousness with sparing use, Extraversion with long sessions and abundant friendships, and Neuroticism with high frequency of sessions. The possible role of Agreeableness in predicting low session frequency and friendships needs further validation.

Predictors of outcomes of psychological treatments for disordered gambling: A systematic review.

PubMed

Merkouris, S S; Thomas, S A; Browning, C J; Dowling, N A

2016-08-01

This systematic review aimed to synthesise the evidence relating to pre-treatment predictors of gambling outcomes following psychological treatment for disordered gambling across multiple time-points (i.e., post-treatment, short-term, medium-term, and long-term). A systematic search from 1990 to 2016 identified 50 articles, from which 11 socio-demographic, 16 gambling-related, 21 psychological/psychosocial, 12 treatment, and no therapist-related variables, were identified. Male gender and low depression levels were the most consistent predictors of successful treatment outcomes across multiple time-points. Likely predictors of successful treatment outcomes also included older age, lower gambling symptom severity, lower levels of gambling behaviours and alcohol use, and higher treatment session attendance. Significant associations, at a minimum of one time-point, were identified between successful treatment outcomes and being employed, ethnicity, no gambling debt, personality traits and being in the action stage of change. Mixed results were identified for treatment goal, while education, income, preferred gambling activity, problem gambling duration, anxiety, any psychiatric comorbidity, psychological distress, substance use, prior gambling treatment and medication use were not significantly associated with treatment outcomes at any time-point. Further research involving consistent treatment outcome frameworks, examination of treatment and therapist predictor variables, and evaluation of predictors across long-term follow-ups is warranted to advance this developing field of research. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of placebo responses: a systematic review of the literature

PubMed Central

Horing, Bjoern; Weimer, Katja; Muth, Eric R.; Enck, Paul

2014-01-01

Objective: Predicting who responds to placebo treatment—and under which circumstances—has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed. Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted. PMID:25324797

The Contribution of Vegetation and Landscape Configuration for Predicting Environmental Change Impacts on Iberian Birds

PubMed Central

Triviño, Maria; Thuiller, Wilfried; Cabeza, Mar; Hickler, Thomas; Araújo, Miguel B.

2011-01-01

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26–40% of the cases with BRT, and in 1–18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species. PMID:22216263

The unusual suspect: Land use is a key predictor of biodiversity patterns in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Martins, Inês Santos; Proença, Vânia; Pereira, Henrique Miguel

2014-11-01

Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species-area relationship with a multi-habitat model, the countryside species-area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.

Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes

Treesearch

Jakob Zscheischler; Simone Fatichi; Sebastian Wolf; Peter D. Blanken; Gil Bohrer; Ken Clark; Ankur R. Desai; David Hollinger; Trevor Keenan; Kimberly A. Novick; Sonia I. Seneviratne

2016-01-01

Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their...

Predictors of Performance in Introductory Finance: Variables within and beyond the Student's Control

ERIC Educational Resources Information Center

Englander, Fred; Wang, Zhaobo; Betz, Kenneth

2015-01-01

This study examined variables that are within and beyond the control of students in explaining variations in performance in an introductory finance course. Regression models were utilized to consider whether the variables within the student's control have a greater impact on course performance relative to the variables beyond the student's…

Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma

PubMed Central

Seliger, Barbara; Dressler, Sven P.; Wang, Ena; Kellner, Roland; Recktenwald, Christian V.; Lottspeich, Friedrich; Marincola, Francesco M.; Baumgärtner, Maja; Atkins, Derek; Lichtenfels, Rudolf

2012-01-01

Results obtained from expression profilings of renal cell carcinoma using different “ome”-based approaches and comprehensive data analysis demonstrated that proteome-based technologies and cDNA microarray analyses complement each other during the discovery phase for disease-related candidate biomarkers. The integration of the respective data revealed the uniqueness and complementarities of the different technologies. While comparative cDNA microarray analyses though restricted to upregulated targets largely revealed genes involved in controlling gene/protein expression (19%) and signal transduction processes (13%), proteomics/PROTEOMEX-defined candidate biomarkers include enzymes of the cellular metabolism (36%), transport proteins (12%) and cell motility/structural molecules (10%). Candidate biomarkers defined by proteomics and PROTEOMEX are frequently shared, whereas the sharing rate between cDNA microarray and proteome-based profilings is limited. Putative candidate biomarkers provide insights into their cellular (dys)function and their diagnostic/prognostic value but still warrant further validation in larger patient numbers. Based on the fact that merely 3 candidate biomarkers were shared by all applied technologies, namely annexin A4, tubulin alpha-1A chain and ubiquitin carboxyl-terminal hydrolase L1 the analysis at a single hierarchical level of biological regulation seems to provide only limited results thus emphasizing the importance and benefit of performing rather combinatorial screenings which can complement the standard clinical predictors. PMID:19235166

Patient Characteristics and Patient Behavior as Predictors of Outcome in Cognitive Therapy and Exposure Therapy for Hypochondriasis.

PubMed

Richtberg, Samantha; Jakob, Marion; Höfling, Volkmar; Weck, Florian

2017-06-01

Psychotherapy for hypochondriasis has greatly improved over the last decades and cognitive-behavioral treatments are most promising. However, research on predictors of treatment outcome for hypochondriasis is rare. Possible predictors of treatment outcome in cognitive therapy (CT) and exposure therapy (ET) for hypochondriasis were investigated. Characteristics and behaviors of 75 patients were considered as possible predictors: sociodemographic variables (sex, age, and cohabitation); psychopathology (pretreatment hypochondriacal symptoms, comorbid mental disorders, and levels of depression, anxiety, and somatic symptoms); and patient in-session interpersonal behavior. Severity of pretreatment hypochondriacal symptoms, comorbid mental disorders, and patient in-session interpersonal behavior were significant predictors in multiple hierarchical regression analyses. Interactions between the predictors and the treatment (CT or ET) were not found. In-session interpersonal behavior is an important predictor of outcome. Furthermore, there are no specific contraindications to treating hypochondriasis with CT or ET. © 2016 Wiley Periodicals, Inc.

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel

2010-01-01

Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734

Predicting national suicide numbers with social media data.

PubMed

Won, Hong-Hee; Myung, Woojae; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J; Kim, Doh Kwan

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

Predicting National Suicide Numbers with Social Media Data

PubMed Central

Won, Hong-Hee; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J.

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention. PMID:23630615

Demographic and socioenvironmental predictors of premorbid marijuana use among patients with first-episode psychosis.

PubMed

Pauselli, Luca; Birnbaum, Michael L; Vázquez Jaime, Beatriz Paulina; Paolini, Enrico; Kelley, Mary E; Broussard, Beth; Compton, Michael T

2018-01-31

We identified, in subjects with first-episode psychosis, demographic and socioenvironmental predictors of three variables pertaining to premorbid marijuana use: age at initiation of marijuana use, trajectories of marijuana use in the five years prior to onset of psychosis, and the cumulative "dose" of marijuana intake in that same premorbid period. We enrolled 247 first-episode psychosis patients and collected data on lifetime marijuana/alcohol/tobacco use, age at onset of psychosis, diverse socioenvironmental variables, premorbid adjustment, past traumatic experiences, perceived neighborhood-level social disorder, and cannabis use experiences. Bivariate tests were used to examine associations between the three premorbid marijuana use variables and hypothesized predictors. Regression models determined which variables remained independently significantly associated. Age at initiation of cigarette smoking was linked to earlier initiation, faster escalation, and higher cumulative dose of premorbid marijuana use. During childhood, poorer academic performance was predictive of an earlier age at initiation of marijuana use, while poorer sociability was related to more rapid escalation to daily use and a higher cumulative dose. As expected, experiencing euphoric effects was positively correlated with trajectories and cumulative dose, but having negative experiences was unrelated. Traumatic childhood/adolescent experiences were correlated with rapid escalation and amount of marijuana used, but not with age at initiation of marijuana use. These data expand the very limited literature on predictors of premorbid marijuana use in first-episode psychosis. Given its association with earlier age at onset of psychosis, and poorer outcomes among first-episode patients, prevention and treatment efforts should be further developed. Copyright © 2018 Elsevier B.V. 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

Psychological Predictors of Visual and Auditory P300 Brain-Computer Interface Performance

PubMed Central

Hammer, Eva M.; Halder, Sebastian; Kleih, Sonja C.; Kübler, Andrea

2018-01-01

Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor “emotional stability” was negatively correlated (Spearman's rho = −0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. “Emotional stability” was also negatively related to auditory P300-BCI performance (Spearman's rho = −0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. “Emotional stability” was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors. PMID:29867319

Predictors of suicidal ideation in chronic pain patients: an exploratory study.

PubMed

Racine, Mélanie; Choinière, Manon; Nielson, Warren R

2014-05-01

To explore whether chronic pain (CP) patients who report suicidal ideation (SI) present a distinctive profile with regard to their sociodemographic characteristics, physical health, psychological well-being, cognitions, and use of antidepressants, illicit drugs, and alcohol for pain relief. Eighty-eight CP patients completed self-administered questionnaires during their intake assessment at 3 pain clinics located in the province of Québec (Canada). Patients reporting active or passive SI on the Beck Depression Inventory were compared with patients reporting no SI. Between-group univariate analyses were performed using profile variables to compare patients with and without SI. Significant variables were then entered into multiple logistic regression analyses to identify significant independent predictors of SI. Twenty-four percent of patients reported having had SI. Unemployed/disabled patients were 6 times more likely to report SI. Poor sleep quality was the only predictor of SI among the physical variables. For psychological well-being, depressive symptoms did not significantly predict SI. However, the poorer the patients perceived their mental health to be the more likely they were to report SI. Pain-related helplessness was the only predictor for SI among the cognitive variables. Patients who had used illicit drugs as a form of pain relief at any time since pain onset were 5 times more likely to report SI. Similar results were obtained for those who were on antidepressants. Some factors associated with SI seem pain specific, whereas others are more generally associated with SI. Better identification and understanding of these factors is essential for the development of targeted suicide prevention programs for at-risk CP patients.

Institutional Delivery Service Utilization among Women from Rural Districts of Wolaita and Dawro Zones, Southern Ethiopia; a Community Based Cross-Sectional Study

PubMed Central

Arba, Mihiretu Alemayehu; Darebo, Tadele Dana; Koyira, Mengistu Meskele

2016-01-01

Introduction The highest number of maternal deaths occur during labour, delivery and the first day after delivery highlighting the critical need for good quality care during this period. Therefore, for the strategies of institutional delivery to be effective, it is essential to understand the factors that influence individual and household factors to utilize skilled birth attendance and institutions for delivery. This study was aimed to assess factors affecting the utilization of institutional delivery service of women in rural districts of Wolaita and Dawro Zones. Methods A community based cross-sectional study was done among mothers who gave birth within the past one year preceding the survey in Wolaita and Dawro Zones, from February 01 –April 30, 2015 by using a three stage sampling technique. Initially, 6 districts were selected randomly from the total of 17 eligible districts. Then, 2 kebele from each district was selected randomly cumulating a total of 12 clusters. Finally, study participants were selected from each cluster by using systematic sampling technique. Accordingly, 957 mothers were included in the survey. Data was collected by using a pretested interviewer administered structured questionnaire. The questionnaire was prepared by including socio-demographic variables and variables of maternal health service utilization factors. Data was entered using Epi-data version 1.4.4.0 and exported to SPSS version 20 for analysis. Bivariate and multiple logistic regressions were applied to identify candidate and predictor variables respectively. Result Only 38% of study participants delivered the index child at health facility. Husband’s educational status, wealth index, average distance from nearest health facility, wanted pregnancy, agreement to follow post-natal care, problem faced during delivery, birth order, preference of health professional for ante-natal care and maternity care were predictors of institutional delivery. Conclusion The use of institutional delivery service is low in the study community. Eventhough antenatal care service is high; nearly two in every three mothers delivered their index child out of health facility. Improving socio-economic status of mothers as well as availing modern health facilities to the nearest locality will have a good impact to improve institutional delivery service utilization. Similarly, education is also a tool to improve awareness of mothers and their husbands for the improvement of health care service utilization. PMID:26986563

Institutional Delivery Service Utilization among Women from Rural Districts of Wolaita and Dawro Zones, Southern Ethiopia; a Community Based Cross-Sectional Study.

PubMed

Arba, Mihiretu Alemayehu; Darebo, Tadele Dana; Koyira, Mengistu Meskele

2016-01-01

The highest number of maternal deaths occur during labour, delivery and the first day after delivery highlighting the critical need for good quality care during this period. Therefore, for the strategies of institutional delivery to be effective, it is essential to understand the factors that influence individual and household factors to utilize skilled birth attendance and institutions for delivery. This study was aimed to assess factors affecting the utilization of institutional delivery service of women in rural districts of Wolaita and Dawro Zones. A community based cross-sectional study was done among mothers who gave birth within the past one year preceding the survey in Wolaita and Dawro Zones, from February 01 -April 30, 2015 by using a three stage sampling technique. Initially, 6 districts were selected randomly from the total of 17 eligible districts. Then, 2 kebele from each district was selected randomly cumulating a total of 12 clusters. Finally, study participants were selected from each cluster by using systematic sampling technique. Accordingly, 957 mothers were included in the survey. Data was collected by using a pretested interviewer administered structured questionnaire. The questionnaire was prepared by including socio-demographic variables and variables of maternal health service utilization factors. Data was entered using Epi-data version 1.4.4.0 and exported to SPSS version 20 for analysis. Bivariate and multiple logistic regressions were applied to identify candidate and predictor variables respectively. Only 38% of study participants delivered the index child at health facility. Husband's educational status, wealth index, average distance from nearest health facility, wanted pregnancy, agreement to follow post-natal care, problem faced during delivery, birth order, preference of health professional for ante-natal care and maternity care were predictors of institutional delivery. The use of institutional delivery service is low in the study community. Eventhough antenatal care service is high; nearly two in every three mothers delivered their index child out of health facility. Improving socio-economic status of mothers as well as availing modern health facilities to the nearest locality will have a good impact to improve institutional delivery service utilization. Similarly, education is also a tool to improve awareness of mothers and their husbands for the improvement of health care service utilization.

Severe right ventricular dysfunction is an independent predictor of pre- and post-transplant mortality among candidates for heart transplantation.

PubMed

Ravis, Eleonore; Theron, Alexis; Mancini, Julien; Jaussaud, Nicolas; Morera, Pierre; Chalvignac, Virginie; Guidon, Catherine; Grisoli, Dominique; Gariboldi, Vlad; Riberi, Alberto; Habib, Gilbert; Mouly-Bandini, Annick; Collart, Frederic

2017-03-01

Heart transplantation is the gold-standard treatment for end-stage heart failure. However, the shortage of grafts has led to longer waiting times and increased mortality for candidates without priority. To study waiting-list and post-transplant mortality, and their risk factors among patients registered for heart transplantation without initial high emergency procedure. All patients registered on the heart transplantation waiting list (2004-2015) without initial high emergency procedure were included. Clinical, biological, echocardiographic and haemodynamic data were collected. Waiting list and 1-year post-transplant survival were analysed with a Kaplan-Meier model. Of 221 patients enrolled, 168 (76.0%) were men. Mean age was 50.0±12.0 years. Forty-seven patients died on the waiting list, resulting in mortality rates of 11.2±2.7% at 1 year, 31.9±5.4% at 2 years and 49.4±7.1% at 3 years. Median survival was 36.0±4.6 months. In the multivariable analysis, left ventricular ejection fraction<30% (hazard ratio [HR]: 3.76, 95% confidence interval [CI]: 1.38-10.24; P=0.010) and severe right ventricular systolic dysfunction (HR: 2.89, 95% CI: 1.41-5.92; P=0.004) were associated with increased waiting-list mortality. The post-transplant survival rate was 73.1±4.4% at 1 year. Pretransplant severe right ventricular dysfunction and age>50 years were strong predictors of death after transplantation (HR: 5.38, 95% CI: 1.38-10.24 [P=0.020] and HR: 6.16, 95% CI: 1.62-9.32 [P=0.0130], respectively). Mortality among candidates for heart transplantation remains high. Patients at highest risk of waiting-list mortality have to be promoted, but without compromising post-transplant outcomes. For this reason, candidates with severe right ventricular dysfunction are of concern, because, for them, transplantation is hazardous. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Prevalence and Religious Predictors of Healing Prayer Use in the USA: Findings from the Baylor Religion Survey.

PubMed

Levin, Jeff

2016-08-01

Using data from the 2010 Baylor Religion Survey (N = 1714), this study investigates the prevalence and religious predictors of healing prayer use among US adults. Indicators include prayed for self (lifetime prevalence = 78.8 %), prayed for others (87.4 %), asked for prayer (54.1 %), laying-on-of-hands (26.1 %), and participated in a prayer group (53.0 %). Each was regressed onto eight religious measures, and then again controlling for sociodemographic variables and health. While all religious measures had net effects on at least one healing prayer indicator, the one consistent predictor was a four-item scale assessing a loving relationship with God. Higher scores were associated with more frequent healing prayer use according to every measure, after controlling for all other religious variables and covariates.

Factors predicting recall of mathematics terms by deaf students: implications for teaching.

PubMed

Lang, Harry; Pagliaro, Claudia

2007-01-01

In this study of deaf high school students, imagery and familiarity were found to be the best predictors of geometry word recall, whereas neither concreteness nor signability of the terms was a significant predictor variable. Recall of high imagery terms was significantly better than for low imagery terms, and the same result was found for high- over low-familiarity and signability. Concrete terms were recalled significantly better than abstract terms. Geometry terms that could be represented with single signs were recalled significantly better than those that are usually fingerspelled or those represented by compound signs. Teachers with degrees and/or certification in mathematics had significantly higher self-ratings for the strongest predictor variables, imagery (visualization), and familiarity, as compared with those without such formal training. Based on these findings, implications for mathematics instruction, teacher education, and research are provided.

Moderator Variables as Bias in Testing Black Children

ERIC Educational Resources Information Center

Williams, Robert L.

1975-01-01

The claim that tests of intelligence and abilities are the best predictors of academic success fails to examine closely the important moderator variable as test and criterion characteristics rather than as person characteristics. (EH)

An investigation of meaningful understanding and effectiveness of the implementation of Piagetian and Ausubelian theories in physics instruction

NASA Astrophysics Data System (ADS)

Williams, Karen Ann

One section of college students (N = 25) enrolled in an algebra-based physics course was selected for a Piagetian-based learning cycle (LC) treatment while a second section (N = 25) studied in an Ausubelian-based meaningful verbal reception learning treatment (MVRL). This study examined the students' overall (concept + problem solving + mental model) meaningful understanding of force, density/Archimedes Principle, and heat. Also examined were students' meaningful understanding as measured by conceptual questions, problems, and mental models. In addition, students' learning orientations were examined. There were no significant posttest differences between the LC and MVRL groups for students' meaningful understanding or learning orientation. Piagetian and Ausubelian theories explain meaningful understanding for each treatment. Students from each treatment increased their meaningful understanding. However, neither group altered their learning orientation. The results of meaningful understanding as measured by conceptual questions, problem solving, and mental models were mixed. Differences were attributed to the weaknesses and strengths of each treatment. This research also examined four variables (treatment, reasoning ability, learning orientation, and prior knowledge) to find which best predicted students' overall meaningful understanding of physics concepts. None of these variables were significant predictors at the.05 level. However, when the same variables were used to predict students' specific understanding (i.e. concept, problem solving, or mental model understanding), the results were mixed. For forces and density/Archimedes Principle, prior knowledge and reasoning ability significantly predicted students' conceptual understanding. For heat, however, reasoning ability was the only significant predictor of concept understanding. Reasoning ability and treatment were significant predictors of students' problem solving for heat and forces. For density/Archimedes Principle, treatment was the only significant predictor of students' problem solving. None of the variables were significant predictors of mental model understanding. This research suggested that Piaget and Ausubel used different terminology to describe learning yet these theories are similar. Further research is needed to validate this premise and validate the blending of the two theories.

Sick-leave track record and other potential predictors of a disability pension. A population based study of 8,218 men and women followed for 16 years

PubMed Central

Wallman, Thorne; Wedel, Hans; Palmer, Edward; Rosengren, Annika; Johansson, Saga; Eriksson, Henry; Svärdsudd, Kurt

2009-01-01

Background A number of previous studies have investigated various predictors for being granted a disability pension. The aim of this study was to test the efficacy of sick-leave track record as a predictor of being granted a disability pension in a large dataset based on subjects sampled from the general population and followed for a long time. Methods Data from five ongoing population-based Swedish studies was used, supplemented with data on all compensated sick leave periods, disability pensions granted, and vital status, obtained from official registers. The data set included 8,218 men and women followed for 16 years, generated 109,369 person years of observation and 97,160 sickness spells. Various measures of days of sick leave during follow up were used as independent variables and disability pension grant was used as outcome. Results There was a strong relationship between individual sickness spell duration and annual cumulative days of sick leave on the one hand and being granted a disability pension on the other, among both men and women, after adjustment for the effects of marital status, education, household size, smoking habits, geographical area and calendar time period, a proxy for position in the business cycle. The interval between sickness spells showed a corresponding inverse relationship. Of all the variables studied, the number of days of sick leave per year was the most powerful predictor of a disability pension. For both men and women 245 annual sick leave days were needed to reach a 50% probability of transition to disability. The independent variables, taken together, explained 96% of the variation in disability pension grantings. Conclusion The sick-leave track record was the most important predictor of the probability of being granted a disability pension in this study, even when the influences of other variables affecting the outcome were taken into account. PMID:19368715

Environmental Literacy in Madeira Island (Portugal): The Influence of Demographic Variables

ERIC Educational Resources Information Center

Spinola, Hélder

2016-01-01

Demographic factors are among those that influence environmental literacy and, particularly, environmentally responsible behaviours, either directly or due to an aggregation effect dependent on other types of variables. Present study evaluates a set of demographic variables as predictors for environmental literacy among 9th grade students from…

Intra-Personal and Extra-Personal Predictors of Suicide Attempts of South Korean Adolescents

ERIC Educational Resources Information Center

Lee, Ji-Young; Bae, Sung-Man

2015-01-01

The purpose of this study was to explore significant variables predicting adolescent suicidal attempts. Socio-environmental variables such as gender, school record, school grade, school adaptation, and family intimacy together with intra-individual variables including depression, anxiety, delinquency, stress, and self-esteem were considered as…

Factors Associated with Asian American Students' Choice of STEM Major

ERIC Educational Resources Information Center

Lowinger, Robert; Song, Hyun-a

2017-01-01

This study explored Asian American students' likelihood of selecting STEM over liberal arts or business college majors using the Education Longitudinal Study of 2002. Student-level variables were the strongest predictors of college major, followed by parent-level variables, and background variables. Academic achievement and interest were the…

Teachers' Knowledge Indices as Predictors of Secondary School Students' Academic Achievement in Kwara State, Nigeria

ERIC Educational Resources Information Center

Olasehinde-Williams, Felicia; Yahaya, Lasiele; Owolabi, Henry

2018-01-01

That less than 40% of candidates who took the Senior School Certificate Examinations in Nigeria between 2009 and 2015 had credits and above in English language and Mathematics has become a source of worry to all stakeholders. Results of research efforts to provide plausible explanations to the problem have been inconclusive. Also, not much had…

United States Marine Corps Basic Reconnaissance Course: Predictors of Success

DTIC Science & Technology

2017-03-01

PAGE INTENTIONALLY LEFT BLANK 81 VI. CONCLUSIONS AND RECOMMENDATIONS A. CONCLUSIONS The objective of my research is to provide quantitative ...percent over the last three years, illustrating there is room for improvement. This study conducts a quantitative and qualitative analysis of the...criteria used to select candidates for the BRC. The research uses multi-variate logistic regression models and survival analysis to determine to what

The Role of the A* Grade at a Level as a Predictor of University Performance in the United Kingdom

ERIC Educational Resources Information Center

Vidal Rodeiro, Carmen; Zanini, Nadir

2015-01-01

In summer 2010, the A* grade at A level was awarded for the first time. This grade was introduced to help higher education institutions to differentiate between the highest achieving candidates and to promote and reward greater stretch and challenge. Exploring data from the Higher Education Statistics Service and making use of multilevel…

Data-Science Analysis of the Macro-scale Features Governing the Corrosion to Crack Transition in AA7050-T7451

NASA Astrophysics Data System (ADS)

Co, Noelle Easter C.; Brown, Donald E.; Burns, James T.

2018-05-01

This study applies data science approaches (random forest and logistic regression) to determine the extent to which macro-scale corrosion damage features govern the crack formation behavior in AA7050-T7451. Each corrosion morphology has a set of corresponding predictor variables (pit depth, volume, area, diameter, pit density, total fissure length, surface roughness metrics, etc.) describing the shape of the corrosion damage. The values of the predictor variables are obtained from white light interferometry, x-ray tomography, and scanning electron microscope imaging of the corrosion damage. A permutation test is employed to assess the significance of the logistic and random forest model predictions. Results indicate minimal relationship between the macro-scale corrosion feature predictor variables and fatigue crack initiation. These findings suggest that the macro-scale corrosion features and their interactions do not solely govern the crack formation behavior. While these results do not imply that the macro-features have no impact, they do suggest that additional parameters must be considered to rigorously inform the crack formation location.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

PubMed

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Prediction of Academic Achievement in an NATA-Approved Graduate Athletic Training Education Program

PubMed Central

Keskula, Douglas R.; Sammarone, Paula G.; Perrin, David H.

1995-01-01

The Purpose of this investigation was to determine which information used in the applicant selection process would best predict the final grade point average of students in a National Athletic Trainers Association (NATA) graduate athletic training education program. The criterion variable used was the graduate grade-point average (GPAg) calculated at the completion of the program of study. The predictor variables included: 1) Graduate Record Examination-Quantitative (GRE-Q) scores; and 2) Graduate Record Examination-Verbal (GRE-V) scores, 3) preadmission grade point average (GPAp), 4) total athletic training hours (hours), and 5) curriculum or internship undergraduate athletic training education (program). Data from 55 graduate athletic training students during a 5-year period were evaluated. Stepwise multiple regression analysis indicated that GPAp was a significant predictor of GPAg, accounting for 34% of the variance. GRE-Q, GRE-V, hours, and program did not significantly contribute individually or in combination to the prediction of GPAg. The results of this investigation suggest that, of the variables examined, GPAp is the best predictor of academic success in an NATA-approved graduate athletic training education program. PMID:16558312