Predicting Responsiveness to Treatment of Children with Autism: A Retrospective Study of the Importance of Physical Dysmorphology

ERIC Educational Resources Information Center

Stoelb, M.; Yarnal, R.; Miles, J.; Takahashi, T. N.; Farmer, J. E.; McCathren, R. B.

2004-01-01

This retrospective study examined predictors of outcome for children with autism following 6 and 12 months of early intensive behavioral intervention. Potential predictor variables included pretreatment functioning, age at onset of treatment, treatment intensity, family involvement, and physical characteristics (e.g., brain abnormalities,…

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills.

PubMed

Brandstätter, Christian; Laner, David; Prantl, Roman; Fellner, Johann

2014-12-01

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed. This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables. The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills. Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia

USGS Publications Warehouse

Wakie, Tewodros; Evangelista, Paul H.; Jarnevich, Catherine S.; Laituri, Melinda

2014-01-01

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species-occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

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.

Clarifying the role of mean centring in multicollinearity of interaction effects.

PubMed

Shieh, Gwowen

2011-11-01

Moderated multiple regression (MMR) is frequently employed to analyse interaction effects between continuous predictor variables. The procedure of mean centring is commonly recommended to mitigate the potential threat of multicollinearity between predictor variables and the constructed cross-product term. Also, centring does typically provide more straightforward interpretation of the lower-order terms. This paper attempts to clarify two methodological issues of potential confusion. First, the positive and negative effects of mean centring on multicollinearity diagnostics are explored. It is illustrated that the mean centring method is, depending on the characteristics of the data, capable of either increasing or decreasing various measures of multicollinearity. Second, the exact reason why mean centring does not affect the detection of interaction effects is given. The explication shows the symmetrical influence of mean centring on the corrected sum of squares and variance inflation factor of the product variable while maintaining the equivalence between the two residual sums of squares for the regression of the product term on the two predictor variables. Thus the resulting test statistic remains unchanged regardless of the obvious modification of multicollinearity with mean centring. These findings provide a clear understanding and demonstration on the diverse impact of mean centring in MMR applications. ©2011 The British Psychological Society.

Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications.

PubMed

Emir, Birol; Johnson, Kjell; Kuhn, Max; Parsons, Bruce

2017-01-01

This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP). The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%. The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14.1%), baseline depression (13.9%), and using pregabalin as a monotherapy (11.7%). The finding that pain changes by week 1 or weeks 1 and 3 are the best predictors of pregabalin response at 6 weeks suggests that adhering to a pregabalin medication regimen is important for an optimal end-of-treatment outcome. Regarding baseline predictors alone, considerable published evidence supports the importance of high baseline pain score and presence of depression as factors that can affect treatment response. Future research would be required to elucidate why using pregabalin as a monotherapy also had more than a 10% variable importance as a potential predictor. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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.

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

Obstetric and Parental Psychiatric Variables as Potential Predictors of Autism Severity

ERIC Educational Resources Information Center

Wallace, Anna E.; Anderson, George M.; Dubrow, Robert

2008-01-01

Associations between obstetric and parental psychiatric variables and subjects' Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) domain scores were examined using linear mixed effects models. Data for the 228 families studied were provided by the Autism Genetic Resource Exchange. Hypertension (P =…

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.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed

Donath, Carolin; Graessel, Elmar; Baier, Dirk; Bleich, Stefan; Hillemacher, Thomas

2014-04-26

Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents' suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Three parental variables showed a relevant association with suicide attempts in adolescents - (all protective): mother's warmth and father's warmth in childhood and mother's control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk - as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD.

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.

Relationship among Demographic Variables and Pupils' Reasoning Ability

ERIC Educational Resources Information Center

Tella, Adeyinka; Tella, Adedeji; Adika, L. O.; Toyobo, Majekodunmi Oluwole

2008-01-01

Introduction: Pupils reasoning ability is a sine-qua-non to the evaluation of their performance in learning and an indicator of their potential predictors of future performance. Method: The study examined the relationship among demographic variables and reasoning ability of primary school pupils. It drew four hundred pupils from ten (10)…

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

Family strengths, motivation, and resources as predictors of health promotion behavior in single-parent and two-parent families.

PubMed

Ford-Gilboe, M

1997-06-01

The extent to which selected aspects of family health potential (strengths, motivation, and resources) predicted health work (health-related problem-solving and goal attainment behaviors) was examined in a Canadian sample of 138 female-headed single-parent families and two-parent families. The mother and one child (age 10-14) each completed mailed self-report instruments to assess the independent variables of family cohesion, family pride, mother's non-traditional sex role orientation, general self-efficacy, internal health locus of control, network support, community support, and family income, as well as the dependent variable, health work. With the effects of mothers' education held constant, the independent variables predicted 22 to 27% of the variance in health work in the total sample and each family type. Family cohesion was the most consistent predictor of health work, accounting for 8 to 13% of the variance. The findings challenge existing problem-oriented views of single-parent families by focusing on their potential to engage in health promotion behavior.

A Systematic Review of Predictors of, and Reasons for, Adherence to Online Psychological Interventions.

PubMed

Beatty, Lisa; Binnion, Claire

2016-12-01

A key issue regarding the provision of psychological therapy in a self-guided online format is low rates of adherence. The aim of this systematic review was to assess both quantitative and qualitative data on the predictors of adherence, as well as participant reported reasons for adhering or not adhering to online psychological interventions. Database searches of PsycINFO, Medline, and CINAHL identified 1721 potentially relevant articles published between 1 January 2000 and 25 November 2015. A further 34 potentially relevant articles were retrieved from reference lists. Articles that reported predictors of, or reasons for, adherence to an online psychological intervention were included. A total of 36 studies met the inclusion criteria. Predictors assessed included demographic, psychological, characteristics of presenting problem, and intervention/computer-related predictors. Evidence suggested that female gender, higher treatment expectancy, sufficient time, and personalized intervention content each predicted higher adherence. Age, baseline symptom severity, and control group allocation had mixed findings. The majority of assessed variables however, did not predict adherence. Few clear predictors of adherence emerged overall, and most results were either mixed or too preliminary to draw conclusions. More research of predictors associated with adherence to online interventions is warranted.

Family and school environmental predictors of sleep bruxism in children.

PubMed

Rossi, Debora; Manfredini, Daniele

2013-01-01

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

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.

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.

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…

Finding and Developing Moderators and Directional Keys by Regression Analysis.

ERIC Educational Resources Information Center

Kokosh, John

A procedure for rapid screening of variables as potential moderators is presented and discussed. A moderator is defined as any variable which can be used to identify differentially predictable persons; or defined statistically by stating that if a predictor and a moderator are each divided into three or more categories and used as independent…

The potential for technology in brief interventions for substance use, and during-session prediction of computer-delivered brief intervention response.

PubMed

Ondersma, Steven J; Grekin, Emily R; Svikis, Dace

2011-01-01

We first provide an overview of the potential of technology in the area of brief interventions for substance use and describe recent projects from our lab that are illustrative of that potential. Second, we present data from a study of during-session predictors of brief intervention response. In a sample of postpartum women (N = 39), several variables showed promise as predictors of later drug use, and a brief index derived from them predicted abstinence with a sensitivity of .7 and a specificity of .89. This promising approach and initial study findings support the importance of future research in this area.

Predictors of fitness to practise declarations in UK medical undergraduates.

PubMed

Paton, Lewis W; Tiffin, Paul A; Smith, Daniel; Dowell, Jon S; Mwandigha, Lazaro M

2018-04-05

Misconduct during medical school predicts subsequent fitness to practise (FtP) events in doctors, but relatively little is known about which factors are associated with such issues during undergraduate education. This study exploits the newly created UK medical education database (UKMED), with the aim of identifying predictors of conduct or health-related issues that could potentially impair FtP. The findings would have implications for policies related to both the selection and support of medical students. Data were available for 14,379 students obtaining provisional registration with the General Medical Council who started medical school in 2007 and 2008. FtP declarations made by students were available, as were various educational and demographic predictor variables, including self-report 'personality measures' for students who participated in UK Clinical Aptitude Test (UKCAT) pilot studies. Univariable and multivariable logistic regression models were developed to evaluate the predictors of FtP declarations. Significant univariable predictors (p < 0.05) for conduct-related declarations included male gender, white ethnicity and a non-professional parental background. Male gender (OR 3.07) and higher 'self-esteem' (OR 1.45) were independently associated with an increased risk of a conduct issue. Female gender, a non-professional background, and lower self-reported 'confidence' were, among others, associated with increased odds of a health-related declaration. Only 'confidence' was a significant independent predictor of a health declaration (OR 0.69). Female gender, higher UKCAT score, a non-professional background and lower 'confidence' scores were significant predictors of reported depression, and the latter two variables were independent predictors of declared depression. White ethnicity and UK nationality were associated with increased odds of both conduct and health-related declarations, as were certain personality traits. Students from non-professional backgrounds may be at increased risk of depression and therefore could benefit from targeted support. The small effect sizes observed for the 'personality measures' suggest they would offer little potential benefit for selection, over and above those measures already in use.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed Central

2014-01-01

Background Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents’ suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. Methods In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Results Three parental variables showed a relevant association with suicide attempts in adolescents – (all protective): mother’s warmth and father’s warmth in childhood and mother’s control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Conclusions Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk – as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD. PMID:24766881

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.

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

Prior Interpersonal Violence Exposure and Experiences During and After a Disaster as Predictors of Posttraumatic Stress Disorder and Depression Among Adolescent Victims of the Spring 2011 Tornadoes.

PubMed

Resnick, Heidi; Zuromski, Kelly L; Galea, Sandro; Price, Matthew; Gilmore, Amanda K; Kilpatrick, Dean G; Ruggiero, Kenneth

2017-07-01

The purpose of the current report was to examine prior history of exposure to interpersonal violence (IPV), as compared with prior accident or prior disaster exposure, experiences during and after a disaster, and demographic variables as predictors of past month posttraumatic stress disorder (PTSD) and depression severity among adolescents exposed to the tornadoes in Alabama and Missouri. IPV exposure has been consistently identified as a unique category of potentially traumatic events (PTE) that significantly increases risk for development of PTSD and other difficulties relative to other event types among adolescents. A population-based sample of adolescents and caregivers ( N = 2,000) were recruited randomly from tornado-affected communities in Alabama and Joplin, Missouri. Participants completed structured telephone interviews on an average of 8.8 months posttornado. Prior history of IPV was prevalent (36.5%), as was reported history of accidents (25.9%) and prior disaster exposure (26.9%). Negative binomial regression analyses with PTSD and depression symptom counts for past month as outcome variables indicated that history of predisaster IPV was most robustly related to PTSD and depression symptoms, such that those with a history of IPV endorsed over 3 times the number of symptoms than those without IPV history. Final model statistics indicated that female gender, physical injury to caregiver, concern about others' safety, prior disaster, prior accident, and prior IPV exposure were also related to PTSD. Predictors of depression symptoms were similar with the exception that concern about others' safety was not a predictor and age was a predictor in the final model. It is important to evaluate potential additive effects of IPV history in addition to recent disaster exposure variables and to consider such history when developing interventions aimed to reduce or prevent symptoms of PTSD and depression among adolescents recently exposed to disaster.

[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.

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…

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.

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

Twenty-six years of post-release monitoring of Florida manatees (Trichechus manatus latirostris): evaluation of a cooperative rehabilitation program

USGS Publications Warehouse

Adimey, Nicole M.; Ross, Monica; Hall, Madison; Reid, James P.; Barlas, Margie E.; Keith Diagne, Lucy W; Bonde, Robert K.

2016-01-01

The rescue, rehabilitation, and release of Florida manatees (Trichechus manatus latirostris) into the wild has occurred since 1974; however, a comprehensive evaluation of the outcomes of the releases has never been conducted. Herein, we examined data for 136 Florida manatees that were rehabilitated and released with telemetry tags between 1988 and 2013 to determine release outcome of each individual as either success (acclimation) or failure after at least 1 y. Ten predictor variables were statistically evaluated for potential relationships to release outcome. To assess the contribution of each predictor variable to release outcome, each variable was tested for significance in univariate analyses. Manatees born in captivity experienced poor success after release (14%), whereas the overall success of wild-born individuals was higher (72%). When compared with other variables in our dataset, number of days in captivity was the strongest predictor for determining success. Manatees rescued as calves and held in captivity for more than 5 y had a high likelihood of failure, while subadults and adults had a high likelihood of success, regardless of the amount of time spent in captivity. Ensuring the success of individual manatees after release is critical for evaluating the contribution of the manatee rehabilitation program to the growth of the wild population.

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

Decision Making Configurations: An Alternative to the Centralization/Decentralization Conceptualization.

ERIC Educational Resources Information Center

Cullen, John B.; Perrewe, Pamela L.

1981-01-01

Used factors identified in the literature as predictors of centralization/decentralization as potential discriminating variables among several decision making configurations in university affiliated professional schools. The model developed from multiple discriminant analysis had reasonable success in classifying correctly only the decentralized…

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.

Predictors of maternal responsiveness.

PubMed

Drake, Emily E; Humenick, Sharron S; Amankwaa, Linda; Younger, Janet; Roux, Gayle

2007-01-01

To explore maternal responsiveness in the first 2 to 4 months after delivery and to evaluate potential predictors of maternal responsiveness, including infant feeding, maternal characteristics, and demographic factors such as age, socioeconomic status, and educational level. A cross-sectional survey design was used to assess the variables of maternal responsiveness, feeding patterns, and maternal characteristics in a convenience sample of 177 mothers in the first 2 to 4 months after delivery. The 60-item self-report instrument included scales to measure maternal responsiveness, self-esteem, and satisfaction with life as well as infant feeding questions and sociodemographic items. An online data-collection strategy was used, resulting in participants from 41 U.S. states. Multiple regression analysis showed that satisfaction with life, self-esteem, and number of children, but not breastfeeding, explained a significant portion of the variance in self-reported maternal responsiveness scores. In this analysis, sociodemographic variables such as age, education, income, and work status showed little or no relationship to maternal responsiveness scores. This study provides additional information about patterns of maternal behavior in the transition to motherhood and some of the variables that influence that transition. Satisfaction with life was a new predictor of maternal responsiveness. However, with only 15% of the variance explained by the predictors in this study, a large portion of the variance in maternal responsiveness remains unexplained. Further research in this area is needed.

Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia

PubMed Central

Chalghaf, Bilel; Chlif, Sadok; Mayala, Benjamin; Ghawar, Wissem; Bettaieb, Jihène; Harrabi, Myriam; Benie, Goze Bertin; Michael, Edwin; Salah, Afif Ben

2016-01-01

Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk. PMID:26856914

Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia.

PubMed

Chalghaf, Bilel; Chlif, Sadok; Mayala, Benjamin; Ghawar, Wissem; Bettaieb, Jihène; Harrabi, Myriam; Benie, Goze Bertin; Michael, Edwin; Salah, Afif Ben

2016-04-01

Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk. © The American Society of Tropical Medicine and Hygiene.

Generated effect modifiers (GEM’s) in randomized clinical trials

PubMed Central

Petkova, Eva; Tarpey, Thaddeus; Su, Zhe; Ogden, R. Todd

2017-01-01

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an “effect modifier”. Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration. PMID:27465235

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.

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.

Predisposition for Empathy, Intercultural Sensitivity, and Intentions for Using Motivational Interviewing in First Year Pharmacy Students

PubMed Central

Kavookjian, Jan; Hutchison, Amber

2017-01-01

Objective. To assess first-year pharmacy (P1) students’ predispositions (eg, perceptions for empathy, intercultural sensitivity, and motivational interviewing (MI) as a patient-centered communication skillset) and identify potential curricula content/communication skills training needs. Methods. A cross-sectional survey was used to collect students’ self-reported perceptions for empathy, intercultural sensitivity, counseling contexts, and projected future MI use. Relationships between variables were explored and logistic regression was used to evaluate intention for using MI in future patient encounters. Results. There were 134 students who participated. Higher predisposition for empathy and for intercultural sensitivity were significantly correlated. Significant predictors for applying MI in future patient encounters were sex, confidence with counseling skills, and current use of MI. Conclusion. Results suggest the need to incorporate innovative training strategies in communication skills curricula. Potential areas include empathy, intercultural sensitivity and significant predictor variables for future MI use. Further investigation in other schools is needed. PMID:29200452

Predisposition for Empathy, Intercultural Sensitivity, and Intentions for Using Motivational Interviewing in First Year Pharmacy Students.

PubMed

Ekong, Gladys; Kavookjian, Jan; Hutchison, Amber

2017-10-01

Objective. To assess first-year pharmacy (P1) students' predispositions (eg, perceptions for empathy, intercultural sensitivity, and motivational interviewing (MI) as a patient-centered communication skillset) and identify potential curricula content/communication skills training needs. Methods. A cross-sectional survey was used to collect students' self-reported perceptions for empathy, intercultural sensitivity, counseling contexts, and projected future MI use. Relationships between variables were explored and logistic regression was used to evaluate intention for using MI in future patient encounters. Results. There were 134 students who participated. Higher predisposition for empathy and for intercultural sensitivity were significantly correlated. Significant predictors for applying MI in future patient encounters were sex, confidence with counseling skills, and current use of MI. Conclusion. Results suggest the need to incorporate innovative training strategies in communication skills curricula. Potential areas include empathy, intercultural sensitivity and significant predictor variables for future MI use. Further investigation in other schools is needed.

The Sociophonetic and Acoustic Vowel Dynamics of Michigan's Upper Peninsula English

NASA Astrophysics Data System (ADS)

Rankinen, Wil A.

The present sociophonetic study examines the English variety in Michigan's Upper Peninsula (UP) based upon a 130-speaker sample from Marquette County. The linguistic variables of interest include seven monophthongs and four diphthongs: 1) front lax, 2) low back, and 3) high back monophthongs and 4) short and 5) long diphthongs. The sample is stratified by the predictor variables of heritage-location, bilingualism, age, sex and class. The aim of the thesis is two fold: 1) to determine the extent of potential substrate effects on a 71-speaker older-aged bilingual and monolingual subset of these UP English speakers focusing on the predictor variables of heritage-location and bilingualism, and 2) to determine the extent of potential exogenous influences on an 85-speaker subset of UP English monolingual speakers by focusing on the predictor variables of heritage-location, age, sex and class. All data were extracted from a reading passage task collected during a sociolinguistic interview and measured instrumentally. The findings of this apparent-time data reveal the presence of lingering effects from substrate sources and developing effects from exogenous sources based upon American and Canadian models of diffusion. The linguistic changes-in-progress from above, led by middle-class females, are taking shape in the speech of UP residents of whom are propagating linguistic phenomena typically associated with varieties of Canadian English (i.e., low-back merger, Canadian shift, and Canadian raising); however, the findings also report resistance of such norms by working-class females. Finally, the data also reveal substrate effects demonstrating cases of dialect leveling and maintenance. As a result, the speech spoken in Michigan's Upper Peninsula can presently be described as a unique variety of English comprised of lingering substrate effects as well as exogenous effects modeled from both American and Canadian English linguistic norms.

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 motivation for abstinence at the end of outpatient substance abuse treatment

PubMed Central

Laudet, Alexandre B.; Stanick, Virginia

2010-01-01

Commitment to abstinence, a motivational construct, is a strong predictor of reductions in drug and alcohol use. Level of commitment to abstinence at treatment end predicts sustained abstinence, a requirement for recovery. This study sought to identify predictors of commitment to abstinence at treatment end to guide clinical practice and to inform the conceptualization of motivational constructs. Polysubstance users (N = 250) recruited at the start of outpatient treatment were re-interviewed at the end of services. Based on the extant literature, potential predictors were during treatment measures of substance use and related cognitions, psychological functioning, recovery supports, stress, quality of life satisfaction, and treatment experiences. In multivariate analyses, perceived harm of future drug use, abstinence self-efficacy, quality of life satisfaction, and number of network members in 12-step recovery contributed 26.6% of the variance explained in the dependent variable, a total of 49.6% when combined with the control variables (demographics and baseline level of the outcome). Gender subgroup analyses yielded largely similar results. Clinical implications of findings for maximizing commitment to abstinence when clients leave treatment are discussed as are future research directions. PMID:20185267

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

PubMed Central

Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

2013-01-01

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

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…

Beat-to-beat variability of cardiac action potential duration: underlying mechanism and clinical implications.

PubMed

Nánási, Péter P; Magyar, János; Varró, András; Ördög, Balázs

2017-10-01

Beat-to-beat variability of cardiac action potential duration (short-term variability, SV) is a common feature of various cardiac preparations, including the human heart. Although it is believed to be one of the best arrhythmia predictors, the underlying mechanisms are not fully understood at present. The magnitude of SV is basically determined by the intensity of cell-to-cell coupling in multicellular preparations and by the duration of the action potential (APD). To compensate for the APD-dependent nature of SV, the concept of relative SV (RSV) has been introduced by normalizing the changes of SV to the concomitant changes in APD. RSV is reduced by I Ca , I Kr , and I Ks while increased by I Na , suggesting that ion currents involved in the negative feedback regulation of APD tend to keep RSV at a low level. RSV is also influenced by intracellular calcium concentration and tissue redox potential. The clinical implications of APD variability is discussed in detail.

The Niño1+2 region and the Niño4 region predictability.

NASA Astrophysics Data System (ADS)

Miguel, Tasambay-Salazar; Jose, Ortizbevia Maria; Francisco Jose, Alvarez-Garcia; Antonio, Ruizdeelvira

2016-04-01

The El Niño-Southern Oscillation variability is monitored basically by the the Niño3.4 Index. In addition, the Niño1+2 and the Niño4 Indexes are also used to characterise ENSO variability, by reason of their relationships with some of the variability of the neighboring regions, like the air temperature in South America or Australia. However, with the increased length of the available instrumental ENSO records, the need of considering the two different ENSO types identified, Eastern Pacific (EP) or Central Pacific (CP), has become more evident. (Yu and Kim 2013). While the Nino3.4 Index is used to monitor the EP events, the CP events are currently identified by removing from the Niño4 Index the variability associated to the Niño1+2 Index (Kao and Yu 2009). Therefore there is a renewed interest on the predictability of both Indexes. In this study we focus on the predictability of the Niño1+2 region variability and those of the Niño4 region, in the recent post-satellital period. We develop a methodology to identify potential predictors among climate modes, represented by their respective indexes. Among the tropical predictors tested we include the most commonly used,like the Southern Oscillation Index or the Warm Water Volume in the equatorial Pacific (WWV) Index, but also some whose part in the ENSO generation and evolution has been pointed only recently, like the Pacific Meridional Mode (PMM) Index or the North Tropical Zonal Gradient and South Tropical Zonal Gradient Indexes.We also include in our study some other tropical Indexes outside the Pacific basin, like the Tropical North Atlantic, the Tropical South Atlantic and the Indian Ocean Dipole Indexes. We use a seasonal approach, based in a linear statistical relationship and focus on leads going from one season to one year. In the case of the Niño1+2 Index, the number of potential predictors is much higher in spring, followed by winter and summer and last of all autumn. The potential predictor most frequently selected is the WWV Index, tied up with persistence. The other predictors consistently selected are the Pacific Meridional Mode (PMM) Index and the Tropical South Atlantic (TSA) Index. The skill values scored by the Niño4 Index hindcast experiments have many features in common with those found for the Niño3.4 case, as for instance the seasonal dependence on the target month. Here also the WWV is the most frequently selected predictor. The PMM comes in the second place. References Kao HY, Yu JY (2009) Contrasting Eastern-Pacific and Central Pacific types of ENSO.J Clim 22:615-632. doi: 10.1175/2008JCLI. Lagos P, Silva Y, Nickl E, Mosquera K (2008) El Niño-related precipitation variability in Peru.Adv. Geosci., 14, 331-337. Yu JY, Kim ST (2013) Identifying the types of major El Niño Events since 1870. Int J Climatol 33:2105-2112. doi: 10.1002/joc.3575. Tasambay-Salazar, M.; Ortiz Beviá, M. J.; Alvarez-García, F. J.; RuizdeElvira Serra, A.. An estimation of ENSO predictability from its seasonal teleconnections. Theoretical and Applied Climatology. 2015, doi: 10.1007/s00704-015-1546-3

Predictors of Full Enteral Feeding Achievement in Very Low Birth Weight Infants

PubMed Central

Corvaglia, Luigi; Fantini, Maria Pia; Aceti, Arianna; Gibertoni, Dino; Rucci, Paola; Baronciani, Dante; Faldella, Giacomo

2014-01-01

Background To elucidate the role of prenatal, neonatal and early postnatal variables in influencing the achievement of full enteral feeding (FEF) in very low birth weight (VLBW) infants and to determine whether neonatal intensive care units (NICUs) differ in this outcome. Methods Population-based retrospective cohort study using data on 1,864 VLBW infants drawn from the “Emilia-Romagna Perinatal Network” Registry from 2004 to 2009. The outcome of interest was time to FEF achievement. Eleven prenatal, neonatal and early postnatal variables and the study NICUs were selected as potential predictors of time to FEF. Parametric survival analysis was used to model time to FEF as a function of the predictors. Marginal effects were used to obtain adjusted estimates of median time to FEF for specific subgroups of infants. Results Lower gestational age, exclusive formula feeding, higher CRIB II score, maternal hypertension, cesarean delivery, SGA and PDA predicted delayed FEF. NICUs proved to be heterogeneous in terms of FEF achievement. Newborns with PDA had a 4.2 days longer predicted median time to FEF compared to those without PDA; newborns exclusively formula-fed had a 1.4 days longer time to FEF compared to those fed human milk. Conclusions The results of our study suggest that time to FEF is influenced by clinical variables and NICU-specific practices. Knowledge of the variables associated with delayed/earlier FEF achievement could help in improving specific aspects of routine clinical management of VLBW infants and to reduce practice variability. PMID:24647523

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.

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

Time-in-a-bottle (TIAB): a longitudinal, correlational study of patterns, potential predictors, and outcomes of immunosuppressive medication adherence in adult kidney transplant recipients.

PubMed

Russell, Cynthia L; Ashbaugh, Catherine; Peace, Leanne; Cetingok, Muammer; Hamburger, Karen Q; Owens, Sarah; Coffey, Deanna; Webb, Andrew W; Hathaway, Donna; Winsett, Rebecca P; Madsen, Richard; Wakefield, Mark R

2013-01-01

This study examined patterns, potential predictors, and outcomes of immunosuppressive medication adherence in a convenience sample of 121 kidney transplant recipients aged 21 yr or older from three kidney transplant centers using a theory-based, descriptive, correlational, longitudinal design. Electronic monitoring was conducted for 12 months using electronic monitoring. Participants were persistent in taking their immunosuppressive medications, but execution, which includes both taking and timing, was poor. Older age was the only demographic variable associated with medication adherence (r = 0.25; p = 0.005). Of the potential predictors examined, only medication self-efficacy was associated with medication non-adherence, explaining about 9% of the variance (r = 0.31, p = 0.0006). The few poor outcomes that occurred were not significantly associated with medication non-adherence, although the small number of poor outcomes may have limited our ability to detect a link. Future research should test fully powered, theory-based, experimental interventions that include a medication self-efficacy component. © 2013 John Wiley & Sons A/S.

Time-in-a-Bottle (TIAB): A Longitudinal, Correlational Study of Patterns, Potential Predictors, and Outcomes of Immunosuppressive Medication Adherence in Adult Kidney Transplant Recipients

PubMed Central

Russell, Cynthia L.; Ashbaugh, Catherine; Peace, Leanne; Cetingok, Muammer; Hamburger, Karen Q.; Owens, Sarah; Coffey, Deanna; Webb, Andrew; Hathaway, Donna; Winsett, Rebecca P.; Madsen, Richard; Wakefield, Mark R.

2013-01-01

This study examined patterns, potential predictors, and outcomes of immunosuppressive medication adherence in a convenience sample of 121 kidney transplant recipients aged 21 years or older from three kidney transplant centers using a theory-based, descriptive, correlational, longitudinal design. Electronic monitoring was conducted for 12 months using the Medication Event Monitoring System. Participants were persistent in taking their immunosuppressive medications, but execution, which includes both taking and timing, was poor. Older age was the only demographic variable associated with medication adherence (r = 0.25; p = 0.005). Of the potential predictors examined, only medication self-efficacy was associated with medication non-adherence, explaining about 9% of the variance (r = 0.31, p = 0.0006). The few poor outcomes that occurred were not significantly associated with medication non-adherence, although the small number of poor outcomes may have limited our ability to detect a link. Future research should test fully powered, theory-based, experimental interventions that include a medication self-efficacy component. PMID:24093614

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Chattopadhyay, Goutami

2012-10-01

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

The Predictive Validity of the Minnesota Reading Assessment for Students in Postsecondary Vocational Education Programs.

ERIC Educational Resources Information Center

Brown, James M.; Chang, Gerald

1982-01-01

The predictive validity of the Minnesota Reading Assessment (MRA) when used to project potential performance of postsecondary vocational-technical education students was examined. Findings confirmed the MRA to be a valid predictor, although the error in prediction varied between the criterion variables. (Author/GK)

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

Early symptom burden predicts recovery after sport-related concussion

PubMed Central

Mannix, Rebekah; Monuteaux, Michael C.; Stein, Cynthia J.; Bachur, Richard G.

2014-01-01

Objective: To identify independent predictors of and use recursive partitioning to develop a multivariate regression tree predicting symptom duration greater than 28 days after a sport-related concussion. Methods: We conducted a prospective cohort study of patients in a sports concussion clinic. Participants completed questionnaires that included the Post-Concussion Symptom Scale (PCSS). Participants were asked to record the date on which they last experienced symptoms. Potential predictor variables included age, sex, score on symptom inventories, history of prior concussions, performance on computerized neurocognitive assessments, loss of consciousness and amnesia at the time of injury, history of prior medical treatment for headaches, history of migraines, and family history of concussion. We used recursive partitioning analysis to develop a multivariate prediction model for identifying athletes at risk for a prolonged recovery from concussion. Results: A total of 531 patients ranged in age from 7 to 26 years (mean 14.6 ± 2.9 years). The mean PCSS score at the initial visit was 26 ± 26; mean time to presentation was 12 ± 5 days. Only total score on symptom inventory was independently associated with symptoms lasting longer than 28 days (adjusted odds ratio 1.044; 95% confidence interval [CI] 1.034, 1.054 for PCSS). No other potential predictor variables were independently associated with symptom duration or useful in developing the optimal regression decision tree. Most participants (86%; 95% CI 80%, 90%) with an initial PCSS score of <13 had resolution of their symptoms within 28 days of injury. Conclusions: The only independent predictor of prolonged symptoms after sport-related concussion is overall symptom burden. PMID:25381296

Early symptom burden predicts recovery after sport-related concussion.

PubMed

Meehan, William P; Mannix, Rebekah; Monuteaux, Michael C; Stein, Cynthia J; Bachur, Richard G

2014-12-09

To identify independent predictors of and use recursive partitioning to develop a multivariate regression tree predicting symptom duration greater than 28 days after a sport-related concussion. We conducted a prospective cohort study of patients in a sports concussion clinic. Participants completed questionnaires that included the Post-Concussion Symptom Scale (PCSS). Participants were asked to record the date on which they last experienced symptoms. Potential predictor variables included age, sex, score on symptom inventories, history of prior concussions, performance on computerized neurocognitive assessments, loss of consciousness and amnesia at the time of injury, history of prior medical treatment for headaches, history of migraines, and family history of concussion. We used recursive partitioning analysis to develop a multivariate prediction model for identifying athletes at risk for a prolonged recovery from concussion. A total of 531 patients ranged in age from 7 to 26 years (mean 14.6 ± 2.9 years). The mean PCSS score at the initial visit was 26 ± 26; mean time to presentation was 12 ± 5 days. Only total score on symptom inventory was independently associated with symptoms lasting longer than 28 days (adjusted odds ratio 1.044; 95% confidence interval [CI] 1.034, 1.054 for PCSS). No other potential predictor variables were independently associated with symptom duration or useful in developing the optimal regression decision tree. Most participants (86%; 95% CI 80%, 90%) with an initial PCSS score of <13 had resolution of their symptoms within 28 days of injury. The only independent predictor of prolonged symptoms after sport-related concussion is overall symptom burden. © 2014 American Academy of Neurology.

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…

Exaggerated Exercise Blood Pressure Response During Treadmill Testing as a Predictor of Future Hypertension in Men: A Longitudinal Study.

PubMed

Jae, Sae Young; Franklin, Barry A; Choo, Jina; Choi, Yoon-Ho; Fernhall, Bo

2015-11-01

The purpose of this study was to evaluate receiver operating characteristic curves to identify optimal cutoff values of exercise systolic blood pressure (SBP) using both peak SBP and relative SBP (peak SBP minus resting SBP) as predictors of future hypertension (HTN). Participants were 3,742 healthy normotensive men who underwent symptom-limited treadmill testing at baseline. Incident HTN was defined as SBP/diastolic blood pressure greater than 140/90 mm Hg and/or diagnosed HTN by a physician. During an average 5-year follow-up, 364 (9.7%) new cases of HTN were observed. The most discriminatory cutoff values for peak SBP and relative SBP for predicting incident HTN were 181 mm Hg (areas under the curve (AUC) = 0.644, sensitivity = 54%, and specificity = 69%) and 52 mm Hg (AUC = 0.549, sensitivity = 64.3%, and specificity = 44.6%), respectively. Participants with peak SBP greater than 181 mm Hg and relative SBP greater than 52 mm Hg had 1.54-fold (95% CI: 1.23-1.93) and 1.44-fold (95% CI: 1.16-1.80) risks of developing HTN after adjusting for potential confounding variables. When these 2 variables were entered simultaneously into the Cox proportional hazards regression model with adjustment for potential confounding variables, only peak SBP (relative risk: 1.39, 95% CI: 1.02-1.89) was a predictor of the development of HTN. The most accurate discriminators for peak and relative SBP during treadmill exercise testing to predict incident HTN were greater than 181 and 52 mm Hg, respectively, in normotensive men. A peak SBP greater than 181 mm Hg during treadmill exercise testing may provide a useful predictor for the development of HTN in clinical practice. © American Journal of Hypertension, Ltd 2015. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Identifying potential academic leaders: Predictors of willingness to undertake leadership roles in an academic department of family medicine.

PubMed

White, David; Krueger, Paul; Meaney, Christopher; Antao, Viola; Kim, Florence; Kwong, Jeffrey C

2016-02-01

To identify variables associated with willingness to undertake leadership roles among academic family medicine faculty. Web-based survey. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with willingness to undertake leadership roles. Department of Family and Community Medicine at the University of Toronto in Ontario. A total of 687 faculty members. Variables related to respondents' willingness to take on various academic leadership roles. Of all 1029 faculty members invited to participate in the survey, 687 (66.8%) members responded. Of the respondents, 596 (86.8%) indicated their level of willingness to take on various academic leadership roles. Multivariable analysis revealed that the predictors associated with willingness to take on leadership roles were as follows: pursuit of professional development opportunities (odds ratio [OR] 3.79, 95% CI 2.29 to 6.27); currently holding at least 1 leadership role (OR 5.37, 95% CI 3.38 to 8.53); a history of leadership training (OR 1.86, 95% CI 1.25 to 2.78); the perception that mentorship is important for one's current role (OR 2.25, 95% CI 1.40 to 3.60); and younger age (OR 0.97, 95% CI 0.95 to 0.99). Willingness to undertake new or additional leadership roles was associated with 2 variables related to leadership experiences, 2 variables related to perceptions of mentorship and professional development, and 1 demographic variable (younger age). Interventions that support opportunities in these areas might expand the pool and strengthen the academic leadership potential of faculty members.

Long-term stability of diurnal salivary cortisol and alpha-amylase secretion patterns.

PubMed

Skoluda, Nadine; La Marca, Roberto; Gollwitzer, Mario; Müller, Andreas; Limm, Heribert; Marten-Mittag, Birgitt; Gündel, Harald; Angerer, Peter; Nater, Urs M

2017-06-01

This study aimed to investigate long-term stability and variability of diurnal cortisol and alpha-amylase patterns. Diurnal cortisol and alpha-amylase secretion patterns were assessed on a single workday with three waves of measurement across a total time period of 24months in 189 participants. Separate hierarchical linear models were analyzed, with and without a number of potential predictor variables (age, BMI, smoking, chronic stress, stress reactivity). While low long-term stability was found in diurnal cortisol, the stability of diurnal alpha-amylase was moderate across the time period of 24months. Several predictor variables had a positive impact on diurnal cortisol and alpha-amylase secretion patterns averaged across waves. Our findings underpin the notion that long-term stability is not necessarily warranted in longitudinal studies. It is important to choose an appropriate study design when attempting to disentangle clinically and biologically relevant changes from naturally occurring variations in diurnal cortisol and alpha-amylase. Copyright © 2017 Elsevier Inc. All rights reserved.

White racial identity, color-blind racial attitudes, and multicultural counseling competence.

PubMed

Johnson, Alex; Jackson Williams, Dahra

2015-07-01

Multicultural counseling competence (awareness, knowledge, and skills) is necessary to provide effective psychotherapy to an increasingly diverse client population (Sue, 2001). Previous research on predictors of competency among White clinicians finds that above having multicultural training, exposure to racially diverse clients, and social desirability, that White racial identity stages predict multicultural counseling competence (Ottavi et al., 1994). Research also suggests that higher color-blind racial attitudes (denying or minimizing racism in society) correlates with less advanced White racial identity stages (Gushue & Constantine, 2007). However, no studies have examined these variables together as they relate to and possibly predict multicultural counseling competence. The current study aims to add to this literature by investigating the effects of these variables together as potential predictors of multicultural counseling competence among (N = 487) White doctoral students studying clinical, counseling, and school psychology. Results of 3 hierarchical multiple regressions found above the effects of social desirability, demographic variables, and multicultural training, that colorblind racial attitudes and White racial identity stages added significant incremental variance in predicting multicultural counseling knowledge, awareness, and skills. These results add to the literature by finding different predictors for each domain of multicultural competence. Implications of the findings for future research and the clinical training of White doctoral trainees are discussed. (c) 2015 APA, all rights reserved).

Potential predictability of a Colombian river flow

NASA Astrophysics Data System (ADS)

Córdoba-Machado, Samir; Palomino-Lemus, Reiner; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the predictability of an important Colombian river (Cauca) has been analysed based on the use of climatic variables as potential predictors. Cauca River is considered one of the most important rivers of Colombia because its basin supports important productive activities related with the agriculture, such as the production of coffee or sugar. Potential relationships between the Cauca River seasonal streamflow anomalies and different climatic variables such as sea surface temperature (SST), precipitation (Pt), temperature over land (Tm) and soil water (Sw) have been analysed for the period 1949-2009. For this end, moving correlation analysis of 30 years have been carried out for lags from one to four seasons for the global SST, and from one to two seasons for South America Pt, Tm and Sw. Also, the stability of the significant correlations have been also studied, identifying the regions used as potential predictors of streamflow. Finally, in order to establish a prediction scheme based on the previous stable correlations, a Principal Component Analysis (PCA) applied on the potential predictor regions has been carried out in order to obtain a representative time series for each predictor field. Significant and stable correlations between the seasonal streamflow and the tropical Pacific SST (El Niño region) are found for lags from one to four (one-year) season. Additionally, some regions in the Indian and Atlantic Oceans also show significant and stable correlations at different lags, highlighting the importance that exerts the Atlantic SST on the hydrology of Colombia. Also significant and stable correlations are found with the Pt, Tm and Sw for some regions over South America, at lags of one and two seasons. The prediction of Cauca seasonal streamflow based on this scheme shows an acceptable skill and represents a relative improvement compared with the predictability obtained using the teleconnection indices associated with El Niño. Keywords: Streamflow, predictability, Cauca, Colombia. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Emotional Intelligence and Personality Traits as Predictors of Occupational Therapy students' Practice Education Performance: A Cross-Sectional Study.

PubMed

Brown, Ted; Williams, Brett; Etherington, Jamie

2016-12-01

This study investigated whether occupational therapy students' emotional intelligence and personality traits are predictive of specific aspects of their fieldwork performance. A total of 114 second and third year undergraduate occupational therapy students (86.6% response rate) completed the Genos Emotional Intelligence Inventory (Genos EI) and the Ten-Item Personality Inventory (TIPI). Fieldwork performance scores were obtained from the Student Practice Evaluation Form Revised (SPEF-R). Linear regressions were completed with the SPEF-R domains being the dependent variables and the Genos EI and TIPI factors being the independent variables. Regression analysis results revealed that the Genos EI subscales of Emotional Management of Others (EMO), Emotional Awareness of Others (EAO), Emotional Expression (EEX) and Emotional Reasoning (ERE) were significant predictors of various domains of students' fieldwork performance. EAO and ERE were significant predictors of students' Communication Skills accounting for 4.6% of its variance. EMO, EAO, EEX and ERE were significant predictors of students' Documentation Skills explaining 6.8% of its variance. EMO was a significant predictor of students' Professional Behaviour accounting for 3.2% of its variance. No TIPI factors were found to be significant predictors of the SPEF-R domains. Occupational therapy students' emotional intelligence was a significant predictor of components of their fieldwork performance while students' personality traits were not. The convenience sampling approach used, small sample size recruited and potential issue of social desirability of the self-reported Genos EI and TIPI data are acknowledged as study limitations. It is recommended that other studies be completed to investigate if any other relevant constructs or factors are predictive of occupational therapy students' fieldwork performance. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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…

Generated effect modifiers (GEM's) in randomized clinical trials.

PubMed

Petkova, Eva; Tarpey, Thaddeus; Su, Zhe; Ogden, R Todd

2017-01-01

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Sociodemographic and Psychiatric Diagnostic Predictors of 3-Year Incidence of DSM-IV Substance Use Disorders among Men and Women in the National Epidemiologic Survey on Alcohol and Related Conditions

PubMed Central

Goldstein, Risë B.; Smith, Sharon M.; Dawson, Deborah A.; Grant, Bridget F.

2016-01-01

Incidence rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women, but information on whether sociodemographic and psychiatric diagnostic predictors of AUD and DUD incidence differ by sex is limited. Using data from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions, sex-specific 3-year incidence rates of AUDs and DUDs among United States adults were compared by sociodemographic variables and baseline psychiatric disorders. Sex-specific logistic regression models estimated odds ratios for prediction of incident AUDs and DUDs, adjusting for potentially confounding baseline sociodemographic and diagnostic variables. Few statistically significant sex differences in predictive relationships were identified and those observed were generally modest. Prospective research is needed to identify predictors of incident DSM-5 AUDs and DUDs and their underlying mechanisms, including whether there is sex specificity by developmental phase, in the role of additional comorbidity in etiology and course, and in outcomes of prevention and treatment. PMID:26727008

Predicting depressive symptoms among the mothers of children with leukaemia: a caregiver stress model perspective.

PubMed

Demirtepe-Saygılı, Dilek; Bozo, Ozlem

2011-05-01

The aim of this study was to find out the predictors of depressive symptoms of mothers of children with leukaemia. The potential predictors were chosen in the light of the caregiver stress model [Pearlin, Mullan, Semple, and Skaff, 1990. Caregiving and the stress process: An overview of concepts and their measures. The Gerontologist, 30(5), 583-594.], which examines the caregiver stress as composed of many factors such as the background variables, primary and secondary stressors, mediators and psychological health outcomes. This study included the caregiving tasks, basic needs of the caregivers and role strain as the stressors; coping strategies and perceived social support as mediators and depressive symptoms as the outcome variable. The participants of this study were 90 mothers of children with leukaemia. The results revealed that the satisfaction level of the basic needs and role strain were the predictors of the depressive symptoms. While emotion-focussed coping and perceived social support mediated the stressors and the depressive symptoms relationship, problem-focussed coping did not. The possible explanations of the results were explored and the implications were discussed.

Prediction of Mental Health Services Use One Year After Regular Referral to Specialized Care Versus Referral to Stepped Collaborative Care.

PubMed

van Orden, Mirjam; Leone, Stephanie; Haffmans, Judith; Spinhoven, Philip; Hoencamp, Erik

2017-04-01

Referral to collaborative mental health care within the primary care setting is a service concept that has shown to be as effective as direct referral to specialized mental health care for patients with common mental disorders. Additionally it is more efficient in terms of lower mental health services use. This post-hoc analysis examines if treatment intensity during 1-year of follow-up can be predicted prospectively by baseline characteristics. With multilevel multivariate regression analyses baseline characteristics were examined as potential predictors of visit counts. Results showed that only the enabling factors service concept and referral delay for treatment had a significant association with mental health visit counts, when outcome was dichotomized in five or more visits. Inclusion of the outcome variable as a count variable confirmed the predictive value of service concept and referral delay, but added marital status as a significant predictor. Overall, enabling factors (service concept and referral delay) seem to be important and dominant predictors of mental health services use.

Patterns of ectopy leading to increased risk of fatal or near-fatal cardiac arrhythmia in patients with depressed left ventricular function after an acute myocardial infarction.

PubMed

Lerma, Claudia; Gorelick, Alexander; Ghanem, Raja N; Glass, Leon; Huikuri, Heikki V

2013-09-01

To identify potential new markers for assessing the risk of sudden arrhythmic events based on a method that captures features of premature ventricular complexes (PVCs) in relation to sinus RR intervals in Holter recordings (heartprint). Holter recordings obtained 6 weeks after acute myocardial infarction from 227 patients with reduced ventricular function (left ventricular ejection fraction ≤ 40%) were used to produce heartprints. Measured indices were: PVCs per hour, standard deviation of coupling interval (SDCI), and the number of occurrences of the most prevalent form of PVCs (SNIB). Predictive values, survival analysis, and Cox regression with adjustment for clinical variables were performed based on primary endpoint, defined as an electrocardiogram-documented fatal or near-fatal arrhythmic event, death from any cause, and cardiac death. High ectopy (PVCs per hour ≥10) was a predictor of all endpoints. Repeating forms of PVCs (SNIB ≥ 83) was a predictor of primary endpoint, hazard ratio = 3.5 (1.3-9.5), and all-cause death, hazard ratio = 2.8 (1.1-7.3), but not cardiac death. SDCI ≤ 80 ms was a predictor of all-cause death and cardiac death, but not of primary endpoint. High ectopy, prevalence of repeating forms of PVCs, and low coupling interval variability are potentially useful risk markers of fatal or near-fatal arrhythmias after myocardial infarction.

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River

PubMed Central

Radinger, Johannes; Wolter, Christian; Kail, Jochem

2015-01-01

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale. PMID:26569119

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.

Predicting Outcome in Computerized Cognitive Behavioral Therapy for Depression in Primary Care: A Randomized Trial

ERIC Educational Resources Information Center

de Graaf, L. Esther; Hollon, Steven D.; Huibers, Marcus J. H.

2010-01-01

Objective: To explore pretreatment and short-term improvement variables as potential moderators and predictors of 12-month follow-up outcome of unsupported online computerized cognitive behavioral therapy (CCBT), usual care, and CCBT combined with usual care for depression. Method: Three hundred and three depressed patients were randomly allocated…

Psychosocial Predictors of Women's Physical Health in Middle Adulthood.

ERIC Educational Resources Information Center

Thomas, Sandra P.

Although health is a key element in one's experience of middle adulthood as a time of productivity and personal fulfillment, research on psychosocial factors predictive of mid-life health is sparse, especially for women. Psychosocial variables are not only highly salient to health, but also are potentially modifiable by women themselves. This…

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

PubMed

Valente, Andrea; Bürki, Audrey; Laganaro, Marina

2014-01-01

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

Predictors of Recurrent Hospital Admission for Patients Presenting With Diabetic Ketoacidosis and Hyperglycemic Hyperosmolar State.

PubMed

Bradford, Annabel L; Crider, Courtney Champagne; Xu, Xizheng; Naqvi, Syed Hasan

2017-01-01

Diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) are two serious, preventable complications of diabetes mellitus. Analysis of variables associated with recurrent DKA and HHS admission has the potential to improve patient outcomes by identifying possible areas for intervention. The aim of this study was to evaluate potential predictors of recurrent DKA or HHS admission. This was a retrospective case-control study of 367 patients presenting during a 5-year period with DKA or HHS at a US tertiary academic medical center. Six potential readmission risk factors identified via literature review were coded as "1" if present and "0" if absent. Readmission odds ratios (ORs) for each risk factor and for the combined score of significant risk factors were calculated by logistic regression. Readmission odds were significantly increased for patients with age < 35, history of depression or substance/alcohol abuse, and self-pay/publicly funded insurance. HbA1C > 10.6% on admission and ethnic minority status did not significantly increase readmission odds, with inadequate study power for these variables. A total "ABCD" score, based on Age (< 35 years), Behavioral health (depression), insurance Coverage (self-pay/publicly funded insurance), and Drug/alcohol abuse, also had a significant effect on readmission odds. Consideration of individual risk factors and the use of a scoring system based on objective predictors of recurrent DKA and HHS admission could be of value in helping identify patients with high readmission risk, allowing interventions to be targeted most effectively to reduce readmission rates, associated morbidity, and mortality.

Body image flexibility: A predictor and moderator of outcome in transdiagnostic outpatient eating disorder treatment.

PubMed

Pellizzer, Mia L; Waller, Glenn; Wade, Tracey D

2018-04-01

Predictors of attrition and predictors and moderators of outcome were explored in a transdiagnostic sample of patients who received ten-session cognitive behavioral therapy (CBT-T) for nonunderweight eating disorders. Body image flexibility, a protective positive body image construct, was hypothesized to be a significant moderator. Data from two case series were combined to form a sample of 78 participants who received CBT-T. Baseline measures of body image, negative affect, personality, and motivation (readiness to change and self-efficacy) were included as potential predictors. Global eating disorder psychopathology at each assessment point (baseline, mid- and post-treatment, 1- and 3-month follow-up) was the outcome variable. Predictors of attrition were assessed using logistic regression, and multilevel modeling was applied for predictors and moderators of outcome. Body image flexibility emerged as the strongest predictor and moderator of global eating disorder psychopathology, followed by body image avoidance. Body checking, negative affect, personality beliefs, and self-efficacy were significant predictors of global eating disorder psychopathology. Higher body image flexibility predicted lower global eating disorder psychopathology at every assessment point. Further research is required to replicate findings and explore the benefit of focusing on positive body image in treatment. © 2018 Wiley Periodicals, Inc.

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.

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…

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

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.

A Systematic Review and Meta-Analysis of the Relationship Between Brain Data and the Outcome in Disorders of Consciousness

PubMed Central

Kotchoubey, Boris; Pavlov, Yuri G.

2018-01-01

A systematic search revealed 68 empirical studies of neurophysiological [EEG, event-related brain potential (ERP), fMRI, PET] variables as potential outcome predictors in patients with Disorders of Consciousness (diagnoses Unresponsive Wakefulness Syndrome [UWS] and Minimally Conscious State [MCS]). Data of 47 publications could be presented in a quantitative manner and systematically reviewed. Insufficient power and the lack of an appropriate description of patient selection each characterized about a half of all publications. In more than 80% studies, neurologists who evaluated the patients’ outcomes were familiar with the results of neurophysiological tests conducted before, and may, therefore, have been influenced by this knowledge. In most subsamples of datasets, effect size significantly correlated with its standard error, indicating publication bias toward positive results. Neurophysiological data predicted the transition from UWS to MCS substantially better than they predicted the recovery of consciousness (i.e., the transition from UWS or MCS to exit-MCS). A meta-analysis was carried out for predictor groups including at least three independent studies with N > 10 per predictor per improvement criterion (i.e., transition to MCS versus recovery). Oscillatory EEG responses were the only predictor group whose effect attained significance for both improvement criteria. Other perspective variables, whose true prognostic value should be explored in future studies, are sleep spindles in the EEG and the somatosensory cortical response N20. Contrary to what could be expected on the basis of neuroscience theory, the poorest prognostic effects were shown for fMRI responses to stimulation and for the ERP component P300. The meta-analytic results should be regarded as preliminary given the presence of numerous biases in the data. PMID:29867725

A Systematic Review and Meta-Analysis of the Relationship Between Brain Data and the Outcome in Disorders of Consciousness.

PubMed

Kotchoubey, Boris; Pavlov, Yuri G

2018-01-01

A systematic search revealed 68 empirical studies of neurophysiological [EEG, event-related brain potential (ERP), fMRI, PET] variables as potential outcome predictors in patients with Disorders of Consciousness (diagnoses Unresponsive Wakefulness Syndrome [UWS] and Minimally Conscious State [MCS]). Data of 47 publications could be presented in a quantitative manner and systematically reviewed. Insufficient power and the lack of an appropriate description of patient selection each characterized about a half of all publications. In more than 80% studies, neurologists who evaluated the patients' outcomes were familiar with the results of neurophysiological tests conducted before, and may, therefore, have been influenced by this knowledge. In most subsamples of datasets, effect size significantly correlated with its standard error, indicating publication bias toward positive results. Neurophysiological data predicted the transition from UWS to MCS substantially better than they predicted the recovery of consciousness (i.e., the transition from UWS or MCS to exit-MCS). A meta-analysis was carried out for predictor groups including at least three independent studies with N > 10 per predictor per improvement criterion (i.e., transition to MCS versus recovery). Oscillatory EEG responses were the only predictor group whose effect attained significance for both improvement criteria. Other perspective variables, whose true prognostic value should be explored in future studies, are sleep spindles in the EEG and the somatosensory cortical response N20. Contrary to what could be expected on the basis of neuroscience theory, the poorest prognostic effects were shown for fMRI responses to stimulation and for the ERP component P300. The meta-analytic results should be regarded as preliminary given the presence of numerous biases in the data.

Therapist Effects on and Predictors of Non-Consensual Dropout in Psychotherapy.

PubMed

Zimmermann, Dirk; Rubel, Julian; Page, Andrew C; Lutz, Wolfgang

2017-03-01

Whereas therapist effects on outcome have been a research topic for several years, the influence of therapists on premature treatment termination (dropout) has hardly been investigated. Since dropout is common during psychological treatment, and its occurrence has important implications for both the individual patient and the healthcare system, it is important to identify the factors associated with it. Participants included 707 patients in outpatient psychotherapy treated by 66 therapists. Multilevel logistic regression models for dichotomous data were used to estimate the impact of therapists on patient dropout. Additionally, sociodemographic variables, symptoms, personality style and treatment expectations were investigated as potential predictors. It was found that 5.7% of variance in dropout could be attributed to therapists. The therapist's effect remained significant after controlling for patient's initial impairment. Furthermore, initial impairment was a predictor of premature termination. Other significant predictors of dropout on a patient level were male sex, lower education status, more histrionic and less compulsive personality style and negative treatment expectations. The findings indicate that differences between therapists influence the likelihood of dropout in outpatient psychotherapy. Further research should focus on variables, which have the potential to explain these inter-individual differences between therapists (e.g., therapist's experience or self-efficacy). Copyright © 2016 John Wiley & Sons, Ltd. There are substantial differences between therapists concerning their average dropout rates. At the patient level, higher initial impairment, male sex, lower education, less compulsive personality style, more histrionic personality style and low treatment expectations seem to be risk factors of non-consensual treatment termination. Psychometric feedback during the course of treatment should be used to identify patients who are at risk for dropout. Copyright © 2016 John Wiley & Sons, Ltd.

Detection of soil erosion within pinyon-juniper woodlands using Thematic Mapper (TM) satellite data

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Ridd, Merrill K.

1991-01-01

The sensitivity of Landsat TM data for detecting soil erosion within pinyon-juniper woodlands, and the potential of the spectral data for assigning the universal soil loss equation (USLE) crop managemnent (C) factor to varying cover types within the woodlands are assessed. Results show greatly accelerated rates of soil erosion on pinyon-juniper sites. Percent cover by pinyon-juniper, total soil-loss, and total nonliving ground cover accounted for nearly 70 percent of the variability in TM channels 2, 3, 4, and 5. TM spectral data were consistently better predictors of soil erosion than the biotic and abiotic field variables. Satellite data were more sensitive to vegetation variation than the USLE C factor, and USLE was found to be a poor predictor of soil loss on pinyon-juniper sites. A new string-to-ground soil erosion prediction technique is introduced.

Causal Attributions as Predictors of Academic Achievement in Father-Absent Children.

ERIC Educational Resources Information Center

Salzman, Stephanie A.

The purpose of this study was to examine the potential impact of maternal attributions and self-attributions on the academic achievement of father-absent children in comparison to commonly identified family interaction and demographic variables. Subjects included 33 male and 34 female father-absent sixth graders (mean age of 11.6 years) and their…

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

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

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

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…

A respiratory alert model for the Shenandoah Valley, Virginia, USA

NASA Astrophysics Data System (ADS)

Hondula, David M.; Davis, Robert E.; Knight, David B.; Sitka, Luke J.; Enfield, Kyle; Gawtry, Stephen B.; Stenger, Phillip J.; Deaton, Michael L.; Normile, Caroline P.; Lee, Temple R.

2013-01-01

Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.

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

PubMed Central

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

2016-01-01

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

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

Predictors of self and parental vaccination decisions in England during the 2009 H1N1 pandemic: Analysis of the Flu Watch pandemic cohort data.

PubMed

Weston, Dale; Blackburn, Ruth; Potts, Henry W W; Hayward, Andrew C

2017-07-05

During the 2009 H1N1 pandemic, UK uptake of the pandemic influenza vaccine was very low. Furthermore, attitudes governing UK vaccination uptake during a pandemic are poorly characterised. To the best of our knowledge, there is no published research explicitly considering predictors of both adult self-vaccination and decisions regarding whether or not to vaccinate one's children among the UK population during the H1N1 pandemic. We therefore aimed to identify predictors of both self-vaccination decisions and parental vaccination decisions using data collected during the H1N1 pandemic as part of the Flu Watch cohort study. Data were analysed separately for 798 adults and 85 children: exploratory factor analysis facilitated reduction of 16 items on attitudes to pandemic vaccine into a smaller number of factors. Single variable analyses with vaccine uptake as the outcome were used to identify variables that were predictive of vaccination in children and adults. Potential predictors were: attitudinal factors created by data reduction, age group, sex, region, deprivation, ethnicity, chronic condition, vocation, healthcare-related occupation and previous influenza vaccination. Consistent with previous literature concerning adult self-vaccination decisions, we found that vaccine efficacy/safety and perceived risk of pandemic influenza were significant predictors of both self-vaccination decisions and parental vaccination decisions. This study provides the first systematic attempt to understand both the predictors of self and parental vaccination uptake among the UK general population during the H1N1 pandemic. Our findings indicate that concerns about vaccine safety, and vaccine effectiveness may be a barrier to increased uptake for both self and parental vaccination. Copyright © 2017. Published by Elsevier Ltd.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-04-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other organic soils are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new dataset comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip well specific long-term annual mean water level (WL) as well as a transformed form of it (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insights into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying e.g. on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-09-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Dissolved organic carbon and its potential predictors in eutrophic lakes.

PubMed

Toming, Kaire; Kutser, Tiit; Tuvikene, Lea; Viik, Malle; Nõges, Tiina

2016-10-01

Understanding of the true role of lakes in the global carbon cycle requires reliable estimates of dissolved organic carbon (DOC) and there is a strong need to develop remote sensing methods for mapping lake carbon content at larger regional and global scales. Part of DOC is optically inactive. Therefore, lake DOC content cannot be mapped directly. The objectives of the current study were to estimate the relationships of DOC and other water and environmental variables in order to find the best proxy for remote sensing mapping of lake DOC. The Boosted Regression Trees approach was used to clarify in which relative proportions different water and environmental variables determine DOC. In a studied large and shallow eutrophic lake the concentrations of DOC and coloured dissolved organic matter (CDOM) were rather high while the seasonal and interannual variability of DOC concentrations was small. The relationships between DOC and other water and environmental variables varied seasonally and interannually and it was challenging to find proxies for describing seasonal cycle of DOC. Chlorophyll a (Chl a), total suspended matter and Secchi depth were correlated with DOC and therefore are possible proxies for remote sensing of seasonal changes of DOC in ice free period, while for long term interannual changes transparency-related variables are relevant as DOC proxies. CDOM did not appear to be a good predictor of the seasonality of DOC concentration in Lake Võrtsjärv since the CDOM-DOC coupling varied seasonally. However, combining the data from Võrtsjärv with the published data from six other eutrophic lakes in the world showed that CDOM was the most powerful predictor of DOC and can be used in remote sensing of DOC concentrations in eutrophic lakes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictors of outcome and methodological issues in children with acute lymphoblastic leukaemia in El Salvador.

PubMed

Bonilla, Miguel; Gupta, Sumit; Vasquez, Roberto; Fuentes, Soad L; deReyes, Gladis; Ribeiro, Raul; Sung, Lillian

2010-12-01

Most children with cancer live in low-income countries (LICs) where risk factors in paediatric acute lymphoblastic leukaemia (ALL) developed in high-income countries may not apply. We describe predictors of survival for children in El Salvador with ALL. We included patients <16 years diagnosed with ALL between January 2001 and July 2007 treated with the El Salvador-Guatemala-Honduras II protocol. Demographic, disease-related, socioeconomic and nutritional variables were examined as potential predictors of event-free survival (EFS) and overall survival (OS). 260/443 patients (58.7%) were classified as standard risk. Standard- and high-risk 5-year EFS were 56.3 ± 4.5% and 48.6 ± 5.5%; 5-year OS were 77.7 ± 3.8% and 61.9 ± 5.8%, respectively. Among standard-risk children, socioeconomic variables such as higher monthly income (hazard ratio [HR] per $100 = 0.84 [95% confidence interval (CI) 0.70-0.99; P=0.04]) and parental secondary education (HR = 0.49, 95% CI 0.29-0.84; P = 0.01) were associated with better EFS. Among high-risk children, higher initial white blood cell (HR per 10×10(9)/L = 1.03, 95% CI 1.02-1.05; P<0.001) predicted worse EFS; socioeconomic variables were not predictive. The difference in EFS and OS appeared related to overestimating OS secondary to poor follow-up after abandonment/relapse. Socioeconomic variables predicted worse EFS in standard-risk children while disease-related variables were predictive in high-risk patients. Further studies should delineate pathways through which socioeconomic status affects EFS in order to design effective interventions. EFS should be the primary outcome in LIC studies. Copyright © 2010 Elsevier Ltd. All rights reserved.

Hypoglycemia in noncritically ill patients receiving total parenteral nutrition: a multicenter study. (Study group on the problem of hyperglycemia in parenteral nutrition; Nutrition area of the Spanish Society of Endocrinology and Nutrition).

PubMed

Olveira, Gabriel; Tapia, María José; Ocón, Julia; Cabrejas-Gómez, Carmen; Ballesteros-Pomar, María D; Vidal-Casariego, Alfonso; Arraiza-Irigoyen, Carmen; Olivares, Josefina; Conde-García, Maria Carmen; García-Manzanares, Álvaro; Botella-Romero, Francisco; Quílez-Toboso, Rosa P; Matía, Pilar; Rubio, Miguel Ángel; Chicharro, Luisa; Burgos, Rosa; Pujante, Pedro; Ferrer, Mercedes; Zugasti, Ana; Petrina, Estrella; Manjón, Laura; Diéguez, Marta; Carrera, Ma José; Vila-Bundo, Anna; Urgelés, Juan Ramón; Aragón-Valera, Carmen; Sánchez-Vilar, Olga; Bretón, Irene; García-Peris, Pilar; Muñoz-Garach, Araceli; Márquez, Efren; Del Olmo, Dolores; Pereira, José Luis; Tous, María C

2015-01-01

Hypoglycemia is a common problem among hospitalized patients. Treatment of hyperglycemia with insulin is potentially associated with an increased risk for hypoglycemia. The aim of this study was to determine the prevalence and predictors of hypoglycemia (capillary blood glucose <70 mg/dL) in hospitalized patients receiving total parenteral nutrition (TPN). This prospective multicenter study involved 19 Spanish hospitals. Noncritically ill adults who were prescribed TPN were included, thus enabling us to collect data on capillary blood glucose and insulin dosage. The study included 605 patients of whom 6.8% (n = 41) had at least one capillary blood glucose <70 mg/dL and 2.6% (n = 16) had symptomatic hypoglycemia. The total number of hypoglycemic episodes per 100 d of TPN was 0.82. In univariate analysis, hypoglycemia was significantly associated with the presence of diabetes, a lower body mass index (BMI), and treatment with intravenous (IV) insulin. Patients with hypoglycemia also had a significantly longer hospital length of stay, PN duration, higher blood glucose variability, and a higher insulin dose. Multiple logistic regression analysis showed that a lower BMI, high blood glucose variability, and TPN duration were risk factors for hypoglycemia. Use of IV insulin and blood glucose variability were predictors of symptomatic hypoglycemia. The occurrence of hypoglycemia in noncritically ill patients receiving PN is low. A lower BMI and a greater blood glucose variability and TPN duration are factors associated with the risk for hypoglycemia. IV insulin and glucose variability were predictors of symptomatic hypoglycemia. Copyright © 2015 Elsevier Inc. All rights reserved.

Prevalence and predictors of anaemia in Romanian infants 6-23 months old.

PubMed

Stativa, E; Rus, A V; Stanescu, A; Pennings, J S; Parris, S R; Wenyika, R

2016-09-01

Anaemia is a public health problem that can lead to a variety of detrimental effects on physical and neurodevelopment in young children. The present study explored the epidemiology of anaemia among infants in Romania, identified risk factors and created a model for predicting it. Data from 1532 infants aged 6-24 months were selected from a larger nationally representative cross-sectional survey. Demographic predictor variables and haemoglobin concentration were extant variables in the data set. Multiple logistic regression was used to determine the best predictors of anaemia. Overall, 46% of 6-24 month olds in the sample had anaemia (Hb < 11.0 g/dl). A variety of risk factors were associated with significantly greater odds of anaemia, but a five-factor model best predicted it (67.9% accuracy). These predictors included being male, living in a rural area, being third born or later, being a Hungarian and living in the South, South-West or West region of Romania. While data indicate a modest decrease in anaemia from earlier Romanian studies, it remains a significant problem. Models like this one have the potential to improve identification and treatment of anaemia in young children. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The influence of attention deficits on functional recovery post stroke during the first 12 months after discharge from hospital.

PubMed

Hyndman, D; Pickering, R M; Ashburn, A

2008-06-01

Attention deficits have been linked to poor recovery after stroke and may predict outcome. We explored the influence of attention on functional recovery post stroke in the first 12 months after discharge from hospital. People with stroke completed measures of attention, balance, mobility and activities of daily living (ADL) ability at the point of discharge from hospital, and 6 and 12 months later. We used correlational analysis and stepwise linear regression to explore potential predictors of outcome. We recruited 122 men and women, mean age 70 years. At discharge, 56 (51%) had deficits of divided attention, 45 (37%) of sustained attention, 43 (36%) of auditory selective attention and 41 (37%) had visual selective attention deficits. Attention at discharge correlated with mobility, balance and ADL outcomes 12 months later. After controlling for the level of the outcome at discharge, correlations remained significant in only five of the 12 relationships. Stepwise linear regression revealed that the outcome measured at discharge, days until discharge and number of medications were better predictors of outcome: in no case was an attention variable at discharge selected as a predictor of outcome at 12 months. Although attention and function correlated significantly, this correlation was reduced after controlling for functional ability at discharge. Furthermore, side of lesion and the attention variables were not demonstrated as important predictors of outcome 12 months later.

Self-Regulation and Recall: Growth Curve Modeling of Intervention Outcomes for Older Adults

PubMed Central

West, Robin L.; Hastings, Erin C.

2013-01-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills. PMID:21604891

Self-regulation and recall: growth curve modeling of intervention outcomes for older adults.

PubMed

West, Robin L; Hastings, Erin C

2011-12-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.

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.

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

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

Review article: closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization?

PubMed

Rinehart, Joseph; Liu, Ngai; Alexander, Brenton; Cannesson, Maxime

2012-01-01

Closed-loop (automated) controllers are encountered in all aspects of modern life in applications ranging from air-conditioning to spaceflight. Although these systems are virtually ubiquitous, they are infrequently used in anesthesiology because of the complexity of physiologic systems and the difficulty in obtaining reliable and valid feedback data from the patient. Despite these challenges, closed-loop systems are being increasingly studied and improved for medical use. Two recent developments have made fluid administration a candidate for closed-loop control. First, the further description and development of dynamic predictors of fluid responsiveness provides a strong parameter for use as a control variable to guide fluid administration. Second, rapid advances in noninvasive monitoring of cardiac output and other hemodynamic variables make goal-directed therapy applicable for a wide range of patients in a variety of clinical care settings. In this article, we review the history of closed-loop controllers in clinical care, discuss the current understanding and limitations of the dynamic predictors of fluid responsiveness, and examine how these variables might be incorporated into a closed-loop fluid administration system.

Modeling longitudinal data, I: principles of multivariate analysis.

PubMed

Ravani, Pietro; Barrett, Brendan; Parfrey, Patrick

2009-01-01

Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors' impact on outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic component and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precision around the point estimates (confidence intervals).

Facebook Addiction: Onset Predictors.

PubMed

Biolcati, Roberta; Mancini, Giacomo; Pupi, Virginia; Mugheddu, Valeria

2018-05-23

Worldwide, Facebook is becoming increasingly widespread as a communication platform. Young people especially use this social networking site daily to maintain and establish relationships. Despite the Facebook expansion in the last few years and the widespread acceptance of this social network, research into Facebook Addiction (FA) is still in its infancy. Hence, the potential predictors of Facebook overuse represent an important matter for investigation. This study aimed to deepen the understanding of the relationship between personality traits, social and emotional loneliness, life satisfaction, and Facebook addiction. A total of 755 participants (80.3% female; n = 606) aged between 18 and 40 (mean = 25.17; SD = 4.18) completed the questionnaire packet including the Bergen Facebook Addiction Scale, the Big Five, the short version of Social and Emotional Loneliness Scale for Adults, and the Satisfaction with Life Scale. A regression analysis was used with personality traits, social, family, romantic loneliness, and life satisfaction as independent variables to explain variance in Facebook addiction. The findings showed that Conscientiousness, Extraversion, Neuroticism, and Loneliness (Social, Family, and Romantic) were strong significant predictors of FA. Age, Openness, Agreeableness, and Life Satisfaction, although FA-related variables, were not significant in predicting Facebook overuse. The risk profile of this peculiar behavioral addiction is also discussed.

Anodic microbial community diversity as a predictor of the power output of microbial fuel cells.

PubMed

Stratford, James P; Beecroft, Nelli J; Slade, Robert C T; Grüning, André; Avignone-Rossa, Claudio

2014-03-01

The relationship between the diversity of mixed-species microbial consortia and their electrogenic potential in the anodes of microbial fuel cells was examined using different diversity measures as predictors. Identical microbial fuel cells were sampled at multiple time-points. Biofilm and suspension communities were analysed by denaturing gradient gel electrophoresis to calculate the number and relative abundance of species. Shannon and Simpson indices and richness were examined for association with power using bivariate and multiple linear regression, with biofilm DNA as an additional variable. In simple bivariate regressions, the correlation of Shannon diversity of the biofilm and power is stronger (r=0.65, p=0.001) than between power and richness (r=0.39, p=0.076), or between power and the Simpson index (r=0.5, p=0.018). Using Shannon diversity and biofilm DNA as predictors of power, a regression model can be constructed (r=0.73, p<0.001). Ecological parameters such as the Shannon index are predictive of the electrogenic potential of microbial communities. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

NASA Astrophysics Data System (ADS)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Predictors of the discharge dosage of an atypical antipsychotic agent among hospitalized, treatment-naive, first-episode psychosis patients in naturalistic, public-sector settings.

PubMed

Compton, Michael T; Kelley, Mary E; Lloyd, Robert Brett; McClam, Tamela; Ramsay, Claire E; Haggard, Patrick J; Augustin, Sara

2011-02-01

Little is known about determinants of second-generation antipsychotic dosages during initial hospitalization of first-episode psychosis. This study examined potential predictors of dosage of an atypical antipsychotic agent, risperidone, at hospital discharge after initial evaluation and treatment of first-episode nonaffective psychosis in 3 naturalistic, public-sector treatment settings. The number of psychotropic agents prescribed and discharge antipsychotic dosage were abstracted from the medical record. Demographic and extensive clinical characteristics were assessed through a clinical research study conducted at the 3 sites. One-way analyses of variance, trend tests using specific linear combinations of estimates, and χ² tests assessed for associations between atypical antipsychotic dosage and 5 hypothesized predictors, as well as 12 exploratory variables. Among 155 hospitalized first-episode patients, 121 (78.1%) were discharged on risperidone, and subsequent analyses focused on that subset. The mean risperidone dosage among those 121 patients was 4.26 mg; 31 received 1 to 2 mg, 45 received 3 to 4 mg, 37 received 5 to 6 mg, and 8 received more than 6 mg. Analyses suggested that older age at hospitalization, the number of psychotropic agents prescribed, excited symptoms, and premorbid social functioning may be predictors of the discharge dosage. Although several factors emerged, in general, predictors of discharge dosages of second-generation agents, here exemplified by risperidone, in real-world practice settings remain to be clarified. Given the importance of antipsychotic initiation during first hospitalization, future research should test an even broader array of potential predictors.

What is the predictor of surgical mortality in adult colorectal perforation? The clinical characteristics and results of a multivariate logistic regression analysis.

PubMed

Hsu, Chao-Wen; Wang, Jui-Ho; Kung, Ya-Hsin; Chang, Min-Chi

2017-06-01

Colorectal perforations are a serious condition associated with a high mortality. The aim of this study was to describe the clinical characteristics and identify predictors for the surgical mortality in adult patients with colorectal perforation, thereby achieving better outcomes. A retrospective study of adult patients diagnosed with colorectal perforation operated was performed. The clinical variables that might influence the surgical mortality were first analyzed, and the significant variables were then analyzed using a logistic regression model. A total of 423 patients were identified, and the surgical mortality rate was 36.9 %. The most common etiology was diverticulitis (38.2 %). The highest etiology-specific mortality was for colorectal cancer (61.5 %) and ischemic proctocolitis (59.8 %). In a logistic analysis, the significant predictors for the surgical mortality were ≥3 comorbidities (p = 0.034), preoperation American Society of Anesthesiologists score ≥4 (p = 0.025), preoperative sepsis or septic shock (p < 0.001), colorectal cancer or ischemic proctocolitis (p = 0.035), reoperation (p = 0.041), and Hinchey classification grade IV (p = 0.024). We demonstrated that ≥3 comorbidities, a preoperation American Society of Anesthesiologists score ≥4, preoperative sepsis or septic shock, colorectal cancer or ischemic proctocolitis, reoperation, and Hinchey classification grade IV are predictors for the surgical mortality in the adult cases of colorectal perforation. These predictors should be taken into consideration to prevent surgical mortality and to reduce potentially unnecessary medical expenses.

Parents' Primary Professional Sources of Parenting Advice Moderate Predictors of Parental Attitudes toward Corporal Punishment.

PubMed

Taylor, Catherine A; McKasson, Sarah; Hoy, Guenevere; DeJong, William

2017-02-01

Despite the risk it poses to children's mental and physical health, approval and use of corporal punishment (CP) remains high in the United States. Informed by the Theory of Planned Behavior, we examined potential predictors of attitudes supportive of CP while assessing the moderating effects of parents' (N=500) chosen primary professional source of advice regarding child discipline: pediatricians (47.8%), religious leaders (20.8%), mental health professionals (MHPs) (n=18.4%), or other identified professionals (13.0%). We conducted a random-digit-dial telephone survey among parents ages 18 and over within New Orleans, LA. The main outcome measure was derived from the Attitudes Toward Spanking scale (ATS). The main "predictors" were: perceived injunctive norms (i.e., perceived approval of CP by professionals; and by family and friends), perceived descriptive norms of family and friends regarding CP, and expected outcomes of CP use. We used multivariate OLS models to regress ATS scores on the predictor variables for each subset of parents based on their chosen professional source of advice. Perceived approval of CP by professionals was the strongest predictor of parental attitudes supportive of CP, except for those seeking advice from MHPs. Perceived injunctive and descriptive norms of family and friends were important, but only for those seeking advice from pediatricians or religious leaders. Positive expected outcomes of CP mattered, but only for those seeking advice from religious leaders or MHPs. In conclusion, the strength and relevance of variables predicting attitudes toward CP varied according to the professional from which the parent was most likely to seek advice.

Silent strain of caregiving: exploring the best predictors of distress in family carers of geriatric patients.

PubMed

Bień-Barkowska, Katarzyna; Doroszkiewicz, Halina; Bień, Barbara

2017-01-01

The aim of this article was to identify the best predictors of distress suffered by family carers (FCs) of geriatric patients. A cross-sectional study of 100 FC-geriatric patient dyads was conducted. The negative impact of care (NIoC) subscale of the COPE index was dichotomized to identify lower stress (score of ≤15 on the scale) and higher stress (score of ≥16 on the scale) exerted on FCs by the process of providing care. The set of explanatory variables comprised a wide range of sociodemographic and care-related attributes, including patient-related results from comprehensive geriatric assessments and disease profiles. The best combination of explanatory variables that provided the highest predictive power for distress among FCs in the multiple logistic regression (LR) model was determined according to statistical information criteria. The statistical robustness of the observed relationships and the discriminative power of the model were verified with the cross-validation method. The mean age of FCs was 57.2 (±10.6) years, whereas that of geriatric patients was 81.7 (±6.4) years. Despite the broad initial set of potential explanatory variables, only five predictors were jointly selected for the best statistical model. A higher level of distress was independently predicted by lower self-evaluation of health; worse self-appraisal of coping well as a caregiver; lower sense of general support; more hours of care per week; and the motor retardation of the cared-for person measured with the speed of the Timed Up and Go (TUG) test. Worse performance on the TUG test was only the patient-related predictor of distress among the variables examined as contributors to the higher NIoC. Enhancing the mobility of geriatric patients through suitably tailored kinesitherapeutic methods during their hospital stay may mitigate the burden endured by FCs.

Legume Diversity Patterns in West Central Africa: Influence of Species Biology on Distribution Models

PubMed Central

de la Estrella, Manuel; Mateo, Rubén G.; Wieringa, Jan J.; Mackinder, Barbara; Muñoz, Jesús

2012-01-01

Objectives Species Distribution Models (SDMs) are used to produce predictions of potential Leguminosae diversity in West Central Africa. Those predictions are evaluated subsequently using expert opinion. The established methodology of combining all SDMs is refined to assess species diversity within five defined vegetation types. Potential species diversity is thus predicted for each vegetation type respectively. The primary aim of the new methodology is to define, in more detail, areas of species richness for conservation planning. Methodology Using Maxent, SDMs based on a suite of 14 environmental predictors were generated for 185 West Central African Leguminosae species, each categorised according to one of five vegetation types: Afromontane, coastal, non-flooded forest, open formations, or riverine forest. The relative contribution of each environmental variable was compared between different vegetation types using a nonparametric Kruskal-Wallis analysis followed by a post-hoc Kruskal-Wallis Paired Comparison contrast. Legume species diversity patterns were explored initially using the typical method of stacking all SDMs. Subsequently, five different ensemble models were generated by partitioning SDMs according to vegetation category. Ecological modelers worked with legume specialists to improve data integrity and integrate expert opinion in the interpretation of individual species models and potential species richness predictions for different vegetation types. Results/Conclusions Of the 14 environmental predictors used, five showed no difference in their relative contribution to the different vegetation models. Of the nine discriminating variables, the majority were related to temperature variation. The set of variables that played a major role in the Afromontane species diversity model differed significantly from the sets of variables of greatest relative important in other vegetation categories. The traditional approach of stacking all SDMs indicated overall centers of diversity in the region but the maps indicating potential species richness by vegetation type offered more detailed information on which conservation efforts can be focused. PMID:22911808

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…

Postoperative air leak grading is useful to predict prolonged air leak after pulmonary lobectomy.

PubMed

Oh, Sang Gi; Jung, Yochun; Jheon, Sanghoon; Choi, Yunhee; Yun, Ju Sik; Na, Kook Joo; Ahn, Byoung Hee

2017-01-23

Results of studies to predict prolonged air leak (PAL; air leak longer than 5 days) after pulmonary lobectomy have been inconsistent and are of limited use. We developed a new scale representing the amount of early postoperative air leak and determined its correlation with air leak duration and its potential as a predictor of PAL. We grade postoperative air leak using a 5-grade scale. All 779 lobectomies from January 2005 to December 2009 with available medical records were reviewed retrospectively. We devised six 'SUM' variables using air leak grades in the initial 72 h postoperatively. Excluding unrecorded cases and postoperative broncho-pleural fistulas, there were 720 lobectomies. PAL occurred in 135 cases (18.8%). Correlation analyses showed each SUM variable highly correlated with air leak duration, and the SUM 4to9 , which was the sum of six consecutive values of air leak grades for every 8 h record on postoperative days 2 and 3, was proved to be the most powerful predictor of PAL; PAL could be predicted with 75.7% and 77.7% positive and negative predictive value, respectively, when SUM 4to9  ≥ 16. When 4 predictors derived from multivariable logistic regression of perioperative variables were combined with SUM 4to9 , there was no significant increase in predictability compared with SUM 4to9 alone. This simple new method to predict PAL using SUM 4to9 showed that the amount of early postoperative air leak is the most powerful predictor of PAL, therefore, grading air leak after pulmonary lobectomy is a useful method to predict PAL.

Use of Research-Based Instructional Strategies in Introductory Physics: Where Do Faculty Leave the Innovation-Decision Process?

ERIC Educational Resources Information Center

Henderson, Charles; Dancy, Melissa; Niewiadomska-Bugaj, Magdalena

2012-01-01

During the fall of 2008 a web survey, designed to collect information about pedagogical knowledge and practices, was completed by a representative sample of 722 physics faculty across the United States (50.3% response rate). This paper presents partial results to describe how 20 potential predictor variables correlate with faculty knowledge about…

Job Satisfaction of Female and Male Superintendents: The Influence of Job Facets and Contextual Variables as Potential Predictors

ERIC Educational Resources Information Center

Young, I. Phillip; Kowalski, Theodore J.; McCord, Robert S.; Petersen, George J.

2012-01-01

A descriptive multiple regression approach was used to assess the job satisfaction of female and male public school superintendents taking part in a decennial survey conducted by AASA. Self-reported job satisfaction of public school superintendents was regressed on their affective reactions to specific job facets (supervision, co-workers, and…

Factors Related to Annual Fund-Raising Contributions from Individual Donors to NCAA Division I-A Institutions

ERIC Educational Resources Information Center

Wells, Douglas E.; Southall, Richard M.; Stotlar, David; Mundfrom, Daniel J.

2005-01-01

The purpose of this study was to identify selected factors related to annual fundraising program contributions at National Collegiate Athletic Association (NCAA) Division I-A (D I-A) institutions and develop an equation for estimating an annual fund-raising goal. Based on a review of the literature, 15 potential predictor variables were selected…

Does the Beck Cognitive Insight Scale Predict Response to Cognitive Remediation in Schizophrenia?

PubMed

Benoit, Audrey; Harvey, Philippe-Olivier; Bherer, Louis; Lepage, Martin

2016-01-01

Cognitive remediation therapy (CRT) has emerged as a viable treatment option for people diagnosed with schizophrenia presenting disabling cognitive deficits. However, it is important to determine which variables can influence response to CRT in order to provide cost-effective treatment. This study's aim was to explore cognitive insight as a potential predictor of cognitive improvement after CRT. Twenty patients with schizophrenia completed a 24-session CRT program involving 18 hours of computer exercises and 6 hours of group discussion to encourage generalization of cognitive training to everyday activities. Pre- and posttest assessments included the CogState Research Battery and the Beck Cognitive Insight Scale (BCIS). Lower self-certainty on the BCIS at baseline was associated with greater improvement in speed of processing (r s = -0.48; p < 0.05) and visual memory (r s = -0.46; p < 0.05). The results of this study point out potential associations between self-certainty and cognitive improvement after CRT, a variable that can easily be measured in clinical settings to help evaluate which patients may benefit most from the intervention. They also underline the need to keep investigating the predictors of good CRT outcomes, which can vary widely between 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.

Bayesian effect estimation accounting for adjustment uncertainty.

PubMed

Wang, Chi; Parmigiani, Giovanni; Dominici, Francesca

2012-09-01

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based on specifying two models: (1) the outcome as a function of the exposure and the potential confounders (the outcome model); and (2) the exposure as a function of the potential confounders (the exposure model). We consider Bayesian variable selection on both models and link the two by introducing a dependence parameter, ω, denoting the prior odds of including a predictor in the outcome model, given that the same predictor is in the exposure model. In the absence of dependence (ω= 1), BAC reduces to traditional Bayesian model averaging (BMA). In simulation studies, we show that BAC, with ω > 1, estimates the exposure effect with smaller bias than traditional BMA, and improved coverage. We, then, compare BAC, a recent approach of Crainiceanu, Dominici, and Parmigiani (2008, Biometrika 95, 635-651), and traditional BMA in a time series data set of hospital admissions, air pollution levels, and weather variables in Nassau, NY for the period 1999-2005. Using each approach, we estimate the short-term effects of on emergency admissions for cardiovascular diseases, accounting for confounding. This application illustrates the potentially significant pitfalls of misusing variable selection methods in the context of adjustment uncertainty. © 2012, The International Biometric Society.

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.

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

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.

Do Social Casino Gamers Migrate to Online Gambling? An Assessment of Migration Rate and Potential Predictors.

PubMed

Kim, Hyoun S; Wohl, Michael J A; Salmon, Melissa M; Gupta, Rina; Derevensky, Jeffrey

2015-12-01

Social casino games (i.e., free-to-play online gambling games) are enjoyed by millions of players worldwide on a daily basis. Despite being free to play, social casino games share many similarities to traditional casino games. As such, concerns have been raised as to whether social casino games influences the migration to online gambling among people who have not engaged in such activity (see Griffiths in World Online Gambl 9:12-13, 2010). To date, however, no empirical research has assessed this possibility. Thus, the purpose of the present research was to assess the extent to which social casino gamers migrate to online gambling and potential predictors (time spent on social casino games, skill building, enhancement and micro-transactions) of such migration. To this end, social casino gamers who never gambled online (N = 409) completed a questionnaire battery assessing our variables of interest and were re-contacted 6-months later to see if they had engaged in online gambling during the intervening months. Approximately 26% of social casino gamers reported having migrated to online gambling. Importantly, engagement in micro-transactions was the only unique predictor of migration from social casino gaming to online gambling. The implications for the potential harms associated with social casino gaming are discussed.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Capacity for Physical Activity Predicts Weight Loss After Roux-en-Y Gastric Bypass

PubMed Central

Hatoum, Ida J.; Stein, Heather K.; Merrifield, Benjamin F.; Kaplan, Lee M.

2014-01-01

Despite its overall excellent outcomes, weight loss after Roux-en-Y gastric bypass (RYGB) is highly variable. We conducted this study to identify clinical predictors of weight loss after RYGB. We reviewed charts from 300 consecutive patients who underwent RYGB from August 1999 to November 2002. Data collected included patient demographics, medical comorbidities, and diet history. Of the 20 variables selected for univariate analysis, 9 with univariate P values ≤ 0.15 were entered into a multivariable regression analysis. Using backward selection, covariates with P < 0.05 were retained. Potential confounders were added back into the model and assessed for effect on all model variables. Complete records were available for 246 of the 300 patients (82%). The patient characteristics were 75% female, 93% white, mean age of 45 years, and mean initial BMI of 52.3 kg/m2. One year after surgery, patients lost an average of 64.8% of their excess weight (s.d. = 20.5%). The multivariable regression analysis revealed that limited physical activity, higher initial BMI, lower educational level, diabetes, and decreased attendance at postoperative appointments had an adverse effect on weight loss after RYGB. A model including these five factors accounts for 41% of the observed variability in weight loss (adjusted r2 = 0.41). In this cohort, higher initial BMI and limited physical activity were the strongest predictors of decreased excess weight loss following RYGB. Limited physical activity may be particularly important because it represents an opportunity for potentially meaningful pre- and postsurgical intervention to maximize weight loss following RYGB. PMID:18997674

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.

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

USGS Publications Warehouse

Ransom, Katherine M.; Nolan, Bernard T.; Traum, Jonathan A.; Faunt, Claudia; Bell, Andrew M.; Gronberg, Jo Ann M.; Wheeler, David C.; Zamora, Celia; Jurgens, Bryant; Schwarz, Gregory E.; Belitz, Kenneth; Eberts, Sandra; Kourakos, George; Harter, Thomas

2017-01-01

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

Mapping the potential distribution of the invasive Red Shiner, Cyprinella lutrensis (Teleostei: Cyprinidae) across waterways of the conterminous United States

USGS Publications Warehouse

Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

2012-01-01

Predicting the future spread of non-native aquatic species continues to be a high priority for natural resource managers striving to maintain biodiversity and ecosystem function. Modeling the potential distributions of alien aquatic species through spatially explicit mapping is an increasingly important tool for risk assessment and prediction. Habitat modeling also facilitates the identification of key environmental variables influencing species distributions. We modeled the potential distribution of an aggressive invasive minnow, the red shiner (Cyprinella lutrensis), in waterways of the conterminous United States using maximum entropy (Maxent). We used inventory records from the USGS Nonindigenous Aquatic Species Database, native records for C. lutrensis from museum collections, and a geographic information system of 20 raster climatic and environmental variables to produce a map of potential red shiner habitat. Summer climatic variables were the most important environmental predictors of C. lutrensis distribution, which was consistent with the high temperature tolerance of this species. Results from this study provide insights into the locations and environmental conditions in the US that are susceptible to red shiner invasion.

Predictive displays for a process-control schematic interface.

PubMed

Yin, Shanqing; Wickens, Christopher D; Helander, Martin; Laberge, Jason C

2015-02-01

Our objective was to examine the extent to which increasing precision of predictive (rate of change) information in process control will improve performance on a simulated process-control task. Predictive displays have been found to be useful in process control (as well as aviation and maritime industries). However, authors of prior research have not examined the extent to which predictive value is increased by increasing predictor resolution, nor has such research tied potential improvements to changes in process control strategy. Fifty nonprofessional participants each controlled a simulated chemical mixture process (honey mixer simulation) that simulated the operations found in process control. Participants in each of five groups controlled with either no predictor or a predictor ranging in the resolution of prediction of the process. Increasing detail resolution generally increased the benefit of prediction over the control condition although not monotonically so. The best overall performance, combining quality and predictive ability, was obtained by the display of intermediate resolution. The two displays with the lowest resolution were clearly inferior. Predictors with higher resolution are of value but may trade off enhanced sensitivity to variable change (lower-resolution discrete state predictor) with smoother control action (higher-resolution continuous predictors). The research provides guidelines to the process-control industry regarding displays that can most improve operator performance.

Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

PubMed

Mathewson, Kyle E; Basak, Chandramallika; Maclin, Edward L; Low, Kathy A; Boot, Walter R; Kramer, Arthur F; Fabiani, Monica; Gratton, Gabriele

2012-12-01

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes. Copyright © 2012 Society for Psychophysiological Research.

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.

Substantial soil organic carbon retention along floodplains of mountain streams

NASA Astrophysics Data System (ADS)

Sutfin, Nicholas A.; Wohl, Ellen

2017-07-01

Small, snowmelt-dominated mountain streams have the potential to store substantial organic carbon in floodplain sediment because of high inputs of particulate organic matter, relatively lower temperatures compared with lowland regions, and potential for increased moisture conditions. This work (i) quantifies mean soil organic carbon (OC) content along 24 study reaches in the Colorado Rocky Mountains using 660 soil samples, (ii) identifies potential controls of OC content based on soil properties and spatial position with respect to the channel, and (iii) and examines soil properties and OC across various floodplain geomorphic features in the study area. Stepwise multiple linear regression (adjusted r2 = 0.48, p < 0.001) indicates that percentage of silt and clay, sample depth, percent sand, distance from the channel, and relative elevation from the channel are significant predictors of OC content in the study area. Principle component analysis indicates limited separation between geomorphic floodplain features based on predictors of OC content. A lack of significant differences among floodplain features suggests that the systematic random sampling employed in this study can capture the variability of OC across floodplains in the study area. Mean floodplain OC (6.3 ± 0.3%) is more variable but on average greater than values in uplands (1.5 ± 0.08% to 2.2 ± 0.14%) of the Colorado Front Range and higher than published values from floodplains in other regions, particularly those of larger rivers.

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.

Predictors of physical activity in persons with mental illness: Testing a social cognitive model.

PubMed

Zechner, Michelle R; Gill, Kenneth J

2016-12-01

This study examined whether the social cognitive theory (SCT) model can be used to explain the variance in physical exercise among persons with serious mental illnesses. A cross-sectional, correlational design was employed. Participants from community mental health centers and supported housing programs (N = 120) completed 9 measures on exercise, social support, self-efficacy, outcome expectations, barriers, and goal-setting. Hierarchical regression tested the relationship between self-report physical activity and SCT determinants while controlling for personal characteristics. The model explained 25% of the variance in exercise. Personal characteristics explained 18% of the variance in physical activity, SCT variables of social support, self-efficacy, outcome expectations, barriers, and goals were entered simultaneously, and they added an r2 change value of .07. Gender (β = -.316, p = .001) and Brief Symptom Inventory Depression subscale (β = -2.08, p < .040) contributed significantly to the prediction of exercise. In a separate stepwise multiple regression, we entered only SCT variables as potential predictors of exercise. Goal-setting was the single significant predictor, F(1, 118) = 13.59, p < .01), r2 = .10. SCT shows promise as an explanatory model of exercise in persons with mental illnesses. Goal-setting practices, self-efficacy, outcome expectations and social support from friends for exercise should be encouraged by psychiatric rehabilitation practitioners. People with more depressive symptoms and women exercise less. More work is needed on theoretical exploration of predictors of exercise. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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...

Academic and Demographic Predictors of NCLEX-RN Pass Rates in First- and Second-Degree Accelerated BSN Programs.

PubMed

Kaddoura, Mahmoud A; Flint, Elizabeth P; Van Dyke, Olga; Yang, Qing; Chiang, Li-Chi

Relatively few studies have addressed predictors of first-attempt outcomes (pass-fail) on the National Council Licensure Examination-Registered Nurses (NCLEX-RN) for accelerated BSN programs. The purpose of this study was to compare potential predictors of NCLEX outcomes in graduates of first-degree accelerated (FDA; n=62) and second-degree accelerated (SDA; n=173) BSN programs sharing a common nursing curriculum. In this retrospective study, bivariate analyses and multiple logistic regression assessed significance of selected demographic and academic characteristics as predictors of NCLEX-RN outcomes. FDA graduates were more likely than SDA graduates to fail the NCLEX-RN (P=.0013). FDA graduates were more likely to speak English as a second or additional language (P<.0001), have lower end-of-program GPA and HESI Exit Exam scores (both P<.0001), and have a higher proportions of grades ≤ C (P=.0023). All four variables were significant predictors of NCLEX-RN outcomes within both FDA and SDA programs. The only significant predictors in adjusted logistic regression of NCLEX-RN outcome for the pooled FDA+SDA graduate sample were proportion of grades ≤ C (a predictor of NCLEX-RN failure) and HESI Exit Exam score (a predictor of passing NCLEX-RN). Grades of C or lower on any course may indicate inadequate mastery of critical NCLEX-RN content and increased risk of NCLEX-RN failure. Copyright © 2016 Elsevier Inc. All rights reserved.

The role of traumatic event history in non-medical use of prescription drugs among a nationally representative sample of US adolescents.

PubMed

McCauley, Jenna L; Danielson, Carla Kmett; Amstadter, Ananda B; Ruggiero, Kenneth J; Resnick, Heidi S; Hanson, Rochelle F; Smith, Daniel W; Saunders, Benjamin E; Kilpatrick, Dean G

2010-01-01

Building on previous research with adolescents that examined demographic variables and other forms of substance abuse in relation to non-medical use of prescription drugs (NMUPD), the current study examined potentially traumatic events, depression, posttraumatic stress disorder (PTSD), other substance use, and delinquent behavior as potential correlates of past-year non-medical use of prescription drugs. A nationally representative sample of 3,614 non-institutionalized, civilian, English-speaking adolescents (aged 12-17 years) residing in households with a telephone was selected. Demographic characteristics, traumatic event history, mental health, and substance abuse variables were assessed. NMUPD was assessed by asking if, in the past year, participants had used a prescription drug in a non-medical manner. Multivariable logistic regressions were conducted for each theoretically derived predictor set. Significant predictors from each set were then entered into a final multivariable logistic regression to determine significant predictors of past-year NMUPD. NMUPD was endorsed by 6.7% of the sample (n = 242). The final multivariable model showed that lifetime history of delinquent behavior, other forms of substance use/abuse, history of witnessed violence, and lifetime history of PTSD were significantly associated with increased likelihood of NMUPD. Risk reduction efforts targeting NMUPD among adolescents who have witnessed significant violence, endorsed abuse of other substances and delinquent behavior, and/or endorsed PTSD are warranted. Interventions for adolescents with history of violence exposure or PTSD, or those adjudicated for delinquent behavior, should include treatment or prevention modules that specifically address NMUPD.

Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning.

PubMed

Alkoby, O; Abu-Rmileh, A; Shriki, O; Todder, D

2018-05-15

Despite the success of neurofeedback treatment in many cases, the variability in the efficacy of the treatment is high, and some studies report that a significant proportion of subjects does not benefit from it. Quantifying the extent of this problem is difficult, as many studies do not report the variability among subjects. Nonetheless, the ability to identify in advance those subjects who are - or who are not - likely to benefit from neurofeedback is an important issue, which is only now starting to gain attention. Here, we review the problem of inefficacy in neurofeedback treatment as well as possible psychological and neurophysiological predictors for successful treatment. A possible explanation for treatment ineffectiveness lies in the necessity to adapt the treatment protocol to the individual subject. We therefore discuss the use of personalized neurofeedback protocols as a potential way to reduce the inefficacy problem. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Therapist effects on dropout from a college counseling center practice research network.

PubMed

Xiao, Henry; Castonguay, Louis G; Janis, Rebecca A; Youn, Soo Jeong; Hayes, Jeffrey A; Locke, Benjamin D

2017-07-01

Dropout has been a pervasive and costly problem in psychotherapy, particularly for college counseling centers. The present study examined potential predictors of dropout using a large data set (N = 10,147 clients, 481 therapists) that was gathered through a college counseling center practice research network as a replication and extension of recent findings regarding therapist effects on dropout. The final model resulted in a dropout rate of 15.9% and a therapist effect of 9.51% on dropout variance. Therapist demographic variables were investigated, though none were found to be significant. Variables found to be predictive of increased likelihood of dropping out included higher levels of general presenting concerns, alcohol-related distress, and current financial stress. Ultimately, this study showed that therapists may play an important role in the likelihood of client dropout, and that additional research should be conducted to identify additional predictors, particularly at the therapist and center level. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Pretreatment attrition from couple therapy for male drug abusers.

PubMed

Kelly, Shalonda; Epstein, Elizabeth E; McCrady, Barbara S

2004-01-01

This study tracked pretreatment attrition of 120 callers, 84 of whom were potentially eligible for outpatient couple treatment for male drug abuse. Demographic, significant other, substance use, and access related variables were examined as predictors of intake and treatment entry. Results were similar to other findings regarding variables associated with initiation of individual substance use treatment, and 29% of eligible callers entered treatment. Men whose partners did not use substances or who used in moderation were more likely to attend the intake session, and couples who received referrals were more likely to enter treatment than those who responded to a newspaper advertisement.

Youth With Epilepsy: Development of a Model of Children's Attitudes Toward Their Condition

PubMed Central

Austin, Joan K.; Dunn, David W.; Perkins, Susan M.; Shen, Jianzhao

2006-01-01

A model of children's attitudes toward their epilepsy was tested in 173 children (9–14 years) with epilepsy and their parents. Predictor variables tested were child characteristics, family mastery, child worry, child self-efficacy for seizure management, child psychosocial care needs, and seizure variables. Data were analyzed using structural equation modeling, leading to a revised model in which less child worry, greater family mastery, and greater child seizure self-efficacy were directly related to more child positive attitudes. Discussion focuses on potential targets for psychosocial interventions aimed at improving attitudes toward epilepsy. PMID:17075611

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...

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.

Patient predictors of response to cognitive behaviour therapy and schema therapy for depression.

PubMed

Carter, Janet D; McIntosh, Virginia Vw; Jordan, Jennifer; Porter, Richard J; Douglas, Katie; Frampton, Christopher M; Joyce, Peter R

2018-01-01

Few studies have examined differential predictors of response to psychotherapy for depression. Greater understanding about the factors associated with therapeutic response may better enable therapists to optimise response by targeting therapy for the individual. The aim of the current exploratory study was to examine patient characteristics associated with response to cognitive behaviour therapy and schema therapy for depression. Participants were 100 outpatients in a clinical trial randomised to either cognitive behaviour therapy or schema therapy. Potential predictors of response examined included demographic, clinical, functioning, cognitive, personality and neuropsychological variables. Individuals with chronic depression and increased levels of pre-treatment negative automatic thoughts had a poorer response to both cognitive behaviour therapy and schema therapy. A treatment type interaction was found for verbal learning and memory. Lower levels of verbal learning and memory impairment markedly impacted on response to schema therapy. This was not the case for cognitive behaviour therapy, which was more impacted if verbal learning and memory was in the moderate range. Study findings are consistent with the Capitalisation Model suggesting that therapy that focuses on the person's strengths is more likely to contribute to a better outcome. Limitations were that participants were outpatients in a randomised controlled trial and may not be representative of other depressed samples. Examination of a variety of potential predictors was exploratory and requires replication.

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.

An association between neighbourhood wealth inequality and HIV prevalence in sub-Saharan Africa.

PubMed

Brodish, Paul Henry

2015-05-01

This paper investigates whether community-level wealth inequality predicts HIV serostatus using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding 5%. The analysis relates the binary dependent variable HIV-positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each statistical enumeration area, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behaviour mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behaviour variables attenuates the effects of both inequality measures. Reporting eleven plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behaviour differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioural mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention.

An association between neighborhood wealth inequality and HIV prevalence in sub-Saharan Africa

PubMed Central

Brodish, Paul Henry

2016-01-01

Summary This paper investigates whether community-level wealth inequality predicts HIV serostatus, using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding five percent. The analysis relates the binary dependent variable HIV positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each SEA, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behavior mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behavior variables attenuates the effects of both inequality measures. Reporting 11 plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behavior differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioral mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention PMID:24406021

Predictors of professional behaviour and academic outcomes in a UK medical school: A longitudinal cohort study.

PubMed

Adam, Jane; Bore, Miles; Childs, Roy; Dunn, Jason; Mckendree, Jean; Munro, Don; Powis, David

2015-01-01

Over the past 70 years, there has been a recurring debate in the literature and in the popular press about how best to select medical students. This implies that we are still not getting it right: either some students are unsuited to medicine or the graduating doctors are considered unsatisfactory, or both. To determine whether particular variables at the point of selection might distinguish those more likely to become satisfactory professional doctors, by following a complete intake cohort of students throughout medical school and analysing all the data used for the students' selection, their performance on a range of other potential selection tests, academic and clinical assessments throughout their studies, and records of professional behaviour covering the entire five years of the course. A longitudinal database captured the following anonymised information for every student (n = 146) admitted in 2007 to the Hull York Medical School (HYMS) in the UK: demographic data (age, sex, citizenship); performance in each component of the selection procedure; performance in some other possible selection instruments (cognitive and non-cognitive psychometric tests); professional behaviour in tutorials and in other clinical settings; academic performance, clinical and communication skills at summative assessments throughout; professional behaviour lapses monitored routinely as part of the fitness-to-practise procedures. Correlations were sought between predictor variables and criterion variables chosen to demonstrate the full range of course outcomes from failure to complete the course to graduation with honours, and to reveal clinical and professional strengths and weaknesses. Student demography was found to be an important predictor of outcomes, with females, younger students and British citizens performing better overall. The selection variable "HYMS academic score", based on prior academic performance, was a significant predictor of components of Year 4 written and Year 5 clinical examinations. Some cognitive subtest scores from the UK Clinical Aptitude Test (UKCAT) and the UKCAT total score were also significant predictors of the same components, and a unique predictor of the Year 5 written examination. A number of the non-cognitive tests were significant independent predictors of Years 4 and 5 clinical performance, and of lapses in professional behaviour. First- and second-year tutor ratings were significant predictors of all outcomes, both desirable and undesirable. Performance in Years 1 and 2 written exams did not predict performance in Year 4 but did generally predict Year 5 written and clinical performance. Measures of a range of relevant selection attributes and personal qualities can predict intermediate and end of course achievements in academic, clinical and professional behaviour domains. In this study HYMS academic score, some UKCAT subtest scores and the total UKCAT score, and some non-cognitive tests completed at the outset of studies, together predicted outcomes most comprehensively. Tutor evaluation of students early in the course also identified the more and less successful students in the three domains of academic, clinical and professional performance. These results may be helpful in informing the future development of selection tools.

Rumination time as a potential predictor of common diseases in high-productive Holstein dairy cows.

PubMed

Moretti, Riccardo; Biffani, Stefano; Tiezzi, Francesco; Maltecca, Christian; Chessa, Stefania; Bozzi, Riccardo

2017-11-01

We examined the hypothesis that rumination time (RT) could serve as a useful predictor of various common diseases of high producing dairy cows and hence improve herd management and animal wellbeing. We measured the changes in rumination time (RT) in the days before the recording of diseases (specifically: mastitis, reproductive system diseases, locomotor system issues, and gastroenteric diseases). We built predictive models to assess the association between RT and these diseases, using the former as the outcome variable, and to study the effects of the latter on the former. The average Pseudo-R 2 of the fitted models was moderate to low, and this could be due to the fact that RT is influenced by other additional factors which have a greater effect than the predictors used here. Although remaining in a moderate-to-low range, the average Pseudo-R 2 of the models regarding locomotion issues and gastroenteric diseases was higher than the others, suggesting the greater effect of these diseases on RT. The results are encouraging, but further work is needed if these models are to become useful predictors.

CWD prevalence, perceived human health risks, and state influences on deer hunting participation.

PubMed

Vaske, Jerry J; Lyon, Katie M

2011-03-01

This study examined factors predicted by previous research to influence hunters' decisions to stop hunting deer in a state. Data were obtained from mail surveys of resident and nonresident deer hunters in Arizona, North Dakota, South Dakota, and Wisconsin (n = 3,518). Hunters were presented with six scenarios depicting hypothetical CWD prevalence levels and human health risks from the disease (e.g., death), and asked if they would continue or stop hunting deer in the state. Bivariate analyses examined the influence of five predictor variables: (a) CWD prevalence, (b) hypothetical human death from CWD, (c) perceived human health risks from CWD, (d) state, and (e) residency. In the bivariate analyses, prevalence was the strongest predictor of quitting hunting in the state followed by hypothetical human death and perceived risk. The presence of CWD in a state and residency were weak, but statistically significant, predictors. Interactions among these predictors increased the potential for stopping hunting in the state. Multivariate analyses suggested that 64% of our respondents would quit hunting in the worst-case scenario. © 2010 Society for Risk Analysis.

Predictors of introduction success in the South Florida avifauna

USGS Publications Warehouse

Allen, Craig R.

2006-01-01

Biological invasions are an increasing global challenge, for which single-species studies and analyses focused on testing single hypotheses of causation in isolation are unlikely to provide much additional insight. Species interact with other species to create communities, which derive from species interactions and from the interactions of species with the scale specific elements of the landscape that provide suitable habitat and exploitable resources. I used logistic regression analysis to sort among potential intrinsic, community and landscape variables that theoretically influence introduction success. I utilized the avian fauna of the Everglades of South Florida, and the variables body mass, distance to nearest neighbor (in terms of body mass), year of introduction, presence of congeners, guild membership, continent of origin, distribution in a body mass aggregation or gap, and distance to body-mass aggregation edge (in terms of body mass). Two variables were significant predictors of introduction success. Introduced avian species whose body mass placed them nearer to a body-mass aggregation edge and further from their neighbor were more likely to become successfully established. This suggests that community interactions, and community level phenomena, may be better understood by explicitly incorporating scale. ?? Springer 2006.

Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

NASA Astrophysics Data System (ADS)

Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

2018-01-01

Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

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

PubMed

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

1999-09-01

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

Environmental Controls on Multi-Scale Soil Nutrient Variability in the Tropics: the Importance of Land-Cover Change

NASA Astrophysics Data System (ADS)

Holmes, K. W.; Kyriakidis, P. C.; Chadwick, O. A.; Matricardi, E.; Soares, J. V.; Roberts, D. A.

2003-12-01

The natural controls on soil variability and the spatial scales at which correlation exists among soil and environmental variables are critical information for evaluating the effects of deforestation. We detect different spatial scales of variability in soil nutrient levels over a large region (hundreds of thousands of km2) in the Amazon, analyze correlations among soil properties at these different scales, and evaluate scale-specific relationships among soil properties and the factors potentially driving soil development. Statistical relationships among physical drivers of soil formation, namely geology, precipitation, terrain attributes, classified soil types, and land cover derived from remote sensing, were included to determine which factors are related to soil biogeochemistry at each spatial scale. Surface and subsurface soil profile data from a 3000 sample database collected in Rond“nia, Brazil, were used to investigate patterns in pH, phosphorus, nitrogen, organic carbon, effective cation exchange capacity, calcium, magnesium, potassium, aluminum, sand, and clay in this environment grading from closed canopy tropical forest to savanna. We focus on pH in this presentation for simplicity, because pH is the single most important soil characteristic for determining the chemical environment of higher plants and soil microbial activity. We determined four spatial scales which characterize integrated patterns of soil chemistry: less than 3 km; 3 to 10 km; 10 to 68 km; and from 68 to 550 km (extent of study area). Although the finest observable scale was fixed by the field sampling density, the coarser scales were determined from relationships in the data through coregionalization modeling, rather than being imposed by the researcher. Processes which affect soils over short distances, such as land cover and terrain attributes, were good predictors of fine scale spatial components of nutrients; processes which affect soils over very large distances, such as precipitation and geology, were better predictors at coarse spatial scales. However, this result may be affected by the resolution of the available predictor maps. Land-cover change exerted a strong influence on soil chemistry at fine spatial scales, and had progressively less of an effect at coarser scales. It is important to note that land cover, and interactions among land cover and the other predictors, continued to be a significant predictor of soil chemistry at every spatial scale up to hundreds of thousands of kilometers.

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.

Exploratory Long-Range Models to Estimate Summer Climate Variability over Southern Africa.

NASA Astrophysics Data System (ADS)

Jury, Mark R.; Mulenga, Henry M.; Mason, Simon J.

1999-07-01

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño-Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa's annual maize yield. Predictors and targets overlap in the years 1971-93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean-atmosphere coupling processes inherent in the exploratory models is outlined.Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa's fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

Predictors and risk factors for the intestinal shedding of Escherichia coli O157 among working donkeys (Equus asinus) in Nigeria

PubMed Central

Jedial, Jesse T.; Shittu, Aminu; Tambuwal, Faruk M.; Abubakar, Mikail B.; Garba, Muhammed K.; Kwaga, Jacob P.; Fasina, Folorunso O.

2015-01-01

Objectives Escherichia coli are an important group of bacteria in the normal gastrointestinal system but can sometimes cause infections in domestic animals and man. Donkeys are routinely used as multipurpose animal but details of burdens of potentially infectious bacteria associated with it are limited. The prevalence and associations between intestinal shedding of E. coli O157 and animal characteristics and management factors were studied among 240 randomly selected working donkeys in north-western Nigeria. Design Four local government areas, of Sokoto State in north-western Nigeria were recruited in this study. A multistage randomised cluster design was used to select subjects and donkey owners within selected zones. Confirmation of infection was based on bacterial culture, isolation and biochemical test for E. coli O157 from faecal samples. Results Of the total bacteria isolated, 203 of the 329 (61.70 per cent) were E. coli, 76 of which was E. coli serotype O157. A multivariable logistic regression model was used to examine the relation between intestinal shedding of E. coli O157 and selected variables. The analysis yielded five potential predictors of shedding: soft faeces in donkeys, Akaza and Fari ecotypes of donkey were positive predictors while maize straw as feed and sampling during the cold dry period were negative predictors. Conclusions This study concludes that controlling intestinal shedding of E. coli O157 among working donkeys in Nigeria is possible using the identified predictors in planning appropriate interventions to reduced human risk of infection. PMID:26392892

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

Chicoine, T.K.; Fay, P.K.; Nielsen, G.A.

Soil characteristics, elevation, annual precipitation, potential evapotranspiration, length of frost-free season, and mean maximum July temperature were estimated for 116 established infestations of spotted knapweed (Centaurea maculosa Lam. number/sup 3/ CENMA) in Montana using basic land resource maps. Areas potentially vulnerable to invasion by the plant were delineated on the basis of representative edaphic and climatic characteristics. No single environmental variable was an effective predictor of sites vulnerable to invasion by spotted knapweed. Only a combination of variables was effective, indicating that the factors that regulate adaptability of this plant are complex. This technique provides a first approximation map ofmore » the regions most similar environmentally to infested sites and; therefore, most vulnerable to further invasion. This weed migration prediction technique shows promise for predicting suitable habitats of other invader species. 6 references, 4 figures, 1 table.« less

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.

A Winter Distribution Model for Bicknell’s Thrush (Catharus bicknelli), a Conservation Tool for a Threatened Migratory Songbird

PubMed Central

McFarland, Kent P.; Rimmer, Christopher C.; Goetz, James E.; Aubry, Yves; Wunderle, Joseph M.; Sutton, Anne; Townsend, Jason M.; Sosa, Alejandro Llanes; Kirkconnell, Arturo

2013-01-01

Conservation planning and implementation require identifying pertinent habitats and locations where protection and management may improve viability of targeted species. The winter range of Bicknell’s Thrush (Catharus bicknelli), a threatened Nearctic-Neotropical migratory songbird, is restricted to the Greater Antilles. We analyzed winter records from the mid-1970s to 2009 to quantitatively evaluate winter distribution and habitat selection. Additionally, we conducted targeted surveys in Jamaica (n = 433), Cuba (n = 363), Dominican Republic (n = 1,000), Haiti (n = 131) and Puerto Rico (n = 242) yielding 179 sites with thrush presence. We modeled Bicknell’s Thrush winter habitat selection and distribution in the Greater Antilles in Maxent version 3.3.1. using environmental predictors represented in 30 arc second study area rasters. These included nine landform, land cover and climatic variables that were thought a priori to have potentially high predictive power. We used the average training gain from ten model runs to select the best subset of predictors. Total winter precipitation, aspect and land cover, particularly broadleaf forests, emerged as important variables. A five-variable model that contained land cover, winter precipitation, aspect, slope, and elevation was the most parsimonious and not significantly different than the models with more variables. We used the best fitting model to depict potential winter habitat. Using the 10 percentile threshold (>0.25), we estimated winter habitat to cover 33,170 km2, nearly 10% of the study area. The Dominican Republic contained half of all potential habitat (51%), followed by Cuba (15.1%), Jamaica (13.5%), Haiti (10.6%), and Puerto Rico (9.9%). Nearly one-third of the range was found to be in protected areas. By providing the first detailed predictive map of Bicknell’s Thrush winter distribution, our study provides a useful tool to prioritize and direct conservation planning for this and other wet, broadleaf forest specialists in the Greater Antilles. PMID:23326554

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

Stress and Family Quality of Life in Parents of Children with Autism Spectrum Disorder: Parent Gender and the Double ABCX Model

ERIC Educational Resources Information Center

McStay, Rebecca L.; Trembath, David; Dissanayake, Cheryl

2014-01-01

Past research has supported the utility of the Double ABCX model of family adaptation for parents raising a child with autism spectrum disorder (ASD). What remains unclear is the impact of family-related variables on outcomes in both mothers and fathers within the same family. We explored the potential predictors of maternal and paternal stress…

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 Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

PubMed

Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

2016-01-01

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

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.

Psychological predictors of retention in a low-threshold methadone maintenance treatment for opioid addicts: a 1-year follow-up study.

PubMed

Perreault, Michel; Julien, Dominic; White, Noe Djawn; Rabouin, Daniel; Lauzon, Pierre; Milton, Diana

2015-01-01

This study investigated the role of psychological variables and judicial problems in treatment retention for a low-threshold methadone program in Montreal, Canada. Logistic regression analyses were computed to examine associations between psychological variables (psychological distress, self-esteem, stages of change), criminal justice involvement, and treatment retention for 106 highly-disorganized opioid users. Higher methadone dosage was associated with increased odds of treatment retention, whereas criminal charges and lower self-esteem decreased these odds. Psychological variables could be identified early in treatment and targeted to increase potential treatment retention. Financial support for this study was provided by the Fonds de Recherche en Santé du Québec.

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.

Recidivism in stalking and obsessional harassment.

PubMed

Rosenfeld, Barry

2003-06-01

Despite the rapidly growth of mental health attention focused on the phenomenon of stalking, no empirical research to date has attempted to assess the frequency of repeat offending or attempted to identify predictors of recidivism. A total of 148 stalking and harassment offenders who were court-ordered to undergo a mental health evaluation were followed for a period of 2.5-13 years in order to assess the frequency of repeat offenses and the variables that differentiated high versus low risk offenders. Recidivism data were obtained from a variety of sources, including criminal justice records, mental health records, and reports from probation officers and victims. A number of potential "predictor" variables were selected on the basis of the existing recidivism literature in other criminal justice populations. Frequency analysis were used to identify variables that significantly differentiated offenders who did and did not reoffened while survival analysis was used to analyze the impact of these covariates on time to reoffense. A total of 49% of the offenders reoffended during the follow-up period, 80% of whom reoffended during the first year. The strongest predictors of recidivism included the presence of a personality disorder, and in particular, a "Cluster B" personality disorder (i.e., antisocial, borderline, and/or narcissistic). In addition, those offenders with both a personality disorder and a history of substance abuse were significantly more likely to reoffened compared to either of these risk factors alone. Surprisingly, the presence of a delusional disorder (e.g., erotomania) was associated with a lower risk of reoffender. The findings are discussed in terms of the legal system and treatment implications.

What variables are important in predicting bovine viral diarrhea virus? A random forest approach.

PubMed

Machado, Gustavo; Mendoza, Mariana Recamonde; Corbellini, Luis Gustavo

2015-07-24

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.

Prediction of hypertensive crisis based on average, variability and approximate entropy of 24-h ambulatory blood pressure monitoring.

PubMed

Schoenenberger, A W; Erne, P; Ammann, S; Perrig, M; Bürgi, U; Stuck, A E

2008-01-01

Approximate entropy (ApEn) of blood pressure (BP) can be easily measured based on software analysing 24-h ambulatory BP monitoring (ABPM), but the clinical value of this measure is unknown. In a prospective study we investigated whether ApEn of BP predicts, in addition to average and variability of BP, the risk of hypertensive crisis. In 57 patients with known hypertension we measured ApEn, average and variability of systolic and diastolic BP based on 24-h ABPM. Eight of these fifty-seven patients developed hypertensive crisis during follow-up (mean follow-up duration 726 days). In bivariate regression analysis, ApEn of systolic BP (P<0.01), average of systolic BP (P=0.02) and average of diastolic BP (P=0.03) were significant predictors of hypertensive crisis. The incidence rate ratio of hypertensive crisis was 14.0 (95% confidence interval (CI) 1.8, 631.5; P<0.01) for high ApEn of systolic BP as compared to low values. In multivariable regression analysis, ApEn of systolic (P=0.01) and average of diastolic BP (P<0.01) were independent predictors of hypertensive crisis. A combination of these two measures had a positive predictive value of 75%, and a negative predictive value of 91%, respectively. ApEn, combined with other measures of 24-h ABPM, is a potentially powerful predictor of hypertensive crisis. If confirmed in independent samples, these findings have major clinical implications since measures predicting the risk of hypertensive crisis define patients requiring intensive follow-up and intensified therapy.

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.

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.

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

PubMed

Røislien, Jo; Søvik, Signe; Eken, Torsten

2018-01-01

Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year. Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC). A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models. Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.

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

Parents’ Primary Professional Sources of Parenting Advice Moderate Predictors of Parental Attitudes toward Corporal Punishment

PubMed Central

Taylor, Catherine A.; McKasson, Sarah; Hoy, Guenevere; DeJong, William

2016-01-01

Despite the risk it poses to children’s mental and physical health, approval and use of corporal punishment (CP) remains high in the United States. Informed by the Theory of Planned Behavior, we examined potential predictors of attitudes supportive of CP while assessing the moderating effects of parents’ (N=500) chosen primary professional source of advice regarding child discipline: pediatricians (47.8%), religious leaders (20.8%), mental health professionals (MHPs) (n=18.4%), or other identified professionals (13.0%). We conducted a random-digit-dial telephone survey among parents ages 18 and over within New Orleans, LA. The main outcome measure was derived from the Attitudes Toward Spanking scale (ATS). The main “predictors” were: perceived injunctive norms (i.e., perceived approval of CP by professionals; and by family and friends), perceived descriptive norms of family and friends regarding CP, and expected outcomes of CP use. We used multivariate OLS models to regress ATS scores on the predictor variables for each subset of parents based on their chosen professional source of advice. Perceived approval of CP by professionals was the strongest predictor of parental attitudes supportive of CP, except for those seeking advice from MHPs. Perceived injunctive and descriptive norms of family and friends were important, but only for those seeking advice from pediatricians or religious leaders. Positive expected outcomes of CP mattered, but only for those seeking advice from religious leaders or MHPs. In conclusion, the strength and relevance of variables predicting attitudes toward CP varied according to the professional from which the parent was most likely to seek advice. PMID:28529440

Percutaneous epiphysiodesis using transphyseal screws for limb-length discrepancies: high variability among growth predictor models.

PubMed

Monier, Bryan C; Aronsson, David D; Sun, Michael

2015-10-01

Percutaneous epiphysiodesis using transphyseal screws (PETS) was developed as a minimally invasive outpatient procedure to address limb-length discrepancy (LLD) that allowed immediate postoperative weight bearing and was potentially reversible by removing the screws. The aims of our study were to report our results using PETS for LLD and evaluate the accuracy of three growth predictor models. Sixteen patients with an average age of 14 years were treated for LLD using PETS. Thirteen patients had screws inserted in a parallel fashion and 3 had crossed screws. We compared the predicted LLD at skeletal maturity using the three growth predictor methods with the actual LLD at skeletal maturity and preoperative LLD with the final LLD at skeletal maturity. The mean LLD at skeletal maturity between the predicted and final measurements was 0.2 cm using the Green-Anderson method, 1.4 cm using the Moseley method, and -0.1 cm using the Paley method. The mean preoperative LLD of 3.1 cm was corrected to 1.7 cm at skeletal maturity (p < 0.001). Six patients complained of pain over the screw heads; however, no patient developed an infection or angular deformity. The three growth predictor methods predicted the final LLD within an average of 1.4 cm, but there was high variability. Although PETS improved the LLD by a mean of 1.4 cm, we believe the results would have been better if PETS was performed at an earlier skeletal age.

Spatial patterns of air pollutants and social groups: a distributive environmental justice study in the phoenix metropolitan region of USA

NASA Astrophysics Data System (ADS)

Pope, Ronald; Wu, Jianguo; Boone, Christopher

2016-11-01

Quantifying spatial distribution patterns of air pollutants is imperative to understand environmental justice issues. Here we present a landscape-based hierarchical approach in which air pollution variables are regressed against population demographics on multiple spatiotemporal scales. Using this approach, we investigated the potential problem of distributive environmental justice in the Phoenix metropolitan region, focusing on ambient ozone and particulate matter. Pollution surfaces (maps) are evaluated against the demographics of class, age, race (African American, Native American), and ethnicity (Hispanic). A hierarchical multiple regression method is used to detect distributive environmental justice relationships. Our results show that significant relationships exist between the dependent and independent variables, signifying possible environmental inequity. Although changing spatiotemporal scales only altered the overall direction of these relationships in a few instances, it did cause the relationship to become nonsignificant in many cases. Several consistent patterns emerged: people aged 17 and under were significant predictors for ambient ozone and particulate matter, but people 65 and older were only predictors for ambient particulate matter. African Americans were strong predictors for ambient particulate matter, while Native Americans were strong predictors for ambient ozone. Hispanics had a strong negative correlation with ambient ozone, but a less consistent positive relationship with ambient particulate matter. Given the legacy conditions endured by minority racial and ethnic groups, and the relative lack of mobility of all the groups, our findings suggest the existence of environmental inequities in the Phoenix metropolitan region. The methodology developed in this study is generalizable with other pollutants to provide a multi-scaled perspective of environmental justice issues.

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.…

The COMMAND trial of cognitive therapy to prevent harmful compliance with command hallucinations: predictors of outcome and mediators of change.

PubMed

Birchwood, Max; Dunn, Graham; Meaden, Alan; Tarrier, Nicholas; Lewis, Shon; Wykes, Til; Davies, Linda; Michail, Maria; Peters, Emmanuelle

2017-12-05

Acting on harmful command hallucinations is a major clinical concern. Our COMMAND CBT trial approximately halved the rate of harmful compliance (OR = 0.45, 95% CI 0.23-0.88, p = 0.021). The focus of the therapy was a single mechanism, the power dimension of voice appraisal, was also significantly reduced. We hypothesised that voice power differential (between voice and voice hearer) was the mediator of the treatment effect. The trial sample (n = 197) was used. A logistic regression model predicting 18-month compliance was used to identify predictors, and an exploratory principal component analysis (PCA) of baseline variables used as potential predictors (confounders) in their own right. Stata's paramed command used to obtain estimates of the direct, indirect and total effects of treatment. Voice omnipotence was the best predictor although the PCA identified a highly predictive cognitive-affective dimension comprising: voices' power, childhood trauma, depression and self-harm. In the mediation analysis, the indirect effect of treatment was fully explained by its effect on the hypothesised mediator: voice power differential. Voice power and treatment allocation were the best predictors of harmful compliance up to 18 months; post-treatment, voice power differential measured at nine months was the mediator of the effect of treatment on compliance at 18 months.

Life Span Studies of ADHD-Conceptual Challenges and Predictors of Persistence and Outcome.

PubMed

Caye, Arthur; Swanson, James; Thapar, Anita; Sibley, Margaret; Arseneault, Louise; Hechtman, Lily; Arnold, L Eugene; Niclasen, Janni; Moffitt, Terrie; Rohde, Luis Augusto

2016-12-01

There is a renewed interest in better conceptualizing trajectories of attention-deficit/hyperactivity disorder (ADHD) from childhood to adulthood, driven by an increased recognition of long-term impairment and potential persistence beyond childhood and adolescence. This review addresses the following major issues relevant to the course of ADHD in light of current evidence from longitudinal studies: (1) conceptual and methodological issues related to measurement of persistence of ADHD, (2) estimates of persistence rate from childhood to adulthood and its predictors, (3) long-term negative outcomes of childhood ADHD and their early predictors, and (4) the recently proposed new adult-onset ADHD. Estimates of persistence vary widely in the literature, and diagnostic criteria, sample characteristics, and information source are the most important factors explaining variability among studies. Evidence indicates that ADHD severity, comorbid conduct disorder and major depressive disorder, and treatment for ADHD are the main predictors of ADHD persistence from childhood to adulthood. Comorbid conduct disorder and ADHD severity in childhood are the most important predictors of adverse outcomes in adulthood among children with ADHD. Three recent population studies suggested the existence of a significant proportion of individuals who report onset of ADHD symptoms and impairments after childhood. Finally, we highlight areas for improvement to increase our understanding of ADHD across the life span.

Life Span Studies of ADHD—Conceptual Challenges and Predictors of Persistence and Outcome

PubMed Central

Caye, Arthur; Swanson, James; Thapar, Anita; Sibley, Margaret; Arseneault, Louise; Hechtman, Lily; Arnold, L. Eugene; Niclasen, Janni; Moffitt, Terrie

2018-01-01

There is a renewed interest in better conceptualizing trajectories of attention-deficit/hyperactivity disorder (ADHD) from childhood to adulthood, driven by an increased recognition of long-term impairment and potential persistence beyond childhood and adolescence. This review addresses the following major issues relevant to the course of ADHD in light of current evidence from longitudinal studies: (1) conceptual and methodological issues related to measurement of persistence of ADHD, (2) estimates of persistence rate from childhood to adulthood and its predictors, (3) long-term negative outcomes of childhood ADHD and their early predictors, and (4) the recently proposed new adult-onset ADHD. Estimates of persistence vary widely in the literature, and diagnostic criteria, sample characteristics, and information source are the most important factors explaining variability among studies. Evidence indicates that ADHD severity, comorbid conduct disorder and major depressive disorder, and treatment for ADHD are the main predictors of ADHD persistence from childhood to adulthood. Comorbid conduct disorder and ADHD severity in childhood are the most important predictors of adverse outcomes in adulthood among children with ADHD. Three recent population studies suggested the existence of a significant proportion of individuals who report onset of ADHD symptoms and impairments after childhood. Finally, we highlight areas for improvement to increase our understanding of ADHD across the life span. PMID:27783340

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

Modelling the effects of environmental conditions on the acoustic occurrence and behaviour of Antarctic blue whales

PubMed Central

Shabangu, Fannie W.; Yemane, Dawit; Stafford, Kathleen M.; Ensor, Paul; Findlay, Ken P.

2017-01-01

Harvested to perilously low numbers by commercial whaling during the past century, the large scale response of Antarctic blue whales Balaenoptera musculus intermedia to environmental variability is poorly understood. This study uses acoustic data collected from 586 sonobuoys deployed in the austral summers of 1997 through 2009, south of 38°S, coupled with visual observations of blue whales during the IWC SOWER line-transect surveys. The characteristic Z-call and D-call of Antarctic blue whales were detected using an automated detection template and visual verification method. Using a random forest model, we showed the environmental preferences pattern, spatial occurrence and acoustic behaviour of Antarctic blue whales. Distance to the southern boundary of the Antarctic Circumpolar Current (SBACC), latitude and distance from the nearest Antarctic shores were the main geographic predictors of blue whale call occurrence. Satellite-derived sea surface height, sea surface temperature, and productivity (chlorophyll-a) were the most important environmental predictors of blue whale call occurrence. Call rates of D-calls were strongly predicted by the location of the SBACC, latitude and visually detected number of whales in an area while call rates of Z-call were predicted by the SBACC, latitude and longitude. Satellite-derived sea surface height, wind stress, wind direction, water depth, sea surface temperatures, chlorophyll-a and wind speed were important environmental predictors of blue whale call rates in the Southern Ocean. Blue whale call occurrence and call rates varied significantly in response to inter-annual and long term variability of those environmental predictors. Our results identify the response of Antarctic blue whales to inter-annual variability in environmental conditions and highlighted potential suitable habitats for this population. Such emerging knowledge about the acoustic behaviour, environmental and habitat preferences of Antarctic blue whales is important in improving the management and conservation of this highly depleted species. PMID:28222124

Modelling the effects of environmental conditions on the acoustic occurrence and behaviour of Antarctic blue whales.

PubMed

Shabangu, Fannie W; Yemane, Dawit; Stafford, Kathleen M; Ensor, Paul; Findlay, Ken P

2017-01-01

Harvested to perilously low numbers by commercial whaling during the past century, the large scale response of Antarctic blue whales Balaenoptera musculus intermedia to environmental variability is poorly understood. This study uses acoustic data collected from 586 sonobuoys deployed in the austral summers of 1997 through 2009, south of 38°S, coupled with visual observations of blue whales during the IWC SOWER line-transect surveys. The characteristic Z-call and D-call of Antarctic blue whales were detected using an automated detection template and visual verification method. Using a random forest model, we showed the environmental preferences pattern, spatial occurrence and acoustic behaviour of Antarctic blue whales. Distance to the southern boundary of the Antarctic Circumpolar Current (SBACC), latitude and distance from the nearest Antarctic shores were the main geographic predictors of blue whale call occurrence. Satellite-derived sea surface height, sea surface temperature, and productivity (chlorophyll-a) were the most important environmental predictors of blue whale call occurrence. Call rates of D-calls were strongly predicted by the location of the SBACC, latitude and visually detected number of whales in an area while call rates of Z-call were predicted by the SBACC, latitude and longitude. Satellite-derived sea surface height, wind stress, wind direction, water depth, sea surface temperatures, chlorophyll-a and wind speed were important environmental predictors of blue whale call rates in the Southern Ocean. Blue whale call occurrence and call rates varied significantly in response to inter-annual and long term variability of those environmental predictors. Our results identify the response of Antarctic blue whales to inter-annual variability in environmental conditions and highlighted potential suitable habitats for this population. Such emerging knowledge about the acoustic behaviour, environmental and habitat preferences of Antarctic blue whales is important in improving the management and conservation of this highly depleted species.

A stochastic model for optimizing composite predictors based on gene expression profiles.

PubMed

Ramanathan, Murali

2003-07-01

This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics. The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints. The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a. The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite. The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability. The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level. The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier." When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method. The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PubMed

Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2017-01-01

The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Risk Factors for Hearing Decrement Among U.S. Air Force Aviation-Related Personnel.

PubMed

Greenwell, Brandon M; Tvaryanas, Anthony P; Maupin, Genny M

2018-02-01

The purpose of this study was to analyze historical hearing sensitivity data to determine factors associated with an occupationally significant change in hearing sensitivity in U.S. Air Force aviation-related personnel. This study was a longitudinal, retrospective cohort analysis of audiogram records for Air Force aviation-related personnel on active duty during calendar year 2013 without a diagnosis of non-noise-related hearing loss. The outcomes of interest were raw change in hearing sensitivity from initial baseline to 2013 audiogram and initial occurrence of a significant threshold shift (STS) and non-H1 audiogram profile. Potential predictor variables included age and elapsed time in cohort for each audiogram, gender, and Air Force Specialty Code. Random forest analyses conducted on a learning sample were used to identify relevant predictor variables. Mixed effects models were fitted to a separate validation sample to make statistical inferences. The final dataset included 167,253 nonbaseline audiograms on 10,567 participants. Only the interaction between time since baseline audiogram and age was significantly associated with raw change in hearing sensitivity by STS metric. None of the potential predictors were associated with the likelihood for an STS. Time since baseline audiogram, age, and their interaction were significantly associated with the likelihood for a non-HI hearing profile. In this study population, age and elapsed time since baseline audiogram were modestly associated with decreased hearing sensitivity and increased likelihood for a non-H1 hearing profile. Aircraft type, as determined from Air Force Specialty Code, was not associated with changes in hearing sensitivity by STS metric.Greenwell BM, Tvaryanas AP, Maupin GM. Risk factors for hearing decrement among U.S. Air Force aviation-related personnel. Aerosp Med Hum Perform. 2018; 89(2):80-86.

Is breastfeeding in infancy predictive of child mental well-being and protective against obesity at 9 years of age?

PubMed

Reynolds, D; Hennessy, E; Polek, E

2014-11-01

Preventing child mental health problems and child obesity have been recognized as public health priorities. The aim of the present study was to examine whether being breastfed (at all or exclusively) in infancy was a predictor of mental well-being and protective against risk of obesity at age 9. Cross-sectional data from a large, nationally representative cohort study in the Republic of Ireland was used (n = 8357). Data on breastfeeding were retrospectively recalled. Child mental well-being was assessed using a parent-completed Strengths and Difficulties Questionnaire (SDQ). Child's height and weight were measured using scientifically calibrated instruments. Logistic regression analyses indicated that, after controlling for a wide range of potential confounding variables, being breastfed in infancy was associated with a 26% (P < 0.05) reduction in the risk of an abnormal SDQ score at 9 years. Being breastfed remained a significant predictor of child mental well-being when child obesity was controlled for, indicating that being breastfed, independent of child obesity, is a predictor of child mental well-being. The results of a second logistic regression indicated that, after controlling for a wide range of potential confounding variables, being breastfed for between 11 and 25 weeks was associated with a 36% (P < 0.05) reduction in the risk of obesity at 9 years, while being breastfed for 26 weeks or longer was associated with a 48% (P < 0.01) reduction in the risk of obesity at 9 years. Breastfeeding in infancy may protect against both poor mental well-being and obesity in childhood. © 2013 John Wiley & Sons Ltd.

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

NASA Astrophysics Data System (ADS)

Pal, I.; Lall, U.; Robertson, A. W.; Cane, M. A.; Bansal, R.

2013-06-01

Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

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.

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.

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

Socio-Demographic, Social-Cognitive, Health-Related and Physical Environmental Variables Associated with Context-Specific Sitting Time in Belgian Adolescents: A One-Year Follow-Up Study.

PubMed

Busschaert, Cedric; Ridgers, Nicola D; De Bourdeaudhuij, Ilse; Cardon, Greet; Van Cauwenberg, Jelle; De Cocker, Katrien

2016-01-01

More knowledge is warranted about multilevel ecological variables associated with context-specific sitting time among adolescents. The present study explored cross-sectional and longitudinal associations of ecological domains of sedentary behaviour, including socio-demographic, social-cognitive, health-related and physical-environmental variables with sitting during TV viewing, computer use, electronic gaming and motorized transport among adolescents. For this longitudinal study, a sample of Belgian adolescents completed questionnaires at school on context-specific sitting time and associated ecological variables. At baseline, complete data were gathered from 513 adolescents (15.0±1.7 years). At one-year follow-up, complete data of 340 participants were available (retention rate: 66.3%). Multilevel linear regression analyses were conducted to explore cross-sectional correlates (baseline variables) and longitudinal predictors (change scores variables) of context-specific sitting time. Social-cognitive correlates/predictors were most frequently associated with context-specific sitting time. Longitudinal analyses revealed that increases over time in considering it pleasant to watch TV (p < .001), in perceiving TV watching as a way to relax (p < .05), in TV time of parents/care givers (p < .01) and in TV time of siblings (p < .001) were associated with more sitting during TV viewing at follow-up. Increases over time in considering it pleasant to use a computer in leisure time (p < .01) and in the computer time of siblings (p < .001) were associated with more sitting during computer use at follow-up. None of the changes in potential predictors were significantly related to changes in sitting during motorized transport or during electronic gaming. Future intervention studies aiming to decrease TV viewing and computer use should acknowledge the importance of the behaviour of siblings and the pleasure adolescents experience during these screen-related behaviours. In addition, more time parents or care givers spent sitting may lead to more sitting during TV viewing of the adolescents, so that a family-based approach may be preferable for interventions. Experimental study designs are warranted to confirm the present findings.

Socio-Demographic, Social-Cognitive, Health-Related and Physical Environmental Variables Associated with Context-Specific Sitting Time in Belgian Adolescents: A One-Year Follow-Up Study

PubMed Central

Busschaert, Cedric; Ridgers, Nicola D.; De Bourdeaudhuij, Ilse; Cardon, Greet; Van Cauwenberg, Jelle; De Cocker, Katrien

2016-01-01

Introduction More knowledge is warranted about multilevel ecological variables associated with context-specific sitting time among adolescents. The present study explored cross-sectional and longitudinal associations of ecological domains of sedentary behaviour, including socio-demographic, social-cognitive, health-related and physical-environmental variables with sitting during TV viewing, computer use, electronic gaming and motorized transport among adolescents. Methods For this longitudinal study, a sample of Belgian adolescents completed questionnaires at school on context-specific sitting time and associated ecological variables. At baseline, complete data were gathered from 513 adolescents (15.0±1.7 years). At one-year follow-up, complete data of 340 participants were available (retention rate: 66.3%). Multilevel linear regression analyses were conducted to explore cross-sectional correlates (baseline variables) and longitudinal predictors (change scores variables) of context-specific sitting time. Results Social-cognitive correlates/predictors were most frequently associated with context-specific sitting time. Longitudinal analyses revealed that increases over time in considering it pleasant to watch TV (p < .001), in perceiving TV watching as a way to relax (p < .05), in TV time of parents/care givers (p < .01) and in TV time of siblings (p < .001) were associated with more sitting during TV viewing at follow-up. Increases over time in considering it pleasant to use a computer in leisure time (p < .01) and in the computer time of siblings (p < .001) were associated with more sitting during computer use at follow-up. None of the changes in potential predictors were significantly related to changes in sitting during motorized transport or during electronic gaming. Conclusions Future intervention studies aiming to decrease TV viewing and computer use should acknowledge the importance of the behaviour of siblings and the pleasure adolescents experience during these screen-related behaviours. In addition, more time parents or care givers spent sitting may lead to more sitting during TV viewing of the adolescents, so that a family-based approach may be preferable for interventions. Experimental study designs are warranted to confirm the present findings. PMID:27936073

The relative impacts of climate and land-use change on conterminous United States bird species from 2001 to 2075

USGS Publications Warehouse

Sohl, Terry L.

2014-01-01

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be "suitable" for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges.

Habitat and Vegetation Variables Are Not Enough When Predicting Tick Populations in the Southeastern United States

PubMed Central

Trout Fryxell, R. T.; Moore, J. E.; Collins, M. D.; Kwon, Y.; Jean-Philippe, S. R.; Schaeffer, S. M.; Odoi, A.; Kennedy, M.; Houston, A. E.

2015-01-01

Two tick-borne diseases with expanding case and vector distributions are ehrlichiosis (transmitted by Amblyomma americanum) and rickettiosis (transmitted by A. maculatum and Dermacentor variabilis). There is a critical need to identify the specific habitats where each of these species is likely to be encountered to classify and pinpoint risk areas. Consequently, an in-depth tick prevalence study was conducted on the dominant ticks in the southeast. Vegetation, soil, and remote sensing data were used to test the hypothesis that habitat and vegetation variables can predict tick abundances. No variables were significant predictors of A. americanum adult and nymph tick abundance, and no clustering was evident because this species was found throughout the study area. For A. maculatum adult tick abundance was predicted by NDVI and by the interaction between habitat type and plant diversity; two significant population clusters were identified in a heterogeneous area suitable for quail habitat. For D. variabilis no environmental variables were significant predictors of adult abundance; however, D. variabilis collections clustered in three significant areas best described as agriculture areas with defined edges. This study identified few landscape and vegetation variables associated with tick presence. While some variables were significantly associated with tick populations, the amount of explained variation was not useful for predicting reliably where ticks occur; consequently, additional research that includes multiple sampling seasons and locations throughout the southeast are warranted. This low amount of explained variation may also be due to the use of hosts for dispersal, and potentially to other abiotic and biotic variables. Host species play a large role in the establishment, maintenance, and dispersal of a tick species, as well as the maintenance of disease cycles, dispersal to new areas, and identification of risk areas. PMID:26656122

The Relative Impacts of Climate and Land-Use Change on Conterminous United States Bird Species from 2001 to 2075

PubMed Central

Sohl, Terry L.

2014-01-01

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges. PMID:25372571

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

A retrospective review of fall risk factors in the bone marrow transplant inpatient service.

PubMed

Vela, Cory M; Grate, Lisa M; McBride, Ali; Devine, Steven; Andritsos, Leslie A

2018-06-01

Purpose The purpose of this study was to compare medications and potential risk factors between patients who experienced a fall during hospitalization compared to those who did not fall while admitted to the Blood and Marrow Transplant inpatient setting at The James Cancer Hospital. Secondary objectives included evaluation of transplant-related disease states and medications in the post-transplant setting that may lead to an increased risk of falls, post-fall variables, and number of tests ordered after a fall. Methods This retrospective, case-control study matched patients in a 2:1 ratio of nonfallers to fallers. Data from The Ohio State University Wexner Medical Center (OSUWMC) reported fall events and patient electronic medical records were utilized. A total of 168 adult Blood and Marrow Transplant inpatients with a hematological malignancy diagnosis were evaluated from 1 January 2010 to 30 September 2012. Results Univariable and multivariable conditional logistic regression models were used to assess the relationship between potential predictor variables of interest and falls. Variables that were found to be significant predictors of falls from the univariable models include age group, incontinence, benzodiazepines, corticosteroids, anticonvulsants and antidepressants, and number of days status-post transplant. When considered for a multivariable model age group, corticosteroids, and a cancer diagnosis of leukemia were significant in the final model. Conclusion Recent medication utilization such as benzodiazepines, anticonvulsants, corticosteroids, and antidepressants placed patients at a higher risk of experiencing a fall. Other significant factors identified from a multivariable analysis found were patients older than age 65, patients with recent corticosteroid administration and a cancer diagnosis of leukemia.

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

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…

The effect of service satisfaction and spiritual well-being on the quality of life of patients with schizophrenia.

PubMed

Lanfredi, Mariangela; Candini, Valentina; Buizza, Chiara; Ferrari, Clarissa; Boero, Maria E; Giobbio, Gian M; Goldschmidt, Nicoletta; Greppo, Stefania; Iozzino, Laura; Maggi, Paolo; Melegari, Anna; Pasqualetti, Patrizio; Rossi, Giuseppe; de Girolamo, Giovanni

2014-05-15

Quality of life (QOL) has been considered an important outcome measure in psychiatric research and determinants of QOL have been widely investigated. We aimed at detecting predictors of QOL at baseline and at testing the longitudinal interrelations of the baseline predictors with QOL scores at a 1-year follow-up in a sample of patients living in Residential Facilities (RFs). Logistic regression models were adopted to evaluate the association between WHOQoL-Bref scores and potential determinants of QOL. In addition, all variables significantly associated with QOL domains in the final logistic regression model were included by using the Structural Equation Modeling (SEM). We included 139 patients with a diagnosis of schizophrenia spectrum. In the final logistic regression model level of activity, social support, age, service satisfaction, spiritual well-being and symptoms' severity were identified as predictors of QOL scores at baseline. Longitudinal analyses carried out by SEM showed that 40% of QOL follow-up variability was explained by QOL at baseline, and significant indirect effects toward QOL at follow-up were found for satisfaction with services and for social support. Rehabilitation plans for people with schizophrenia living in RFs should also consider mediators of change in subjective QOL such as satisfaction with mental health services. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Inter-individual differences in sleep response to shift work in novice police officers - A prospective study.

PubMed

Lammers-van der Holst, Heidi M; Van Dongen, Hans P A; Drosopoulos, Spyridon; Kerkhof, Gerard A

2016-01-01

The aim of this longitudinal study on novice police officers was to investigate inter-individual differences in sleep response to shift work, and to identify potential baseline predictors thereof. A total of 42 subjects were assessed at baseline, prior to commencing shift work. They were re-assessed during three follow-up sessions within the first 2 years of shift work exposure after approximately 4, 12, and 20 months of rotating shift work. Wrist actigraphy and sleep logs were used to investigate nocturnal sleep at baseline and daytime sleep after night shifts during the follow-up sessions. Actigraphically estimated total sleep time and subjective sleep quality were analyzed as outcome variables, using mixed-effects analysis of variance. Systematic inter-individual differences were observed in the overall response of these outcome variables to shift work. In this sample, flexibility of sleeping habits and gender were found to be predictors of daytime total sleep time in the first 2 years of shift work exposure. Flexibility of sleeping habits and subjective quality of nighttime sleep prior to shift work were found to be predictors of subjective quality of daytime sleep. These results suggest that it may be possible to detect and even predict sleep deficiencies in response to shift work early on, which could be a basis for the development of individualized interventions to improve shift work tolerance.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

PubMed

Abram, Samantha V; Helwig, Nathaniel E; Moodie, Craig A; DeYoung, Colin G; MacDonald, Angus W; Waller, Niels G

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

PubMed Central

Abram, Samantha V.; Helwig, Nathaniel E.; Moodie, Craig A.; DeYoung, Colin G.; MacDonald, Angus W.; Waller, Niels G.

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks. PMID:27516732

Applying an Ensemble Classification Tree Approach to the Prediction of Completion of a 12-Step Facilitation Intervention with Stimulant Abusers

PubMed Central

Doyle, Suzanne R.; Donovan, Dennis M.

2014-01-01

Aims The purpose of this study was to explore the selection of predictor variables in the evaluation of drug treatment completion using an ensemble approach with classification trees. The basic methodology is reviewed and the subagging procedure of random subsampling is applied. Methods Among 234 individuals with stimulant use disorders randomized to a 12-Step facilitative intervention shown to increase stimulant use abstinence, 67.52% were classified as treatment completers. A total of 122 baseline variables were used to identify factors associated with completion. Findings The number of types of self-help activity involvement prior to treatment was the predominant predictor. Other effective predictors included better coping self-efficacy for substance use in high-risk situations, more days of prior meeting attendance, greater acceptance of the Disease model, higher confidence for not resuming use following discharge, lower ASI Drug and Alcohol composite scores, negative urine screens for cocaine or marijuana, and fewer employment problems. Conclusions The application of an ensemble subsampling regression tree method utilizes the fact that classification trees are unstable but, on average, produce an improved prediction of the completion of drug abuse treatment. The results support the notion there are early indicators of treatment completion that may allow for modification of approaches more tailored to fitting the needs of individuals and potentially provide more successful treatment engagement and improved outcomes. PMID:25134038

The association of perceived discrimination with low back pain.

PubMed

Edwards, Robert R

2008-10-01

A handful of recent studies have documented perceived discrimination as a correlate of poor physical and mental health status among ethnic and racial minority groups. To date, however, despite a proliferation of research on ethnic disparities in the severity and impact of a number of persistent pain conditions, there have been no reports on associations between perceived discrimination and pain-related symptoms. Using data from a national survey (the National Survey of Midlife Development in the United States; MIDUS), we explore the relationships between perceived discriminatory events and the report of back pain among African-American and white men and women. As expected, African-American participants reported substantially greater perceptions of discrimination than white participants. Moreover, in models that included a variety of physical and mental health variables, episodes of major lifetime discriminatory events were the strongest predictors of back pain report in African-Americans, and perceived day-to-day discrimination was the strongest predictor of back pain report specifically in African-American women. Among white participants, perceptions of discrimination were minimally related or unrelated to back pain. To our knowledge, these are the first data documenting an association between perceived discrimination and report of back pain; the fact that perceptions of discrimination were stronger predictors than physical health variables highlights the potential salience and adverse impact of perceived discrimination in ethnic and racial minority groups.

Developing and testing a measurement tool for assessing predictors of breakfast consumption based on a health promotion model.

PubMed

Dehdari, Tahereh; Rahimi, Tahereh; Aryaeian, Naheed; Gohari, Mahmood Reza; Esfeh, Jabiz Modaresi

2014-01-01

To develop an instrument for measuring Health Promotion Model constructs in terms of breakfast consumption, and to identify the constructs that were predictors of breakfast consumption among Iranian female students. A questionnaire on Health Promotion Model variables was developed and potential predictors of breakfast consumption were assessed using this tool. One hundred female students, mean age 13 years (SD ± 1.2 years). Two middle schools from moderate-income areas in Qom, Iran. Health Promotion Model variables were assessed using a 58-item questionnaire. Breakfast consumption was also measured. Internal consistency (Cronbach alpha), content validity index, content validity ratio, multiple linear regression using stepwise method, and Pearson correlation. Content validity index and content validity ratio scores of the developed scale items were 0.89 and 0.93, respectively. Internal consistencies (range, .74-.91) of subscales were acceptable. Prior related behaviors, perceived barriers, self-efficacy, and competing demand and preferences were 4 constructs that could predict 63% variance of breakfast frequency per week among subjects. The instrument developed in this study may be a useful tool for researchers to explore factors affecting breakfast consumption among students. Students with a high level of self-efficacy, more prior related behavior, fewer perceived barriers, and fewer competing demands were most likely to regularly consume breakfast. Copyright © 2014 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

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

[Referral to internal medicine for alcoholism: influence on follow-up care].

PubMed

Avila, P; Marcos, M; Avila, J J; Laso, F J

2008-11-01

The problem of high rates of patient drop-out in alcohol treatment programs is frequently reported in the literature. Our aim was to investigate if internal medicine referral could improve abstinence and retention rates in a cohort of alcoholic patients. A retrospective observational study was conducted comparing 200 alcoholic patients attending a psychiatric unit (group 1) with 100 patients attending both this unit and an internal medicine unit (group 2). We collected sociodemographic and clinical variables and analysed differences regarding abstinence and retention rates by means of univariate and multivariate analysis. At 3 and 12 months follow-up, group 2 patients had higher retention and abstinence rates than group 1 patients. Multivariate analysis including potential confounding variables showed that independent predictors of one-year retention were internal medicine referral and being married. Independent predictors of one-year abstinence were being married, age > 44 years and receipt of drug treatment. The higher retention rate found among patients referred to Internal Medicine specialists, a result that has not been previously reported to the best of our knowledge, emphasizes the importance of a multidisciplinary team approach in the treatment of alcoholism.

Parafunctional habits are associated cumulatively to painful temporomandibular disorders in adolescents.

PubMed

Fernandes, Giovana; Franco-Micheloni, Ana Lúcia; Siqueira, José Tadeu Tesseroli; Gonçalves, Daniela Aparecida Godói; Camparis, Cinara Maria

2016-01-01

This cross-sectional study was designed to evaluate the effect of sleep bruxism, awake bruxism and parafunctional habits, both separately and cumulatively, on the likelihood of adolescents to present painful TMD. The study was conducted on a sample of 1,094 adolescents (aged 12-14). The presence of painful TMD was assessed using the Research Diagnostic Criteria for Temporomandibular Disorders, Axis I. Data on sleep bruxism, awake bruxism and parafunctional habits (nail/pen/pencil/lip/cheek biting, resting one's head on one's hand, and gum chewing) were researched by self-report. After adjusting for potential demographic confounders using logistic regression, each of the predictor variables (sleep bruxism, awake bruxism and parafunctional habits) was significantly associated with painful TMD. In addition, the odds for painful TMD were higher in the concomitant presence of two (OR=4.6, [95%CI=2.06, 10.37]) or three predictor (OR=13.7, [95%CI=5.72, 32.96]) variables. These findings indicate that the presence of concomitant muscle activities during sleep and awake bruxism and parafunctional habits increases the likelihood almost linearly of adolescents to present painful TMD.

Predicting adolescents' intake of fruits and vegetables.

PubMed

Lytle, Leslie A; Varnell, Sherri; Murray, David M; Story, Mary; Perry, Cheryl; Birnbaum, Amanda S; Kubik, Martha Y

2003-01-01

To explore potential predictors of adolescents' fruit and vegetable intake by expanding on current theory and drawing from other adolescent research. This research reports on baseline and interim data from a school-based intervention study. Data were collected through surveys administered to students at the beginning and end of their 7th grade year. The students attended 16 public schools in Minnesota. Data were collected on 3878 students; approximately half were female and 67% were white. All students in the 7th grade cohort were invited to participate in the surveys and over 94% completed both surveys. Our dependent variable, fruit and vegetable intake, was assessed by a validated fruit and vegetable food frequency scale. Predictive factors assessed included parenting style, spirituality/religiosity, depressive symptoms, and other commonly assessed predictors. Generalized linear mixed model regression. Omnibus test of association using P <.05 is reported. Subjective norms, barriers, knowledge, usual food choice, parenting style, spirituality/religiosity, and depressive symptoms were statistically significant predictors of intake. The model explained about 31% of the variance in fruit and vegetable consumption. To better understand adolescents' fruit and vegetable intake, we must explore novel predictors. Our results need to be replicated, and more exploratory research in this field is needed.

Potential predictors of psychological distress and well-being in medical students: a cross-sectional pilot study.

PubMed

Bore, Miles; Kelly, Brian; Nair, Balakrishnan

2016-01-01

Research has consistently found that the proportion of medical students who experience high levels of psychological distress is significantly greater than that found in the general population. The aim of our research was to assess the levels of psychological distress more extensively than has been done before, and to determine likely predictors of distress and well-being. In 2013, students from an Australian undergraduate medical school (n=127) completed a questionnaire that recorded general demographics, hours per week spent studying, in paid work, volunteer work, and physical exercise; past and current physical and mental health, social support, substance use, measures of psychological distress (Kessler Psychological Distress Scale, depression, anxiety, stress, burnout); and personality traits. Females were found to have higher levels of psychological distress than males. However, in regression analysis, the effect of sex was reduced to nonsignificance when other variables were included as predictors of psychological distress. The most consistent significant predictors of our 20 indicators of psychological distress were social support and the personality traits of emotional resilience and self-control. The findings suggest that emotional resilience skills training embedded into the medical school curriculum could reduce psychological distress among medical students.

Illness beliefs and psychological outcome in people with Parkinson's disease.

PubMed

Simpson, Jane; Lekwuwa, Godwin; Crawford, Trevor

2013-06-01

Illness beliefs are important predictors of psychological outcome in people with chronic illness and evidence suggests these could also be significant in furthering our understanding of psychological functioning in people with Parkinson's disease. Illness beliefs are specific, dynamic representations of an illness and cover dimensions such as cause, identity, consequences and controllability. Eighty-one people with Parkinson's disease completed a series of questionnaires to provide demographic, clinical and psychosocial data, which were then used to assess the relative impact of illness beliefs on their psychological functioning. Psychological functioning was assessed by measuring levels of depression, anxiety, stress, positive affect and emotional well-being. Hierarchical block regression indicated that illness beliefs were important independent predictors across some but not all outcomes and the results emphasised the importance of testing new predictors against more established predictors of outcome such as physical functioning and self-esteem. The illness beliefs most important in psychological outcome in people with PD were causal beliefs (particularly in psychosocial causes) and illness coherence (the level of understanding of the illness). The therapeutic potential of psychosocial variables was discussed given that these can be modified during therapy and this change can positively influence psychological outcome.

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.

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.

Abiotic Factors Affecting Benthic Invertebrate Biomass and Community Structure in a Fourth-Order Rocky Mountain Watershed

NASA Astrophysics Data System (ADS)

Chanat, J. G.; Clements, W. H.; MacDonald, L. H.

2005-05-01

The potential ecological impact of excess streambed sediment resulting from forest management activities is a persistent concern for land managers. This study examined the relationship between streambed sediment, along with other site- and reach-scale abiotic factors, and benthic macroinvertebrate community structure in a 272 km2 basin in the Colorado Front Range. Physical habitat parameters and invertebrates were sampled in late summer at 68 sites located in sixteen stream reaches. Invertebrate data were used to formulate twenty indices of community structure. Multiple regression identified site-level substrate particle size as the most important predictor of six indices, including total density (R2 = 0.22), biomass (R2 = 0.17), and taxa richness (R2 = 0.32). All of the remaining fourteen indices were most strongly predicted by reach-level variables, including discharge (percent shredders, R2 = 0.24; Plecoptera density, R2 = 0.29), and elevation (percent collector-filterers, R2 = 0.28; Trichoptera density, R2 = 0.37). Although the sites represented a wide range of substrate composition and embeddedness, no physical variable associated with fine sediment appeared as a strong predictor of any of the twenty indices. Thus, sediment is not among the most important factors associated with site-to-site variability of benthic community structure in this relatively pristine watershed.

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

Methodological considerations regarding response bias effect in substance use research: is correlation between the measured variables sufficient?

PubMed Central

2011-01-01

Efforts for drug free sport include developing a better understanding of the behavioural determinants that underline doping with an increased interest in developing anti-doping prevention and intervention programmes. Empirical testing of both is dominated by self-report questionnaires, which is the most widely used method in psychological assessments and sociology polls. Disturbingly, the potential distorting effect of socially desirable responding (SD) is seldom considered in doping research, or dismissed based on weak correlation between some SD measure and the variables of interest. The aim of this report is to draw attention to i) the potential distorting effect of SD and ii) the limitation of using correlation analysis between a SD measure and the individual measures. Models of doping opinion as a potentially contentious issue was tested using structural equation modeling technique (SEM) with and without the SD variable, on a dataset of 278 athletes, assessing the SD effect both at the i) indicator and ii) construct levels, as well as iii) testing SD as an independent variable affecting expressed doping opinion. Participants were categorised by their SD score into high- and low SD groups. Based on low correlation coefficients (<|0.22|) observed in the overall sample, SD effect on the indicator variables could be disregarded. Regression weights between predictors and the outcome variable varied between groups with high and low SD but despite the practically non-existing relationship between SD and predictors (<|0.11|) in the low SD group, both groups showed improved model fit with SD, independently. The results of this study clearly demonstrate the presence of SD effect and the inadequacy of the commonly used pairwise correlation to assess social desirability at model level. In the absence of direct observation of the target behaviour (i.e. doping use), evaluation of the effectiveness of future anti-doping campaign, along with empirical testing of refined doping behavioural models, will likely to continue to rely on self-reported information. Over and above controlling the effect of socially desirable responding in research that makes inferences based on self-reported information on social cognitive and behavioural measures, it is recommended that SD effect is appropriately assessed during data analysis. PMID:21244663

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.

Elder mistreatment and physical health among older adults: the South Carolina Elder Mistreatment Study.

PubMed

Cisler, Josh M; Amstadter, Ananda B; Begle, Angela M; Hernandez, Melba; Acierno, Ron

2010-08-01

Exposure to potentially traumatic events (PTEs), including interpersonal violence, is associated with poorer physical health in young adults. This relation has not been well-investigated among older adults in specific populations. The present study was designed to investigate whether exposure to PTEs and elder mistreatment are associated with physical health status among older adults residing in South Carolina. Older adults aged 60 and above (N = 902) participated in a structured interview assessing elder mistreatment history, PTEs, demographics, and social dependency variables. Results demonstrated that PTEs were associated with poor self-rated health independently and when controlling for other significant predictors. A recent history of emotional mistreatment was associated with poor self-rated health independently, but not when controlling for other significant predictors.

Neighborhood Social Predictors of Weight-related Measures in Underserved African Americans in the PATH Trial.

PubMed

McDaniel, Tyler C; Wilson, Dawn K; Coulon, Sandra M; Hand, Gregory A; Siceloff, E Rebekah

2015-11-05

African Americans have the highest rate of obesity in the United States relative to other ethnic minority groups. Bioecological factors including neighborhood social and physical environmental variables may be important predictors of weight-related measures specifically body mass index (BMI) in African American adults. Baseline data from the Positive Action for Today's Health (PATH) trial were collected from 417 African American adults. Overall a multiple regression model for BMI was significant, showing positive associations with average daily moderate-to-vigorous physical activity (MVPA) (B =-.21, P<.01) and neighborhood social interaction (B =-.13, P<.01). Consistent with previous literature, results show that neighborhood social interaction was associated with healthier BMI, highlighting it as a potential critical factor for future interventions in underserved, African American communities.

Are we drawing the right conclusions from randomised placebo-controlled trials? A post-hoc analysis of data from a randomised controlled trial

PubMed Central

2009-01-01

Background Assumptions underlying placebo controlled trials include that the placebo effect impacts on all study arms equally, and that treatment effects are additional to the placebo effect. However, these assumptions have recently been challenged, and different mechanisms may potentially be operating in the placebo and treatment arms. The objective of the current study was to explore the nature of placebo versus pharmacological effects by comparing predictors of the placebo response with predictors of the treatment response in a randomised, placebo-controlled trial of a phytotherapeutic combination for the treatment of menopausal symptoms. A substantial placebo response was observed but no significant difference in efficacy between the two arms. Methods A post hoc analysis was conducted on data from 93 participants who completed this previously published study. Variables at baseline were investigated as potential predictors of the response on any of the endpoints of flushing, overall menopausal symptoms and depression. Focused tests were conducted using hierarchical linear regression analyses. Based on these findings, analyses were conducted for both groups separately. These findings are discussed in relation to existing literature on placebo effects. Results Distinct differences in predictors were observed between the placebo and active groups. A significant difference was found for study entry anxiety, and Greene Climacteric Scale (GCS) scores, on all three endpoints. Attitude to menopause was found to differ significantly between the two groups for GCS scores. Examination of the individual arms found anxiety at study entry to predict placebo response on all three outcome measures individually. In contrast, low anxiety was significantly associated with improvement in the active treatment group. None of the variables found to predict the placebo response was relevant to the treatment arm. Conclusion This study was a post hoc analysis of predictors of the placebo versus treatment response. Whilst this study does not explore neurobiological mechanisms, these observations are consistent with the hypotheses that 'drug' effects and placebo effects are not necessarily additive, and that mutually exclusive mechanisms may be operating in the two arms. The need for more research in the area of mechanisms and mediators of placebo versus active responses is supported. Trial Registration International Clinical Trials Registry ISRCTN98972974. PMID:19549306

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.

Variability in baseline travel behaviour as a predictor of changes in commuting by active travel, car and public transport: a natural experimental study

PubMed Central

Heinen, Eva; Ogilvie, David

2016-01-01

Purpose To strengthen our understanding of the impact of baseline variability in mode choice on the likelihood of travel behaviour change. Methods Quasi-experimental analyses in a cohort study of 450 commuters exposed to a new guided busway with a path for walking and cycling in Cambridge, UK. Exposure to the intervention was defined using the shortest network distance from each participant’s home to the busway. Variability in commuter travel behaviour at baseline was defined using the Herfindahl–Hirschman Index, the number of different modes of transport used over a week, and the proportion of trips made by the main (combination of) mode(s). The outcomes were changes in the share of commute trips (i) involving any active travel, (ii) involving any public transport, and (iii) made entirely by car. Variability and change data were derived from a self-reported seven-day record collected before (2009) and after (2012) the intervention. Separate multinomial regression models were estimated to assess the influence of baseline variability on behaviour change, both independently and as an interaction effect with exposure to the intervention. Results All three measures of variability predicted changes in mode share in most models. The effect size for the intervention was slightly strengthened after including variability. Commuters with higher baseline variability were more likely to increase their active mode share (e.g. for HHI: relative risk ratio [RRR] for interaction 3.34, 95% CI 1.41, 7.89) and decrease their car mode share in response to the intervention (e.g. for HHI: RRR 7.50, 95% CI 2.52, 22.34). Conclusions People reporting a higher level of variability in mode choice were more likely to change their travel behaviour following an intervention. Future research should consider such variability as a potential predictor and effect modifier of travel and physical activity behaviour change, and its significance for the design and targeting of interventions. PMID:27200265

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).

Social determinants and their interference in homicide rates in a city in northeastern Brazil.

PubMed

de Sousa, Geziel dos Santos; Magalhães, Francismeire Brasileiro; Gama, Isabelle da Silva; de Lima, Maria Vilma Neves; de Almeida, Rosa Lívia Freitas; Vieira, Luiza Jane Eyre de Souza; Bezerra Filho, José Gomes

2014-01-01

This paper aims to analyze the possible relationship between social determinants and homicide mortality in Fortaleza (CE), Brazil. To investigate whether the rate of mortality by homicides is related to social determinants, an ecological study with emphasis on spatial analysis was conducted in the city of Fortaleza. Social, economic, demographic and sanitation data, as well as information regarding years of potential life lost, and Human Development Index were collected. The dependent variable was the rate of homicides in the period 2004 to 2006. In order to verify the relationship between the outcome variable and the predictor variables, we performed a multivariate linear regression model. We found associations between social determinants and the rate of mortality by homicides. Variables related to income and education were proven determinants for mortality. The multiple regression model showed that 51% of homicides in Fortaleza neighborhoods are explained by years of potential life lost, proportion of households with poor housing, average years of schooling, per capita income and percentage of household heads with 15 or more years of study. The coefficients for years of potential life lost and households with poor housing were positive. The findings indicate that the mortality by homicide is associated with high levels of poverty and uncontrolled urbanization, which migrates to the peripheries of urban centers.

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%.

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.

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

Predictors of early versus late smoking abstinence within a 24-month disease management program.

PubMed

Cox, Lisa Sanderson; Wick, Jo A; Nazir, Niaman; Cupertino, A Paula; Mussulman, Laura M; Ahluwalia, Jasjit S; Ellerbeck, Edward F

2011-03-01

Standard smoking cessation treatment studies have been limited to 6- to 12-month follow-up, and examination of predictors of abstinence has been restricted to this timeframe. The KanQuit study enrolled 750 rural smokers across all stages of readiness to stop smoking and provided pharmacotherapy management and/or disease management, including motivational interviewing (MI) counseling every 6 months over 2 years. This paper examines differences in predictors of abstinence following initial (6-month) and extended (24-month) intervention. Baseline variables were analyzed as potential predictors of self-reported smoking abstinence at Month 6 and at Month 24. Chi-square tests, 2-sample t tests, and multiple logistic regression analyses were used to identify predictors of abstinence among 592 participants who completed assessment at baseline and Months 6 and 24. Controlling for treatment group, the final regression models showed that male gender and lower baseline cigarettes per day predicted abstinence at both 6 and 24 months. While remaining significant, the relative advantage of being male decreased over time. Global motivation to stop smoking, controlled motivation, and self-efficacy predicted abstinence at 6 months but did not predict abstinence at Month 24. In contrast, stage of change was strongly predictive of 24-month smoking status. While the importance of some predictors of successful smoking cessation appeared to diminish over time, initial lack of interest in cessation and number of cigarettes per day strongly predicted continued smoking following a 2-year program.

Parental engagement in preventive parenting programs for child mental health: a systematic review of predictors and strategies to increase engagement

PubMed Central

Finan, Samantha J.; Swierzbiolek, Brooke; Priest, Naomi; Warren, Narelle

2018-01-01

Background Child mental health problems are now recognised as a key public health concern. Parenting programs have been developed as one solution to reduce children’s risk of developing mental health problems. However, their potential for widespread dissemination is hindered by low parental engagement, which includes intent to enrol, enrolment, and attendance. To increase parental engagement in preventive parenting programs, we need a better understanding of the predictors of engagement, and the strategies that can be used to enhance engagement. Method Employing a PRISMA method, we conducted a systematic review of the predictors of parent engagement and engagement enhancement strategies in preventive parenting programs. Key inclusion criteria included: (1) the intervention is directed primarily at the parent, (2) parent age >18 years, the article is (3) written in English and (4) published between 2004–2016. Stouffer’s method of combining p-values was used to determine whether associations between variables were reliable. Results Twenty-three articles reported a variety of predictors of parental engagement and engagement enhancement strategies. Only one of eleven predictors (child mental health symptoms) demonstrated a reliable association with enrolment (Stouffer’s p < .01). Discussion There was a lack of consistent evidence for predictors of parental engagement. Nonetheless, preliminary evidence suggests that engagement enhancement strategies modelled on theories, such as the Health Belief Model and Theory of Planned Behaviour, may increase parents’ engagement. Systematic review registration PROSPERO CRD42014013664. PMID:29719737

Role of Adult Attachment in the Intergenerational Transmission of Violence: Mediator, Moderator, or Independent Predictor?

DTIC Science & Technology

2002-05-02

scores on validated measures of adult CPA risk such as the Child Abuse Potential (CAP) Inventory (Milner, 1986, 1994; e.g., Crouch, Milner, & Thomsen...demographic variables), it is impossible to determine the unique impact of each type of child abuse on adult attachment. Two studies have...incest. Child Abuse & Neglect, 22, 45-61. Alexander, P. C., Moore, S., & Alexander, E. R. III (2001). What is transmitted in the intergenerational

Magnitude and Determinants of the Ratio between Prevalence of Low Vision and Blindness in Rapid Assessment of Avoidable Blindness Surveys.

PubMed

Kaphle, Dinesh; Lewallen, Susan

2017-10-01

To determine the magnitude and determinants of the ratio between prevalence of low vision and prevalence of blindness in rapid assessment of avoidable blindness (RAAB) surveys globally. Standard RAAB reports were downloaded from the repository or requested from principal investigators. Potential predictor variables included prevalence of uncorrected refractive error (URE) as well as gross domestic product (GDP) per capita, health expenditure per capita of the country across World Bank regions. Univariate and multivariate linear regression were used to investigate the correlation between potential predictor variables and the ratio. The results of 94 surveys from 43 countries showed that the ratio ranged from 1.35 in Mozambique to 11.03 in India with a median value of 3.90 (Interquartile range 3.06;5.38). Univariate regression analysis showed that prevalence of URE (p = 0.04), logarithm of GDP per capita (p = 0.01) and logarithm of health expenditure per capita (p = 0.03) were significantly associated with the higher ratio. However, only prevalence of URE was found to be significant in multivariate regression analysis (p = 0.03). There is a wide variation in the ratio of the prevalence of low vision to the prevalence of blindness. Eye care service utilization indicators such as the prevalence of URE may explain some of the variation across the regions.

Urinary phthalate metabolites and their biotransformation products: predictors and temporal variability among men and women

PubMed Central

Meeker, John D.; Calafat, Antonia M.; Hauser, Russ

2012-01-01

Most epidemiology studies investigating potential adverse health effects in relation to phthalates measure the urinary concentration of the free plus glucuronidated species of phthalate metabolites (i.e., total concentration) to estimate exposure. However, the free species may represent the biologically relevant dose. In this study, we collected 943 urine samples from 112 men and 157 women and assessed the between- and within-person variability and predictors of a) the free and total urinary concentrations of phthalate metabolites, and b) the percentage of free phthalate metabolites (a potential phenotypic indicator of individual susceptibility). We also explored the proportion of urinary di-(2-ethylhexyl) phthalate (DEHP) metabolites contributed to by the bioactive mono-2-ethylhexyl phthalate (MEHP), considered a possible indicator of susceptibility to phthalate exposure. The percentage of phthalate metabolites present in the free form were less stable over time than the total metabolite concentration, and, therefore, it is not likely a useful indicator of metabolic susceptibility. Thus, the added costs and effort involved in the measurement of free in addition to total metabolite concentrations in large-scale studies may not be justified. Conversely, the proportion of DEHP metabolites contributed to by MEHP was more stable within individuals over time and may be a promising indicator of susceptibility if time of day of sample collection is carefully considered. PMID:22354176

Predicting Use of Nurse Care Coordination by Older Adults With Chronic Conditions.

PubMed

Vanderboom, Catherine E; Holland, Diane E; Mandrekar, Jay; Lohse, Christine M; Witwer, Stephanie G; Hunt, Vicki L

2017-07-01

To be effective, nurse care coordination must be targeted at individuals who will use the service. The purpose of this study was to identify variables that predicted use of care coordination by primary care patients. Data on the potential predictor variables were obtained from patient interviews, the electronic health record, and an administrative database of 178 adults eligible for care coordination. Use of care coordination was obtained from an administrative database. A multivariable logistic regression model was developed using a bootstrap sampling approach. Variables predicting use of care coordination were dependence in both activities of daily living (ADL) and instrumental activities of daily living (IADL; odds ratio [OR] = 5.30, p = .002), independent for ADL but dependent for IADL (OR = 2.68, p = .01), and number of prescription medications (OR = 1.12, p = .002). Consideration of these variables may improve identification of patients to target for care coordination.

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.

Identifying dissolved oxygen variability and stress in tidal freshwater streams of northern New Zealand.

PubMed

Wilding, Thomas K; Brown, Edmund; Collier, Kevin J

2012-10-01

Tidal streams are ecologically important components of lotic network, and we identify dissolved oxygen (DO) depletion as a potentially important stressor in freshwater tidal streams of northern New Zealand. Other studies have examined temporal DO variability within rivers and we build on this by examining variability between streams as a basis for regional-scale predictors of risk for DO stress. Diel DO variability in these streams was driven by: (1) photosynthesis by aquatic plants and community respiration which produced DO maxima in the afternoon and minima early morning (range, 0.6-4.7 g/m(3)) as a product of the solar cycle and (2) tidal variability as a product of the lunar cycle, including saline intrusions with variable DO concentrations plus a small residual effect on freshwater DO for low-velocity streams. The lowest DO concentrations were observed during March (early autumn) when water temperatures and macrophyte biomass were high. Spatial comparisons indicated that low-gradient tidal streams were at greater risk of DO depletions harmful to aquatic life. Tidal influence was stronger in low-gradient streams, which typically drain more developed catchments, have lower reaeration potential and offer conditions more suitable for aquatic plant proliferation. Combined, these characteristics supported a simple method based on the extent of low-gradient channel for identifying coastal streams at risk of DO depletion. High-risk streams can then be targeted for riparian planting, nutrient limits and water allocation controls to reduce potential ecological stress.

Myocardial injury after noncardiac surgery: a large, international, prospective cohort study establishing diagnostic criteria, characteristics, predictors, and 30-day outcomes.

PubMed

Botto, Fernando; Alonso-Coello, Pablo; Chan, Matthew T V; Villar, Juan Carlos; Xavier, Denis; Srinathan, Sadeesh; Guyatt, Gordon; Cruz, Patricia; Graham, Michelle; Wang, C Y; Berwanger, Otavio; Pearse, Rupert M; Biccard, Bruce M; Abraham, Valsa; Malaga, German; Hillis, Graham S; Rodseth, Reitze N; Cook, Deborah; Polanczyk, Carisi A; Szczeklik, Wojciech; Sessler, Daniel I; Sheth, Tej; Ackland, Gareth L; Leuwer, Martin; Garg, Amit X; Lemanach, Yannick; Pettit, Shirley; Heels-Ansdell, Diane; Luratibuse, Giovanna; Walsh, Michael; Sapsford, Robert; Schünemann, Holger J; Kurz, Andrea; Thomas, Sabu; Mrkobrada, Marko; Thabane, Lehana; Gerstein, Hertzel; Paniagua, Pilar; Nagele, Peter; Raina, Parminder; Yusuf, Salim; Devereaux, P J; Devereaux, P J; Sessler, Daniel I; Walsh, Michael; Guyatt, Gordon; McQueen, Matthew J; Bhandari, Mohit; Cook, Deborah; Bosch, Jackie; Buckley, Norman; Yusuf, Salim; Chow, Clara K; Hillis, Graham S; Halliwell, Richard; Li, Stephen; Lee, Vincent W; Mooney, John; Polanczyk, Carisi A; Furtado, Mariana V; Berwanger, Otavio; Suzumura, Erica; Santucci, Eliana; Leite, Katia; Santo, Jose Amalth do Espirirto; Jardim, Cesar A P; Cavalcanti, Alexandre Biasi; Guimaraes, Helio Penna; Jacka, Michael J; Graham, Michelle; McAlister, Finlay; McMurtry, Sean; Townsend, Derek; Pannu, Neesh; Bagshaw, Sean; Bessissow, Amal; Bhandari, Mohit; Duceppe, Emmanuelle; Eikelboom, John; Ganame, Javier; Hankinson, James; Hill, Stephen; Jolly, Sanjit; Lamy, Andre; Ling, Elizabeth; Magloire, Patrick; Pare, Guillaume; Reddy, Deven; Szalay, David; Tittley, Jacques; Weitz, Jeff; Whitlock, Richard; Darvish-Kazim, Saeed; Debeer, Justin; Kavsak, Peter; Kearon, Clive; Mizera, Richard; O'Donnell, Martin; McQueen, Matthew; Pinthus, Jehonathan; Ribas, Sebastian; Simunovic, Marko; Tandon, Vikas; Vanhelder, Tomas; Winemaker, Mitchell; Gerstein, Hertzel; McDonald, Sarah; O'Bryne, Paul; Patel, Ameen; Paul, James; Punthakee, Zubin; Raymer, Karen; Salehian, Omid; Spencer, Fred; Walter, Stephen; Worster, Andrew; Adili, Anthony; Clase, Catherine; Cook, Deborah; Crowther, Mark; Douketis, James; Gangji, Azim; Jackson, Paul; Lim, Wendy; Lovrics, Peter; Mazzadi, Sergio; Orovan, William; Rudkowski, Jill; Soth, Mark; Tiboni, Maria; Acedillo, Rey; Garg, Amit; Hildebrand, Ainslie; Lam, Ngan; Macneil, Danielle; Mrkobrada, Marko; Roshanov, Pavel S; Srinathan, Sadeesh K; Ramsey, Clare; John, Philip St; Thorlacius, Laurel; Siddiqui, Faisal S; Grocott, Hilary P; McKay, Andrew; Lee, Trevor W R; Amadeo, Ryan; Funk, Duane; McDonald, Heather; Zacharias, James; Villar, Juan Carlos; Cortés, Olga Lucía; Chaparro, Maria Stella; Vásquez, Skarlett; Castañeda, Alvaro; Ferreira, Silvia; Coriat, Pierre; Monneret, Denis; Goarin, Jean Pierre; Esteve, Cristina Ibanez; Royer, Catherine; Daas, Georges; Chan, Matthew T V; Choi, Gordon Y S; Gin, Tony; Lit, Lydia C W; Xavier, Denis; Sigamani, Alben; Faruqui, Atiya; Dhanpal, Radhika; Almeida, Smitha; Cherian, Joseph; Furruqh, Sultana; Abraham, Valsa; Afzal, Lalita; George, Preetha; Mala, Shaveta; Schünemann, Holger; Muti, Paola; Vizza, Enrico; Wang, C Y; Ong, G S Y; Mansor, Marzida; Tan, Alvin S B; Shariffuddin, Ina I; Vasanthan, V; Hashim, N H M; Undok, A Wahab; Ki, Ushananthini; Lai, Hou Yee; Ahmad, Wan Azman; Razack, Azad H A; Malaga, German; Valderrama-Victoria, Vanessa; Loza-Herrera, Javier D; De Los Angeles Lazo, Maria; Rotta-Rotta, Aida; Szczeklik, Wojciech; Sokolowska, Barbara; Musial, Jacek; Gorka, Jacek; Iwaszczuk, Pawel; Kozka, Mateusz; Chwala, Maciej; Raczek, Marcin; Mrowiecki, Tomasz; Kaczmarek, Bogusz; Biccard, Bruce; Cassimjee, Hussein; Gopalan, Dean; Kisten, Theroshnie; Mugabi, Aine; Naidoo, Prebashini; Naidoo, Rubeshan; Rodseth, Reitze; Skinner, David; Torborg, Alex; Paniagua, Pilar; Urrutia, Gerard; Maestre, Mari Luz; Santaló, Miquel; Gonzalez, Raúl; Font, Adrià; Martínez, Cecilia; Pelaez, Xavier; De Antonio, Marta; Villamor, Jose Marcial; García, Jesús Alvarez; Ferré, Maria José; Popova, Ekaterina; Alonso-Coello, Pablo; Garutti, Ignacio; Cruz, Patricia; Fernández, Carmen; Palencia, Maria; Díaz, Susana; Del Castillo, Teresa; Varela, Alberto; de Miguel, Angeles; Muñoz, Manuel; Piñeiro, Patricia; Cusati, Gabriel; Del Barrio, Maria; Membrillo, Maria José; Orozco, David; Reyes, Fidel; Sapsford, Robert J; Barth, Julian; Scott, Julian; Hall, Alistair; Howell, Simon; Lobley, Michaela; Woods, Janet; Howard, Susannah; Fletcher, Joanne; Dewhirst, Nikki; Williams, C; Rushton, A; Welters, I; Leuwer, M; Pearse, Rupert; Ackland, Gareth; Khan, Ahsun; Niebrzegowska, Edyta; Benton, Sally; Wragg, Andrew; Archbold, Andrew; Smith, Amanda; McAlees, Eleanor; Ramballi, Cheryl; Macdonald, Neil; Januszewska, Marta; Stephens, Robert; Reyes, Anna; Paredes, Laura Gallego; Sultan, Pervez; Cain, David; Whittle, John; Del Arroyo, Ana Gutierrez; Sessler, Daniel I; Kurz, Andrea; Sun, Zhuo; Finnegan, Patrick S; Egan, Cameron; Honar, Hooman; Shahinyan, Aram; Panjasawatwong, Krit; Fu, Alexander Y; Wang, Sihe; Reineks, Edmunds; Nagele, Peter; Blood, Jane; Kalin, Megan; Gibson, David; Wildes, Troy

2014-03-01

Myocardial injury after noncardiac surgery (MINS) was defined as prognostically relevant myocardial injury due to ischemia that occurs during or within 30 days after noncardiac surgery. The study's four objectives were to determine the diagnostic criteria, characteristics, predictors, and 30-day outcomes of MINS. In this international, prospective cohort study of 15,065 patients aged 45 yr or older who underwent in-patient noncardiac surgery, troponin T was measured during the first 3 postoperative days. Patients with a troponin T level of 0.04 ng/ml or greater (elevated "abnormal" laboratory threshold) were assessed for ischemic features (i.e., ischemic symptoms and electrocardiography findings). Patients adjudicated as having a nonischemic troponin elevation (e.g., sepsis) were excluded. To establish diagnostic criteria for MINS, the authors used Cox regression analyses in which the dependent variable was 30-day mortality (260 deaths) and independent variables included preoperative variables, perioperative complications, and potential MINS diagnostic criteria. An elevated troponin after noncardiac surgery, irrespective of the presence of an ischemic feature, independently predicted 30-day mortality. Therefore, the authors' diagnostic criterion for MINS was a peak troponin T level of 0.03 ng/ml or greater judged due to myocardial ischemia. MINS was an independent predictor of 30-day mortality (adjusted hazard ratio, 3.87; 95% CI, 2.96-5.08) and had the highest population-attributable risk (34.0%, 95% CI, 26.6-41.5) of the perioperative complications. Twelve hundred patients (8.0%) suffered MINS, and 58.2% of these patients would not have fulfilled the universal definition of myocardial infarction. Only 15.8% of patients with MINS experienced an ischemic symptom. Among adults undergoing noncardiac surgery, MINS is common and associated with substantial mortality.

Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2012-09-01

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.

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.

Association Between Undergraduate Performance Predictors and Academic and Clinical Performance of Osteopathic Medical Students.

PubMed

Agahi, Farshad; Speicher, Mark R; Cisek, Grace

2018-02-01

Medical schools use a variety of preadmission indices to select potential students. These indices generally include undergraduate grade point average (GPA), Medical College Admission Test (MCAT) scores, and preadmission interviews. To investigate whether the admission indices used by Midwestern University Arizona College of Osteopathic Medicine are associated with the academic and clinical performance of their students. Associations between the prematriculation variables of undergraduate science GPA, undergraduate total GPA, MCAT component scores, and interview scores and the academic and clinical variables of the first- and second-year medical school GPA, Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Cognitive Evaluation (CE) total and discipline scores, scores in clinical rotations for osteopathic competencies, COMLEX-USA Level 2-Performance Evaluation passage, and match status were evaluated. Two-tailed Pearson product-moment correlations with a Bonferroni adjustment were used to examine these relationships. The traditional predictors of science and total undergraduate GPA as well as total and component MCAT scores had small to moderate associations with first- and second-year GPA, as well as COMLEX-USA Level 1 and Level 2-CE total scores. Of all predictors, only the MCAT biological sciences score had a statistically significant correlation with failure of the COMLEX-USA Level 2-Performance Evaluation examination (P=.009). Average interview scores were associated only with the osteopathic competency of medical knowledge (r=0.233; n=209; P=.001), as assessed by clerkship preceptors. No predictors were associated with scores in objective structured clinical encounters or with failing to match to a residency position. The data indicate that traditional predictors of academic performance (undergraduate GPA, undergraduate science GPA, and MCAT scores) have small to moderate association with medical school grades and performance on COMLEX-USA Level 1 and Level 2-CE. This finding requires additional research into the value of the interview in the medical school admissions process and the availability of alternatives that allow better prediction and assessment of applicant performance.

Influence of BMI and dietary restraint on self-selected portions of prepared meals in US women.

PubMed

Labbe, David; Rytz, Andréas; Brunstrom, Jeffrey M; Forde, Ciarán G; Martin, Nathalie

2017-04-01

The rise of obesity prevalence has been attributed in part to an increase in food and beverage portion sizes selected and consumed among overweight and obese consumers. Nevertheless, evidence from observations of adults is mixed and contradictory findings might reflect the use of small or unrepresentative samples. The objective of this study was i) to determine the extent to which BMI and dietary restraint predict self-selected portion sizes for a range of commercially available prepared savoury meals and ii) to consider the importance of these variables relative to two previously established predictors of portion selection, expected satiation and expected liking. A representative sample of female consumers (N = 300, range 18-55 years) evaluated 15 frozen savoury prepared meals. For each meal, participants rated their expected satiation and expected liking, and selected their ideal portion using a previously validated computer-based task. Dietary restraint was quantified using the Dutch Eating Behaviour Questionnaire (DEBQ-R). Hierarchical multiple regression was performed on self-selected portions with age, hunger level, and meal familiarity entered as control variables in the first step of the model, expected satiation and expected liking as predictor variables in the second step, and DEBQ-R and BMI as exploratory predictor variables in the third step. The second and third steps significantly explained variance in portion size selection (18% and 4%, respectively). Larger portion selections were significantly associated with lower dietary restraint and with lower expected satiation. There was a positive relationship between BMI and portion size selection (p = 0.06) and between expected liking and portion size selection (p = 0.06). Our discussion considers future research directions, the limited variance explained by our model, and the potential for portion size underreporting by overweight participants. Copyright © 2016 Nestec S.A. Published by Elsevier Ltd.. All rights reserved.

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.

Drug Concentration Thresholds Predictive of Therapy Failure and Death in Children With Tuberculosis: Bread Crumb Trails in Random Forests

PubMed Central

Swaminathan, Soumya; Pasipanodya, Jotam G.; Ramachandran, Geetha; Hemanth Kumar, A. K.; Srivastava, Shashikant; Deshpande, Devyani; Nuermberger, Eric; Gumbo, Tawanda

2016-01-01

Background. The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown. Methods. Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables. Results. Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28–5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08–4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99–11.82). Conclusions. We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis. PMID:27742636

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.

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.

Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India

PubMed Central

Das, Arundhati; Nagendra, Harini; Anand, Madhur; Bunyan, Milind

2015-01-01

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation. PMID:26121353

Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India.

PubMed

Das, Arundhati; Nagendra, Harini; Anand, Madhur; Bunyan, Milind

2015-01-01

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000 m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300 m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

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

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

PubMed

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

2018-02-01

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

PREDICTORS OF COMPUTER USE IN COMMUNITY-DWELLING ETHNICALLY DIVERSE OLDER ADULTS

PubMed Central

Werner, Julie M.; Carlson, Mike; Jordan-Marsh, Maryalice; Clark, Florence

2011-01-01

Objective In this study we analyzed self-reported computer use, demographic variables, psychosocial variables, and health and well-being variables collected from 460 ethnically diverse, community-dwelling elders in order to investigate the relationship computer use has with demographics, well-being and other key psychosocial variables in older adults. Background Although younger elders with more education, those who employ active coping strategies, or those who are low in anxiety levels are thought to use computers at higher rates than others, previous research has produced mixed or inconclusive results regarding ethnic, gender, and psychological factors, or has concentrated on computer-specific psychological factors only (e.g., computer anxiety). Few such studies have employed large sample sizes or have focused on ethnically diverse populations of community-dwelling elders. Method With a large number of overlapping predictors, zero-order analysis alone is poorly equipped to identify variables that are independently associated with computer use. Accordingly, both zero-order and stepwise logistic regression analyses were conducted to determine the correlates of two types of computer use: email and general computer use. Results Results indicate that younger age, greater level of education, non-Hispanic ethnicity, behaviorally active coping style, general physical health, and role-related emotional health each independently predicted computer usage. Conclusion Study findings highlight differences in computer usage, especially in regard to Hispanic ethnicity and specific health and well-being factors. Application Potential applications of this research include future intervention studies, individualized computer-based activity programming, or customizable software and user interface design for older adults responsive to a variety of personal characteristics and capabilities. PMID:22046718

Predictors of computer use in community-dwelling, ethnically diverse older adults.

PubMed

Werner, Julie M; Carlson, Mike; Jordan-Marsh, Maryalice; Clark, Florence

2011-10-01

In this study, we analyzed self-reported computer use, demographic variables, psychosocial variables, and health and well-being variables collected from 460 ethnically diverse, community-dwelling elders to investigate the relationship computer use has with demographics, well-being, and other key psychosocial variables in older adults. Although younger elders with more education, those who employ active coping strategies, or those who are low in anxiety levels are thought to use computers at higher rates than do others, previous research has produced mixed or inconclusive results regarding ethnic, gender, and psychological factors or has concentrated on computer-specific psychological factors only (e.g., computer anxiety). Few such studies have employed large sample sizes or have focused on ethnically diverse populations of community-dwelling elders. With a large number of overlapping predictors, zero-order analysis alone is poorly equipped to identify variables that are independently associated with computer use. Accordingly, both zero-order and stepwise logistic regression analyses were conducted to determine the correlates of two types of computer use: e-mail and general computer use. Results indicate that younger age, greater level of education, non-Hispanic ethnicity, behaviorally active coping style, general physical health, and role-related emotional health each independently predicted computer usage. Study findings highlight differences in computer usage, especially in regard to Hispanic ethnicity and specific health and well-being factors. Potential applications of this research include future intervention studies, individualized computer-based activity programming, or customizable software and user interface design for older adults responsive to a variety of personal characteristics and capabilities.

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

Assessment of extreme value distributions for maximum temperature in the Mediterranean area

NASA Astrophysics Data System (ADS)

Beck, Alexander; Hertig, Elke; Jacobeit, Jucundus

2015-04-01

Extreme maximum temperatures highly affect the natural as well as the societal environment Heat stress has great effects on flora, fauna and humans and culminates in heat related morbidity and mortality. Agriculture and different industries are severely affected by extreme air temperatures. Even more under climate change conditions, it is necessary to detect potential hazards which arise from changes in the distributional parameters of extreme values, and this is especially relevant for the Mediterranean region which is characterized as a climate change hot spot. Therefore statistical approaches are developed to estimate these parameters with a focus on non-stationarities emerging in the relationship between regional climate variables and their large-scale predictors like sea level pressure, geopotential heights, atmospheric temperatures and relative humidity. Gridded maximum temperature data from the daily E-OBS dataset (Haylock et al., 2008) with a spatial resolution of 0.25° x 0.25° from January 1950 until December 2012 are the predictands for the present analyses. A s-mode principal component analysis (PCA) has been performed in order to reduce data dimension and to retain different regions of similar maximum temperature variability. The grid box with the highest PC-loading represents the corresponding principal component. A central part of the analyses is the model development for temperature extremes under the use of extreme value statistics. A combined model is derived consisting of a Generalized Pareto Distribution (GPD) model and a quantile regression (QR) model which determines the GPD location parameters. The QR model as well as the scale parameters of the GPD model are conditioned by various large-scale predictor variables. In order to account for potential non-stationarities in the predictors-temperature relationships, a special calibration and validation scheme is applied, respectively. Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New (2008), A European daily high-resolution gridded data set of surface temperature and precipitation for 1950 - 2006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

Prognostic models for predicting posttraumatic seizures during acute hospitalization, and at 1 and 2 years following traumatic brain injury.

PubMed

Ritter, Anne C; Wagner, Amy K; Szaflarski, Jerzy P; Brooks, Maria M; Zafonte, Ross D; Pugh, Mary Jo V; Fabio, Anthony; Hammond, Flora M; Dreer, Laura E; Bushnik, Tamara; Walker, William C; Brown, Allen W; Johnson-Greene, Doug; Shea, Timothy; Krellman, Jason W; Rosenthal, Joseph A

2016-09-01

Posttraumatic seizures (PTS) are well-recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury. Prognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post-injury were developed using a recent (2011-2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p-value < 0.20 that were used to fit initial prognostic models. Multivariable logistic regression modeling with backward-stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism). The prognostic models identified sex, craniotomy, contusion load, and pre-injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively. The prognostic model for PTS during acute hospitalization did not discriminate well. Year 1 and year 2 models showed fair to good predictive validity for PTS. Cranial surgery, although medically necessary, requires ongoing research regarding potential benefits of increased monitoring for signs of epileptogenesis, PTS prophylaxis, and/or rehabilitation/social support. Future studies should externally validate models and determine clinical utility. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Forced Expiratory Volume in 1 Second Variability Helps Identify Patients with Cystic Fibrosis at Risk of Greater Loss of Lung Function.

PubMed

Morgan, Wayne J; VanDevanter, Donald R; Pasta, David J; Foreman, Aimee J; Wagener, Jeffrey S; Konstan, Michael W

2016-02-01

To evaluate several alternative measures of forced expiratory volume in 1 second percent predicted (FEV1 %pred) variability as potential predictors of future FEV1 %pred decline in patients with cystic fibrosis. We included 13,827 patients age ≥6 years from the Epidemiologic Study of Cystic Fibrosis 1994-2002 with ≥4 FEV1 %pred measurements spanning ≥366 days in both a 2-year baseline period and a 2-year follow-up period. We predicted change from best baseline FEV1 %pred to best follow-up FEV1 %pred and change from baseline to best in the second follow-up year by using multivariable regression stratified by 4 lung-disease stages. We assessed 5 measures of variability (some as deviations from the best and some as deviations from the trend line) both alone and after controlling for demographic and clinical factors and for the slope and level of FEV1 %pred. All 5 measures of FEV1 %pred variability were predictive, but the strongest predictor was median deviation from the best FEV1 %pred in the baseline period. The contribution to explanatory power (R(2)) was substantial and exceeded the total contribution of all other factors excluding the FEV1 %pred rate of decline. Adding the other variability measures provided minimal additional value. Median deviation from the best FEV1 %pred is a simple metric that markedly improves prediction of FEV1 %pred decline even after the inclusion of demographic and clinical characteristics and the FEV1 %pred rate of decline. The routine calculation of this variability measure could allow clinicians to better identify patients at risk and therefore in need of increased intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

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

Landscape scale measures of steelhead (Oncorhynchus mykiss) bioenergetic growth rate potential in Lake Michigan and comparison with angler catch rates

USGS Publications Warehouse

Hook, T.O.; Rutherford, E.S.; Brines, Shannon J.; Geddes, C.A.; Mason, D.M.; Schwab, D.J.; Fleischer, G.W.

2004-01-01

The relative quality of a habitat can influence fish consumption, growth, mortality, and production. In order to quantify habitat quality, several authors have combined bioenergetic and foraging models to generate spatially explicit estimates of fish growth rate potential (GRP). However, the capacity of GRP to reflect the spatial distributions of fishes over large areas has not been fully evaluated. We generated landscape scale estimates of steelhead (Oncorhynchus mykiss) GRP throughout Lake Michigan for 1994-1996, and used these estimates to test the hypotheses that GRP is a good predictor of spatial patterns of steelhead catch rates. We used surface temperatures (measured with AVHRR satellite imagery) and acoustically measured steelhead prey densities (alewife, Alosa pseudoharengus) as inputs for the GRP model. Our analyses demonstrate that potential steelhead growth rates in Lake Michigan are highly variable in both space and time. Steelhead GRP tended to increase with latitude, and mean GRP was much higher during September 1995, compared to 1994 and 1996. In addition, our study suggests that landscape scale measures of GRP are not good predictors of steelhead catch rates throughout Lake Michigan, but may provide an index of interannual variation in system-wide habitat quality.

Using satellite-based measurements to explore ...

EPA Pesticide Factsheets

New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), ozone (O3)), aerosol optical properties (aerosol optical depth (AOD), Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI), temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD×AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatia

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.

Cross-sectional study of variables associated with length of stay and ICU need in open Roux-En-Y gastric bypass surgery for morbid obese patients: an exploratory analysis based on the Public Health System administrative database (Datasus) in Brazil.

PubMed

Asano, Elio Fernando; Rasera, Irineu; Shiraga, Elisabete Cristina

2012-12-01

This is an exploratory analysis of potential variables associated with open Roux-en-Y gastric bypass (RYGB) surgery hospitalization resource use pattern. Cross-sectional study based on an administrative database (DATASUS) records. Inclusion criteria were adult patients undergoing RYGB between Jan/2008 and Jun/2011. Dependent variables were length of stay (LoS) and ICU need. Independent variables were: gender, age, region, hospital volume, surgery at certified center of excellence (CoE) by the Surgical Review Corporation (SRC), teaching hospital, and year of hospitalization. Univariate and multivariate analysis (logistic regression for ICU need and linear regression for length of stay) were performed. Data from 13,069 surgeries were analyzed. In crude analysis, hospital volume was the most impactful variable associated with log-transformed LoS (1.312 ± 0.302 high volume vs. 1.670 ± 0.581 low volume, p < 0.001), whereas for ICU need it was certified CoE (odds ratio (OR), 0.016; 95% confidence interval (CI), 0.010-0.026). After adjustment by logistic regression, certified CoE remained as the strongest predictor of ICU need (OR, 0.011; 95% CI, 0.007-0.018), followed by hospital volume (OR, 3.096; 95% CI, 2.861-3.350). Age group, male gender, and teaching hospital were also significantly associated (p < 0.001). For log-transformed LoS, final model includes hospital volume (coefficient, -0.223; 95% CI, -0.250 to -0.196) and teaching hospital (coefficient, 0.375; 95% CI, 0.351-0.398). Region of Brazil was not associated with any of the outcomes. High-volume hospital was the strongest predictor for shorter LoS, whereas SRC certification was the strongest predictor of lower ICU need. Public health policies targeting an increase of efficiency and patient access to the procedure should take into account these results.

Understanding uncertainty in seagrass injury recovery: an information-theoretic approach.

PubMed

Uhrin, Amy V; Kenworthy, W Judson; Fonseca, Mark S

2011-06-01

Vessel groundings cause severe, persistent gaps in seagrass beds. Varying degrees of natural recovery have been observed for grounding injuries, limiting recovery prediction capabilities, and therefore, management's ability to focus restoration efforts where natural recovery is unlikely. To improve our capacity for predicting seagrass injury recovery, we used an information-theoretic approach to evaluate the relative contribution of specific injury attributes to the natural recovery of 30 seagrass groundings in Florida Keys National Marine Sanctuary, Florida, USA. Injury recovery was defined by three response variables examined independently: (1) initiation of seagrass colonization, (2) areal contraction, and (3) sediment in-filling. We used a global model and all possible subsets for four predictor variables: (1) injury age, (2) original injury volume, (3) original injury perimeter-to-area ratio, and (4) wave energy. Successional processes were underway for many injuries with fast-growing, opportunistic seagrass species contributing most to colonization. The majority of groundings that exhibited natural seagrass colonization also exhibited areal contraction and sediment in-filling. Injuries demonstrating colonization, contraction, and in-filling were on average older and smaller, and they had larger initial perimeter-to-area ratios. Wave energy was highest for colonizing injuries. The information-theoretic approach was unable to select a single "best" model for any response variable. For colonization and contraction, injury age had the highest relative importance as a predictor variable; wave energy appeared to be associated with second-order effects, such as sediment in-filling, which in turn, facilitated seagrass colonization. For sediment in-filling, volume and perimeter-to-area ratio had similar relative importance as predictor variables with age playing a lesser role than seen for colonization and contraction. Our findings confirm that these injuries naturally initiate seagrass colonization with the potential to recover to pre-injury conditions, but likely on a decadal scale given the slow growth of the climax species (Thalassia testudinum), which is often the most severely injured. Our analysis supports current perceptions that sediment in-filling is critical to the recovery process and indicates that in order to stabilize injuries and facilitate seagrass recovery, managers should consider immediate restorative filling procedures for injuries having an original volume >14-16 m3.

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…

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.…

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.

Intending to stay: Positive images, attitudes, and classroom experiences as influences on students' intentions to persist in science and engineering majors

NASA Astrophysics Data System (ADS)

Wyer, Mary Beth

2000-10-01

Contemporary research on persistence in undergraduate education in science and engineering has focused primarily on identifying the structural, social, and psychological barriers to participation by students in underrepresented groups. As a result, there is a wealth of data to document why students leave their majors, but there is little direct empirical data to support prevailing presumptions about why students stay. Moreover, researchers have used widely differing definitions and measures of persistence, and they have seldom explored field differences. This study compared three ways of measuring persistence. These constituted three criterion variables: commitment to major, degree aspirations, and commitment to a science/engineering career. The study emphasized social factors that encourage students to persist, including four predictor variables---(1) positive images of scientists/engineers, (2) positive attitudes toward gender and racial equality, (3) positive classroom experiences, and (4) high levels of social integration. In addition, because researchers have repeatedly documented the degree to which women are more likely than men to drop out of science and engineering majors, the study examined the potential impact of gender in relation to these predictor variables. A survey was administered in the classroom to a total of 285 students enrolled in a required course for either a biological sciences and or an engineering major. Predictor variables were developed from standard scales, including the Images of Science/Scientists Scale, the Attitudes toward Women Scale, the Women in Science Scale, and the Perceptions of Prejudice Scale. Based on logistic regression models, results indicate that positive images of scientists and engineers was significantly related to improving the odds of students having a high commitment to major, high degree aspirations, and high commitment to career. There was also evidence that positive attitudes toward gender and racial equality as well as positive classroom experiences improved the odds of students' having high degree aspirations. There was limited evidence to suggest the significance of gender in interaction with the predictor variables. There was tentative evidence that field differences may play a critical role in persistence. The study concludes on two points. The first is that gender may be a more important factor in explaining why some students leave their science and engineering majors than in explaining why others stay. The second is that research directed at improving diversity in science would benefit from discussion about the measures of persistence.

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.

Relationship among family environment, self-control, friendship quality, and adolescents’ smartphone addiction in South Korea: Findings from nationwide data

PubMed Central

Kim, Hye-Jin; Min, Jin-Young; Lee, Tae-Jin; Yoo, Seunghyun

2018-01-01

Background Many studies have examined the negative impact on smartphone addiction in adolescents. Recent concerns have focused on predictors of smartphone addiction. This study aimed to investigate the association of adolescents’ smartphone addiction with family environment (specifically, domestic violence and parental addiction). We further investigated whether self-control and friendship quality, as predictors of smartphone addiction, may reduce the observed risk. Methods We used the 2013 national survey on internet usage and utilization data from the National Information Agency of Korea. Information on exposure and covariates included self-reported experience of domestic violence and parental addiction, sociodemographic variables, and other variables potentially related to smartphone addiction. Smartphone addiction was estimated using a smartphone addiction proneness scale, a standardized measure developed by national institutions in Korea. Results Adolescents who had experienced domestic violence (OR = 1.74; 95% CI: 1.23–2.45) and parental addiction (OR = 2.01; 95% CI: 1.24–3.27) were found to be at an increased risk for smartphone addiction after controlling for all potential variables. Furthermore, on classifying adolescents according to their level of self-control and friendship quality the association between domestic violence and parental addiction, and smartphone addiction was found to be significant in the group with adolescents with lower levels of self-control (OR = 2.87; 95% CI: 1.68–4.90 and OR = 1.95; 95% CI: 1.34–2.83) and friendship quality (OR = 2.33; 95% CI: 1.41–3.85 and OR = 1.83; 95% CI: 1.26–2.64). Conclusion Our findings suggest that family dysfunction was significantly associated with smartphone addiction. We also observed that self-control and friendship quality act as protective factors against adolescents’ smartphone addiction. PMID:29401496

Relationship among family environment, self-control, friendship quality, and adolescents' smartphone addiction in South Korea: Findings from nationwide data.

PubMed

Kim, Hye-Jin; Min, Jin-Young; Min, Kyoung-Bok; Lee, Tae-Jin; Yoo, Seunghyun

2018-01-01

Many studies have examined the negative impact on smartphone addiction in adolescents. Recent concerns have focused on predictors of smartphone addiction. This study aimed to investigate the association of adolescents' smartphone addiction with family environment (specifically, domestic violence and parental addiction). We further investigated whether self-control and friendship quality, as predictors of smartphone addiction, may reduce the observed risk. We used the 2013 national survey on internet usage and utilization data from the National Information Agency of Korea. Information on exposure and covariates included self-reported experience of domestic violence and parental addiction, sociodemographic variables, and other variables potentially related to smartphone addiction. Smartphone addiction was estimated using a smartphone addiction proneness scale, a standardized measure developed by national institutions in Korea. Adolescents who had experienced domestic violence (OR = 1.74; 95% CI: 1.23-2.45) and parental addiction (OR = 2.01; 95% CI: 1.24-3.27) were found to be at an increased risk for smartphone addiction after controlling for all potential variables. Furthermore, on classifying adolescents according to their level of self-control and friendship quality the association between domestic violence and parental addiction, and smartphone addiction was found to be significant in the group with adolescents with lower levels of self-control (OR = 2.87; 95% CI: 1.68-4.90 and OR = 1.95; 95% CI: 1.34-2.83) and friendship quality (OR = 2.33; 95% CI: 1.41-3.85 and OR = 1.83; 95% CI: 1.26-2.64). Our findings suggest that family dysfunction was significantly associated with smartphone addiction. We also observed that self-control and friendship quality act as protective factors against adolescents' smartphone addiction.

The role of self-construal in predicting self-presentational motives for online social network use in the UK and Japan.

PubMed

Long, Karen; Zhang, Xiao

2014-07-01

Self-presentational motives underlying online social network (OSN) use were explored in samples of British and Japanese users. Self-expression, maintaining privacy, and attention seeking were strong motives in both samples; impression management and modesty were less strongly endorsed. Measures of independent and interdependent self-construal, as well as narcissism and modesty, were investigated as potential predictors of these motivations. Independent self-construal emerged as the most important predictor across both samples, with less independent participants showing more concern with image management and modesty. Participants with more interdependent self-construals were more concerned about maintaining privacy. There were some differences in the patterns of prediction between the samples, but overall self-construal measures contributed to the explanation of the majority of the motivations, whereas narcissistic or modest personality variables did not.

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.

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.

Intentions to Maintain Adherence to Mammography

PubMed Central

Bowling, J. Michael; Brewer, Noel T.; Lipkus, Isaac M.; Skinner, Celette Sugg; Strigo, Tara S.; Rimer, Barbara K.

2008-01-01

Abstract Objective Recent attention has focused on moving women from having initial mammograms to maintaining adherence to regular mammography schedules. We examined behavioral intentions to maintain mammography adherence, which include the likelihood of performing a behavior, and implementation intentions, specific action plans to obtain mammograms. Potential predictors were Theory of Planned Behavior constructs, previous barriers, previous mammography maintenance, and age. Methods Respondents were 2062 currently adherent women due for their next mammograms in 3–4 months according to American Cancer Society recommendations for annual screening. Statistical models were used to examine predictors of behavioral and two implementation intentions, including having thought about where women would get their next mammograms and having thought about making appointments. Results With the exception of pros, cons, and subjective norms, all variables predicted behavioral intentions (p ≤ 0.05). Stronger perceived control, previous mammography maintenance, and one barrier (vs. none) predicted being more likely to have thought about where to get their next mammograms. Previous maintenance and no barriers (vs. two) predicted being more likely to have thought about making appointments. Conclusions Our findings suggest that among women currently adherent to mammography, volitional factors, such as barriers, may be better predictors of implementation intentions than motivational factors, such as attitudes. Implementation variables may be useful in understanding how women move from intentions to action. Future research should examine how such factors relate to mammography maintenance behaviors and can be integrated into behavior change interventions. PMID:18657041

Predictors of poor sleep quality among head and neck cancer patients.

PubMed

Shuman, Andrew G; Duffy, Sonia A; Ronis, David L; Garetz, Susan L; McLean, Scott A; Fowler, Karen E; Terrell, Jeffrey E

2010-06-01

The objective of this study was to determine the predictors of sleep quality among head and neck cancer patients 1 year after diagnosis. This was a prospective, multisite cohort study of head and neck cancer patients (N = 457). Patients were surveyed at baseline and 1 year after diagnosis. Chart audits were also conducted. The dependent variable was a self-assessed sleep score 1 year after diagnosis. The independent variables were a 1 year pain score, xerostomia, treatment received (radiation, chemotherapy, and/or surgery), presence of a feeding tube and/or tracheotomy, tumor site and stage, comorbidities, depression, smoking, problem drinking, age, and sex. Both baseline (67.1) and 1-year postdiagnosis (69.3) sleep scores were slightly lower than population means (72). Multivariate analyses showed that pain, xerostomia, depression, presence of a tracheotomy tube, comorbidities, and younger age were statistically significant predictors of poor sleep 1 year after diagnosis of head and neck cancer (P < .05). Smoking, problem drinking, and female sex were marginally significant (P < .09). Type of treatment (surgery, radiation and/or chemotherapy), primary tumor site, and cancer stage were not significantly associated with 1-year sleep scores. Many factors adversely affecting sleep in head and neck cancer patients are potentially modifiable and appear to contribute to decreased quality of life. Strategies to reduce pain, xerostomia, depression, smoking, and problem drinking may be warranted, not only for their own inherent value, but also for improvement of sleep and the enhancement of quality of life.

The therapeutic alliance and therapist adherence as predictors of dropout from cognitive therapy for depression when combined with antidepressant medication.

PubMed

Cooper, Andrew A; Strunk, Daniel R; Ryan, Elizabeth T; DeRubeis, Robert J; Hollon, Steven D; Gallop, Robert

2016-03-01

Previous psychotherapy research has examined the therapeutic alliance and therapist adherence as correlates or predictors of symptom change. While some initial evidence suggests the alliance is associated with risk of dropout in cognitive behavioral treatment for depression, evidence of such relations has been limited to date. We examined the relation of these psychotherapy process variables and dropout in the context of cognitive therapy for depression when provided in combination with pharmacotherapy. Patients were randomized to the CT plus pharmacotherapy condition of a clinical trial for chronic or recurrent depression. Consistent with the spirit of personalized medicine, patients were treated until they met remission and recovery criteria (or reached the maximum allowable time in the study). In a sample of 176 patients, we examined observer-rated alliance and therapist adherence in the first three CT sessions as potential predictors of treatment dropout. The therapeutic alliance and one facet of therapist adherence (i.e., Behavioral Methods/Homework) predicted reduced odds of dropout. Therapist use of Negotiating/Structuring predicted greater likelihood of dropout, but only when other variables were included in the model. Process ratings were not available for concurrent pharmacotherapy sessions. A minority of patients did not have session recordings available. Results are consistent with the possibility that the therapeutic alliance and therapists' focus on homework and behavioral methods promote treatment retention in combined treatment for depression. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Therapeutic Alliance and Therapist Adherence as Predictors of Dropout from Cognitive Therapy for Depression when Combined with Antidepressant Medication

PubMed Central

Cooper, Andrew A.; Strunk, Daniel R.; Ryan, Elizabeth T.; DeRubeis, Robert J.; Hollon, Steven D.; Gallop, Robert

2015-01-01

BACKGROUND Previous psychotherapy research has examined the therapeutic alliance and therapist adherence as correlates or predictors of symptom change. While some initial evidence suggests the alliance is associated with risk of dropout in cognitive behavioral treatment for depression, evidence of such relations has been limited to date. We examined the relation of these psychotherapy process variables and dropout in the context of cognitive therapy for depression when provided in combination with pharmacotherapy. METHODS Patients were randomized to the CT plus pharmacotherapy condition of a clinical trial for chronic or recurrent depression. Consistent with the spirit of personalized medicine, patients were treated until they met remission and recovery criteria (or reached the maximum allowable time in the study). In a sample of 176 patients, we examined observer-rated alliance and therapist adherence in the first three CT sessions as potential predictors of treatment dropout. RESULTS The therapeutic alliance and one facet of therapist adherence (i.e., Behavioral Methods/Homework) predicted reduced odds of dropout. Therapist use of Negotiating/Structuring predicted greater likelihood of dropout, but only when other variables were included in the model. LIMITATIONS Process ratings were not available for concurrent pharmacotherapy sessions. A minority of patients did not have session recordings available. CONCLUSIONS Results are consistent with the possibility that the therapeutic alliance and therapists’ focus on homework and behavioral methods promote treatment retention in combined treatment for depression. PMID:26164110

Predictors of nonresponse in a questionnaire-based outcome study of vocational rehabilitation patients.

PubMed

Burrus, Cyrille; Ballabeni, Pierluigi; Deriaz, Olivier; Gobelet, Charles; Luthi, François

2009-09-01

To identify predictors of nonresponse to a self-report study of patients with orthopedic trauma hospitalized for vocational rehabilitation between November 15, 2003, and December 31, 2005. The role of biopsychosocial complexity, assessed using the INTERMED, was of particular interest. Cohort study. Questionnaires with quality of life, sociodemographic, and job-related questions were given to patients at hospitalization and 1 year after discharge. Sociodemographic data, biopsychosocial complexity, and presence of comorbidity were available at hospitalization (baseline) for all eligible patients. Logistic regression models were used to test a number of baseline variables as potential predictors of nonresponse to the questionnaires at each of the 2 time points. Rehabilitation clinic. Patients (N=990) hospitalized for vocational rehabilitation over a period of 2 years. Not applicable. Nonresponse to the questionnaires was the binary dependent variable. Patients with high biopsychosocial complexity, foreign native language, or low educational level were less likely to respond at both time points. Younger patients were less likely to respond at 1 year. Those living in a stable partnership were less likely than singles to respond at hospitalization. Sex, psychiatric, and somatic comorbidity and alcoholism were never associated with nonresponse. We stress the importance of assessing biopsychosocial complexity to predict nonresponse. Furthermore, the factors we found to be predictive of nonresponse are also known to influence treatment outcome and vocational rehabilitation. Therefore, it is important to increase the response rate of the groups of concern in order to reduce selection bias in epidemiologic investigations.

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.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Changing abilities vs. changing tasks: Examining validity degradation with test scores and college performance criteria both assessed longitudinally.

PubMed

Dahlke, Jeffrey A; Kostal, Jack W; Sackett, Paul R; Kuncel, Nathan R

2018-05-03

We explore potential explanations for validity degradation using a unique predictive validation data set containing up to four consecutive years of high school students' cognitive test scores and four complete years of those students' college grades. This data set permits analyses that disentangle the effects of predictor-score age and timing of criterion measurements on validity degradation. We investigate the extent to which validity degradation is explained by criterion dynamism versus the limited shelf-life of ability scores. We also explore whether validity degradation is attributable to fluctuations in criterion variability over time and/or GPA contamination from individual differences in course-taking patterns. Analyses of multiyear predictor data suggest that changes to the determinants of performance over time have much stronger effects on validity degradation than does the shelf-life of cognitive test scores. The age of predictor scores had only a modest relationship with criterion-related validity when the criterion measurement occasion was held constant. Practical implications and recommendations for future research are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Effectiveness of Collision-Involved Motorcycle Helmets in Thailand

PubMed Central

Wobrock, Jesse; Smith, Terry; Kasantikul, Vira; Whiting, William

2003-01-01

The purpose of this study was to analyze variables present in selected motorcycle crashes involving helmeted riders to find the best injury predictors. The helmets used in this study were collected from motorcycle crashes in Thailand. Pertinent data were collected, a conventional helmet impact drop test apparatus was used to quantify the head impact forces, and stepwise multiple regression analyses were performed. The results indicate that the geometry of the object impacting the head and GSI were the best predictors for MAIS (R2=.875) while geometry of the object, liner thickness and impact energy were the best predictors for ISS (R2=.911). Analysis of motor vehicle crashes in the United States in the year 2001 reveals that motorcyclist fatalities increased 7.2%, from 2,862 fatalities in 2000 to 3,067 in 2001 [NHTSA 2002]. In 2001, 59,000 motorcyclists were injured, which represents an increase of 2.0% from 2000. These statistics are indicative of the risk that motorcycle riders face in the traffic environment and warrant the need for further research focusing on injury potential in motorcycle crashes. PMID:12941212

Childhood Bullying: A Review of Constructs, Contexts, and Nursing Implications

PubMed Central

Liu, Jianghong; Graves, Nicola

2011-01-01

Bullying among children as a pervasive problem has been increasingly recognized as an important public health issue. However, while much attention has been given to understanding the impact of bullying on victims, it is equally important to examine predictors of bullying and potential outcomes for bullies themselves. The current literature on bullying lacks consensus on a utilizable definition of bullying in research, which can vary by theoretical framework. In an attempt to bridge the gaps in the literature, this paper will provide a review of the state of the science on bullying among children, including the major theoretical constructs of bullying and their respective viewpoints on predictors and correlates of bullying. A secondary aim for this paper is to summarize empirical evidence for predictors of bullying and victimization, which can provide strategies for intervention and prevention by public health nursing professionals. By calling attention to the variability in the bullying literature and the limitations of current evidence available, researchers can better address methodological gaps and effectively move toward developing studies to inform nursing treatment programs and enhance public health initiatives that reduce violence in school settings. PMID:22092466

Effort test failure: toward a predictive model.

PubMed

Webb, James W; Batchelor, Jennifer; Meares, Susanne; Taylor, Alan; Marsh, Nigel V

2012-01-01

Predictors of effort test failure were examined in an archival sample of 555 traumatically brain-injured (TBI) adults. Logistic regression models were used to examine whether compensation-seeking, injury-related, psychological, demographic, and cultural factors predicted effort test failure (ETF). ETF was significantly associated with compensation-seeking (OR = 3.51, 95% CI [1.25, 9.79]), low education (OR:. 83 [.74, . 94]), self-reported mood disorder (OR: 5.53 [3.10, 9.85]), exaggerated displays of behavior (OR: 5.84 [2.15, 15.84]), psychotic illness (OR: 12.86 [3.21, 51.44]), being foreign-born (OR: 5.10 [2.35, 11.06]), having sustained a workplace accident (OR: 4.60 [2.40, 8.81]), and mild traumatic brain injury severity compared with very severe traumatic brain injury severity (OR: 0.37 [0.13, 0.995]). ETF was associated with a broader range of statistical predictors than has previously been identified and the relative importance of psychological and behavioral predictors of ETF was evident in the logistic regression model. Variables that might potentially extend the model of ETF are identified for future research efforts.

Predict the Medicare Functional Classification Level (K-level) using the Amputee Mobility Predictor in people with unilateral transfemoral and transtibial amputation: A pilot study.

PubMed

Dillon, Michael P; Major, Matthew J; Kaluf, Brian; Balasanov, Yuri; Fatone, Stefania

2018-04-01

While Amputee Mobility Predictor scores differ between Medicare Functional Classification Levels (K-level), this does not demonstrate that the Amputee Mobility Predictor can accurately predict K-level. To determine how accurately K-level could be predicted using the Amputee Mobility Predictor in combination with patient characteristics for persons with transtibial and transfemoral amputation. Prediction. A cumulative odds ordinal logistic regression was built to determine the effect that the Amputee Mobility Predictor, in combination with patient characteristics, had on the odds of being assigned to a particular K-level in 198 people with transtibial or transfemoral amputation. For people assigned to the K2 or K3 level by their clinician, the Amputee Mobility Predictor predicted the clinician-assigned K-level more than 80% of the time. For people assigned to the K1 or K4 level by their clinician, the prediction of clinician-assigned K-level was less accurate. The odds of being in a higher K-level improved with younger age and transfemoral amputation. Ordinal logistic regression can be used to predict the odds of being assigned to a particular K-level using the Amputee Mobility Predictor and patient characteristics. This pilot study highlighted critical method design issues, such as potential predictor variables and sample size requirements for future prospective research. Clinical relevance This pilot study demonstrated that the odds of being assigned a particular K-level could be predicted using the Amputee Mobility Predictor score and patient characteristics. While the model seemed sufficiently accurate to predict clinician assignment to the K2 or K3 level, further work is needed in larger and more representative samples, particularly for people with low (K1) and high (K4) levels of mobility, to be confident in the model's predictive value prior to use in clinical practice.

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.

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.

Predicting protein function and other biomedical characteristics with heterogeneous ensembles

PubMed Central

Whalen, Sean; Pandey, Om Prakash

2015-01-01

Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a powerful approach to improving prediction performance is to construct heterogeneous ensemble predictors that combine the output of diverse individual predictors that capture complementary aspects of the problems and/or datasets. In this paper, we demonstrate the potential of such heterogeneous ensembles, derived from stacking and ensemble selection methods, for addressing PFP and other similar biomedical prediction problems. Deeper analysis of these results shows that the superior predictive ability of these methods, especially stacking, can be attributed to their attention to the following aspects of the ensemble learning process: (i) better balance of diversity and performance, (ii) more effective calibration of outputs and (iii) more robust incorporation of additional base predictors. Finally, to make the effective application of heterogeneous ensembles to large complex datasets (big data) feasible, we present DataSink, a distributed ensemble learning framework, and demonstrate its sound scalability using the examined datasets. DataSink is publicly available from https://github.com/shwhalen/datasink. PMID:26342255

Patterns and predictors of ADHD persistence into adulthood: Results from the National Comorbidity Survey Replication

PubMed Central

Kessler, Ronald C.; Adler, Lenard A.; Barkley, Russell; Biederman, Joseph; Conners, C. Keith; Faraone, Stephen V.; Greenhill, Laurence L.; Jaeger, Savina; Secnik, Kristina; Spencer, Thomas; Üstün, T. Bedirhan; Zaslavsky, Alan M.

2010-01-01

BACKGROUND Despite growing interest in adult ADHD, little is known about predictors of persistence of childhood cases into adulthood. METHODS A retrospective assessment of childhood ADHD, childhood risk factors, and a screen for adult ADHD were included in a sample of 3197 18–44 year old respondents in the National Comorbidity Survey Replication (NCS-R). Blinded adult ADHD clinical reappraisal interviews were administered to a sub-sample of respondents. Multiple imputation (MI) was used to estimate adult persistence of childhood ADHD. Logistic regression was used to study retrospectively reported childhood predictors of persistence. Potential predictors included socio-demographics, childhood ADHD severity, childhood adversity, traumatic life experiences, and comorbid DSM-IV child-adolescent disorders (anxiety, mood, impulse-control, and substance disorders). RESULTS 36.3% of respondents with retrospectively assessed childhood ADHD were classified by blinded clinical interviews as meeting DSM-IV criteria for current ADHD. Childhood ADHD severity and childhood treatment significantly predicted persistence. Controlling for severity and excluding treatment, none of the other variables significantly predicted persistence even though they were significantly associated with childhood ADHD. CONCLUSIONS No modifiable risk factors were found for adult persistence of ADHD. Further research, ideally based on prospective general population samples, is needed to search for modifiable determinants of adult persistence of ADHD. PMID:15950019

Predictors of Inappropriate Use of Diagnostic Tests and Management of Bronchiolitis

PubMed Central

Sarmiento, Lorena; Rojas-Soto, Gladys E.

2017-01-01

Background The aim of the present study was to determine predictors of inappropriate use of diagnostic tests and management of bronchiolitis in a population of hospitalized infants. Methods In an analytical cross-sectional study, we determined independent predictors of the inappropriate use of diagnostic tests and management of bronchiolitis in a population of hospitalized infants. We defined a composite outcome score as the main outcome variable. Results Of the 303 included patients, 216 (71.3%) experienced an inappropriate use of diagnostic tests and treatment of bronchiolitis. After controlling for potential confounders, it was found that atopic dermatitis (OR 5.30; CI 95% 1.14–24.79; p = 0.034), length of hospital stay (OR 1.48; CI 95% 1.08–2.03; p = 0.015), and the number of siblings (OR 1.92; CI 95% 1.13–3.26; p = 0.015) were independent predictors of an inappropriate use of diagnostic tests and treatment of the disease. Conclusions Inappropriate use of diagnostic tests and treatment of bronchiolitis was a highly prevalent outcome in our population of study. Participants with atopic dermatitis, a longer hospital stay, and a greater number of siblings were at increased risk for inappropriate use of diagnostic tests and management of the disease. PMID:28758127

The Influence of Organized Physical Activity (Including Gymnastics) on Young Adult Skeletal Traits: Is Maturity Phase Important?

PubMed

Bernardoni, Brittney; Scerpella, Tamara A; Rosenbaum, Paula F; Kanaley, Jill A; Raab, Lindsay N; Li, Quefeng; Wang, Sijian; Dowthwaite, Jodi N

2015-05-01

We prospectively evaluated adolescent organized physical activity (PA) as a factor in adult female bone traits. Annual DXA scans accompanied semiannual records of anthropometry, maturity, and PA for 42 participants in this preliminary analysis (criteria: appropriately timed DXA scans at ~1 year premenarche [predictor] and ~5 years postmenarche [dependent variable]). Regression analysis evaluated total adolescent interscan PA and PA over 3 maturity subphases as predictors of young adult bone outcomes: 1) bone mineral content (BMC), geometry, and strength indices at nondominant distal radius and femoral neck; 2) subhead BMC; 3) lumbar spine BMC. Analyses accounted for baseline gynecological age (years pre- or postmenarche), baseline bone status, adult body size and interscan body size change. Gymnastics training was evaluated as a potentially independent predictor, but did not improve models for any outcomes (p > .07). Premenarcheal bone traits were strong predictors of most adult outcomes (semipartial r2 = .21-0.59, p ≤ .001). Adult 1/3 radius and subhead BMC were predicted by both total PA and PA 1-3 years postmenarche (p < .03). PA 3-5 years postmenarche predicted femoral narrow neck width, endosteal diameter, and buckling ratio (p < .05). Thus, participation in organized physical activity programs throughout middle and high school may reduce lifetime fracture risk in females.

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.

Predictors of dropout from internet-based self-help cognitive behavioral therapy for insomnia.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Ho, Fiona Yan-Yee; Ho, Lai-Ming

2015-10-01

Dropout from self-help cognitive-behavioral therapy for insomnia (CBT-I) potentially diminishes therapeutic effect and poses clinical concern. We analyzed the characteristics of subjects who did not complete a 6-week internet-based CBT-I program. Receiver operator characteristics (ROC) analysis was used to identify potential variables and cutoff for predicting dropout among 207 participants with self-report insomnia 3 or more nights per week for at least 3 months randomly assigned to self-help CBT-I with telephone support (n = 103) and self-help CBT-I (n = 104). Seventy-two participants (34.4%) did not complete all 6 sessions, while 42 of the 72 (56.9%) dropped out prior to the fourth session. Significant predictors of non-completion are total sleep time (TST) ≥ 6.82 h, Hospital Anxiety and Depression Scale depression score ≥ 9 and Insomnia Severity Index score < 13 at baseline in this ranking order. Only TST ≥ 5.92 h predicts early dropout. Longer TST and less severe insomnia predict dropout in this study of self-help CBT-I, in contrast to shorter TST as a predictor in 2 studies of face-to-face CBT-I, while greater severity of depression predicts dropout in both this study and a study of face-to-face CBT-I. Strategies for minimizing dropout from internet-based CBT-I are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting Medical Students’ Current Attitudes Toward Psychiatry, Interest in Psychiatry, and Estimated Likelihood of Working in Psychiatry: A Cross-Sectional Study in Four European Countries

PubMed Central

Warnke, Ingeborg; Gamma, Alex; Buadze, Maria; Schleifer, Roman; Canela, Carlos; Strebel, Bernd; Tényi, Tamás; Rössler, Wulf; Rüsch, Nicolas; Liebrenz, Michael

2018-01-01

Psychiatry as a medical discipline is becoming increasingly important due to the high and increasing worldwide burden associated with mental disorders. Surprisingly, however, there is a lack of young academics choosing psychiatry as a career. Previous evidence on medical students’ perspectives is abundant but has methodological shortcomings. Therefore, by attempting to avoid previous shortcomings, we aimed to contribute to a better understanding of the predictors of the following three outcome variables: current medical students’ attitudes toward psychiatry, interest in psychiatry, and estimated likelihood of working in psychiatry. The sample consisted of N = 1,356 medical students at 45 medical schools in Germany and Austria as well as regions of Switzerland and Hungary with a German language curriculum. We used snowball sampling via Facebook with a link to an online questionnaire as recruitment procedure. Snowball sampling is based on referrals made among people. This questionnaire included a German version of the Attitudes Toward Psychiatry Scale (ATP-30-G) and further variables related to outcomes and potential predictors in terms of sociodemography (e.g., gender) or medical training (e.g., curriculum-related experience with psychiatry). Data were analyzed by linear mixed models and further regression models. On average, students had a positive attitude to and high general interest in, but low professional preference for, psychiatry. A neutral attitude to psychiatry was partly related to the discipline itself, psychiatrists, or psychiatric patients. Female gender and previous experience with psychiatry, particularly curriculum-related and personal experience, were important predictors of all outcomes. Students in the first years of medical training were more interested in pursuing psychiatry as a career. Furthermore, the country of the medical school was related to the outcomes. However, statistical models explained only a small proportion of variance. The findings indicate that particularly curriculum-related experience is important for determining attitudes toward psychiatry, interest in the subject and self-predicted professional career choice. We therefore encourage the provision of opportunities for clinical experience by psychiatrists. However, further predictor variables need to be considered in future studies. PMID:29593577

Predicting Medical Students' Current Attitudes Toward Psychiatry, Interest in Psychiatry, and Estimated Likelihood of Working in Psychiatry: A Cross-Sectional Study in Four European Countries.

PubMed

Warnke, Ingeborg; Gamma, Alex; Buadze, Maria; Schleifer, Roman; Canela, Carlos; Strebel, Bernd; Tényi, Tamás; Rössler, Wulf; Rüsch, Nicolas; Liebrenz, Michael

2018-01-01

Psychiatry as a medical discipline is becoming increasingly important due to the high and increasing worldwide burden associated with mental disorders. Surprisingly, however, there is a lack of young academics choosing psychiatry as a career. Previous evidence on medical students' perspectives is abundant but has methodological shortcomings. Therefore, by attempting to avoid previous shortcomings, we aimed to contribute to a better understanding of the predictors of the following three outcome variables: current medical students' attitudes toward psychiatry, interest in psychiatry, and estimated likelihood of working in psychiatry. The sample consisted of N  = 1,356 medical students at 45 medical schools in Germany and Austria as well as regions of Switzerland and Hungary with a German language curriculum. We used snowball sampling via Facebook with a link to an online questionnaire as recruitment procedure. Snowball sampling is based on referrals made among people. This questionnaire included a German version of the Attitudes Toward Psychiatry Scale (ATP-30-G) and further variables related to outcomes and potential predictors in terms of sociodemography (e.g., gender) or medical training (e.g., curriculum-related experience with psychiatry). Data were analyzed by linear mixed models and further regression models. On average, students had a positive attitude to and high general interest in, but low professional preference for, psychiatry. A neutral attitude to psychiatry was partly related to the discipline itself, psychiatrists, or psychiatric patients. Female gender and previous experience with psychiatry, particularly curriculum-related and personal experience, were important predictors of all outcomes. Students in the first years of medical training were more interested in pursuing psychiatry as a career. Furthermore, the country of the medical school was related to the outcomes. However, statistical models explained only a small proportion of variance. The findings indicate that particularly curriculum-related experience is important for determining attitudes toward psychiatry, interest in the subject and self-predicted professional career choice. We therefore encourage the provision of opportunities for clinical experience by psychiatrists. However, further predictor variables need to be considered in future studies.

The Effect of Ballistic Impacts on the High Cycle Fatigue Properties of Ti-48Al-2Nb-2Cr (at.%)

NASA Technical Reports Server (NTRS)

Draper, S. L.; Lerch, B. A.; Pereira, J. M.; Nathal, M. V.; Austin, C. M.; Erdman, O.

2000-01-01

The ability of gamma - TiAl to withstand potential foreign and/or domestic object damage is a technical risk to the implementation of gamma - TiAl in low pressure turbine (LPT) blade applications. The overall purpose of the present study was to determine the influence of ballistic impact damage on the high cycle fatigue strength of gamma - TiAl simulated LPT blades. Impact and specimen variables included ballistic impact energy, projectile hardness, impact temperature, impact location, and leading edge thickness. The level of damage induced by the ballistic impacting was studied and quantified on both the impact (front) and backside of the specimens. Multiple linear regression was used to model the cracking and fatigue response as a function of the impact variables. Of the impact variables studied, impact energy had the largest influence on the response of gamma - TiAl to ballistic impacting. Backside crack length was the best predictor of remnant fatigue strength for low energy impacts (<0.74J) whereas Hertzian crack length (impact side damage) was the best predictor for higher energy impacts. The impacted gamma - TiAl samples displayed a classical mean stress dependence on the fatigue strength. For the fatigue design stresses of a 6th stage LPT blade in a GE90 engine, a Ti-48Al-2Nb-2Cr LPT blade would survive an impact of normal service conditions.

The association between subgingival periodontal pathogens and systemic inflammation.

PubMed

Winning, Lewis; Patterson, Christopher C; Cullen, Kathy M; Stevenson, Kathryn A; Lundy, Fionnuala T; Kee, Frank; Linden, Gerard J

2015-09-01

To investigate associations between periodontal disease pathogens and levels of systemic inflammation measured by C-reactive protein (CRP). A representative sample of dentate 60-70-year-old men in Northern Ireland had a comprehensive periodontal examination. Men taking statins were excluded. Subgingival plaque samples were analysed by quantitative real time PCR to identify the presence of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola and Tannerella forsythia. High-sensitivity CRP (mg/l) was measured from fasting blood samples. Multiple linear regression analysis was performed using log-transformed CRP concentration as the dependent variable, with the presence of each periodontal pathogen as predictor variables, with adjustment for various potential confounders. A total of 518 men (mean age 63.6 SD 3.0 years) were included in the analysis. Multiple regression analysis showed that body mass index (p < 0.001), current smoking (p < 0.01), the detectable presence of P. gingivalis (p < 0.01) and hypertension (p = 0.01), were independently associated with an increased CRP. The detectable presence of P. gingivalis was associated with a 20% (95% confidence interval 4-35%) increase in CRP (mg/l) after adjustment for all other predictor variables. In these 60-70-year-old dentate men, the presence of P. gingivalis in subgingival plaque was significantly associated with a raised level of C-reactive protein. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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…

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia.

PubMed

Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G R William; Robinson, Timothy P; Tatem, Andrew J; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P; Xiao, Xiangming; Linard, Catherine; Van Boeckel, Thomas P; Martin, Vincent; Bhatt, Samir; Gething, Peter W; Farrar, Jeremy J; Hay, Simon I; Yu, Hongjie

2014-06-17

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

PubMed Central

Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G. R. William; Robinson, Timothy P.; Tatem, Andrew J.; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P.; Xiao, Xiangming; Linard, Catherine; Van Boeckel, Thomas P.; Martin, Vincent; Bhatt, Samir; Gething, Peter W.; Farrar, Jeremy J.; Hay, Simon I.; Yu, Hongjie

2014-01-01

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease. PMID:24937647

Selective attrition and intraindividual variability in response time moderate cognitive change.

PubMed

Yao, Christie; Stawski, Robert S; Hultsch, David F; MacDonald, Stuart W S

2016-01-01

Selection of a developmental time metric is useful for understanding causal processes that underlie aging-related cognitive change and for the identification of potential moderators of cognitive decline. Building on research suggesting that time to attrition is a metric sensitive to non-normative influences of aging (e.g., subclinical health conditions), we examined reason for attrition and intraindividual variability (IIV) in reaction time as predictors of cognitive performance. Three hundred and four community dwelling older adults (64-92 years) completed annual assessments in a longitudinal study. IIV was calculated from baseline performance on reaction time tasks. Multilevel models were fit to examine patterns and predictors of cognitive change. We show that time to attrition was associated with cognitive decline. Greater IIV was associated with declines on executive functioning and episodic memory measures. Attrition due to personal health reasons was also associated with decreased executive functioning compared to that of individuals who remained in the study. These findings suggest that time to attrition is a useful metric for representing cognitive change, and reason for attrition and IIV are predictive of non-normative influences that may underlie instances of cognitive loss in older adults.

Selective Attrition and Intraindividual Variability in Response Time Moderate Cognitive Change

PubMed Central

Yao, Christie; Stawski, Robert S.; Hultsch, David F.; MacDonald, Stuart W.S.

2016-01-01

Objectives Selection of a developmental time metric is useful for understanding causal processes that underlie aging-related cognitive change, and for the identification of potential moderators of cognitive decline. Building on research suggesting that time to attrition is a metric sensitive to non-normative influences of aging (e.g., subclinical health conditions), we examined reason for attrition and intraindividual variability (IIV) in reaction time as predictors of cognitive performance. Method Three-hundred and four community dwelling older adults (64-92 years) completed annual assessments in a longitudinal study. IIV was calculated from baseline performance on reaction time tasks. Multilevel models were fit to examine patterns and predictors of cognitive change. Results We show that time to attrition was associated with cognitive decline. Greater IIV was associated with declines on executive functioning and episodic memory measures. Attrition due to personal health reasons was also associated with decreased executive functioning compared to individuals who remained in study. Discussion These findings suggest that time to attrition is a useful metric for representing cognitive change, and reason for attrition and IIV are predictive of non-normative influences that may underlie instances of cognitive loss in older adults. PMID:26647008

Implicit Theories, Expectancies, and Values Predict Mathematics Motivation and Behavior across High School and College.

PubMed

Priess-Groben, Heather A; Hyde, Janet Shibley

2017-06-01

Mathematics motivation declines for many adolescents, which limits future educational and career options. The present study sought to identify predictors of this decline by examining whether implicit theories assessed in ninth grade (incremental/entity) predicted course-taking behaviors and utility value in college. The study integrated implicit theory with variables from expectancy-value theory to examine potential moderators and mediators of the association of implicit theories with college mathematics outcomes. Implicit theories and expectancy-value variables were assessed in 165 American high school students (47 % female; 92 % White), who were then followed into their college years, at which time mathematics courses taken, course-taking intentions, and utility value were assessed. Implicit theories predicted course-taking intentions and utility value, but only self-concept of ability predicted courses taken, course-taking intentions, and utility value after controlling for prior mathematics achievement and baseline values. Expectancy for success in mathematics mediated associations between self-concept of ability and college outcomes. This research identifies self-concept of ability as a stronger predictor than implicit theories of mathematics motivation and behavior across several years: math self-concept is critical to sustained engagement in mathematics.

Self-reported health and cortisol awakening response in parents of people with asperger syndrome: the role of trait anger and anxiety, coping and burden.

PubMed

Ruiz-Robledillo, N; Moya-Albiol, L

2013-11-01

Caring for offspring with autism spectrum disorders entails high levels of stress for a long period of time and is associated with several types of health complaints. Few studies have focused on specific effects of particular disorders in the spectrum. This study was carried out with the aim of evaluating the global health of parents of people with Asperger syndrome (N = 53) compared to those of typically developing children (N = 54) through self-reported measures (medication consumption and somatic symptoms) and biological markers (cortisol awakening response [CAR]). Additionally, we analysed various psychological variables as potential predictors of caregiver health. We found that caregivers take more medication and have worse self-reported health than controls, but there were no significant differences in CAR between the groups. However, after controlling for negative affect, differences between groups in CAR reached significance. With regards to predictor variables, anxiety trait, cognitive-coping style, burden and anger temperament were significantly associated with caregiver's self-reported health. These findings underline the need to develop interventions that foster improvements in the health of caregivers, reduce their burden and enhance their quality of life.

Predicting outcome in severe traumatic brain injury using a simple prognostic model.

PubMed

Sobuwa, Simpiwe; Hartzenberg, Henry Benjamin; Geduld, Heike; Uys, Corrie

2014-06-17

Several studies have made it possible to predict outcome in severe traumatic brain injury (TBI) making it beneficial as an aid for clinical decision-making in the emergency setting. However, reliable predictive models are lacking for resource-limited prehospital settings such as those in developing countries like South Africa. To develop a simple predictive model for severe TBI using clinical variables in a South African prehospital setting. All consecutive patients admitted at two level-one centres in Cape Town, South Africa, for severe TBI were included. A binary logistic regression model was used, which included three predictor variables: oxygen saturation (SpO₂), Glasgow Coma Scale (GCS) and pupil reactivity. The Glasgow Outcome Scale was used to assess outcome on hospital discharge. A total of 74.4% of the outcomes were correctly predicted by the logistic regression model. The model demonstrated SpO₂ (p=0.019), GCS (p=0.001) and pupil reactivity (p=0.002) as independently significant predictors of outcome in severe TBI. Odds ratios of a good outcome were 3.148 (SpO₂ ≥ 90%), 5.108 (GCS 6 - 8) and 4.405 (pupils bilaterally reactive). This model is potentially useful for effective predictions of outcome in severe TBI.

Measuring the impact of an interprofessional multimedia learning resource on Japanese nurses and nursing students using the Theory of Planned Behavior Medication Safety Questionnaire.

PubMed

Omura, Mieko; Levett-Jones, Tracy; Stone, Teresa Elizabeth; Maguire, Jane; Lapkin, Samuel

2015-12-01

Interprofessional communication and teamwork are essential for medication safety; however, limited educational opportunities for health professionals and students to develop these skills exist in Japan. This study evaluated the impact of an interprofessional multimedia learning resource on registered nurses' and nursing students' intention to practice in a manner promoting medication safety. Using a quasi-experimental design, Japanese registered nurses and nursing students (n = 203) were allocated to an experimental (n = 109) or control group (n = 94). Behavioral intentions of medication safety and the predictor variables of attitudes, perceived behavioral control, and subjective norms were measured using a Japanese version of the Theory of Planned Behavior Medication Safety Questionnaire. Registered nurses in the experimental group demonstrated a greater intention to collaborate and practice in a manner that enhanced medication safety, evidenced by higher scores than the control group on all predictor variables. The results demonstrate the potential for interprofessional multimedia learning resources to positively impact the behaviors of Japanese registered nurses in relation to safe medication practices. Further research in other contexts and with other cohorts is warranted. © 2015 Wiley Publishing Asia Pty Ltd.

Generalized SAMPLE SIZE Determination Formulas for Investigating Contextual Effects by a Three-Level Random Intercept Model.

PubMed

Usami, Satoshi

2017-03-01

Behavioral and psychological researchers have shown strong interests in investigating contextual effects (i.e., the influences of combinations of individual- and group-level predictors on individual-level outcomes). The present research provides generalized formulas for determining the sample size needed in investigating contextual effects according to the desired level of statistical power as well as width of confidence interval. These formulas are derived within a three-level random intercept model that includes one predictor/contextual variable at each level to simultaneously cover various kinds of contextual effects that researchers can show interest. The relative influences of indices included in the formulas on the standard errors of contextual effects estimates are investigated with the aim of further simplifying sample size determination procedures. In addition, simulation studies are performed to investigate finite sample behavior of calculated statistical power, showing that estimated sample sizes based on derived formulas can be both positively and negatively biased due to complex effects of unreliability of contextual variables, multicollinearity, and violation of assumption regarding the known variances. Thus, it is advisable to compare estimated sample sizes under various specifications of indices and to evaluate its potential bias, as illustrated in the example.

Individualized Risk Model for Venous Thromboembolism After Total Joint Arthroplasty.

PubMed

Parvizi, Javad; Huang, Ronald; Rezapoor, Maryam; Bagheri, Behrad; Maltenfort, Mitchell G

2016-09-01

Venous thromboembolism (VTE) after total joint arthroplasty (TJA) is a potentially fatal complication. Currently, a standard protocol for postoperative VTE prophylaxis is used that makes little distinction between patients at varying risks of VTE. We sought to develop a simple scoring system identifying patients at higher risk for VTE in whom more potent anticoagulation may need to be administered. Utilizing the National Inpatient Sample data, 1,721,806 patients undergoing TJA were identified, among whom 15,775 (0.9%) developed VTE after index arthroplasty. Among the cohort, all known potential risk factors for VTE were assessed. An initial logistic regression model using potential predictors for VTE was performed. Predictors with little contribution or poor predictive power were pruned from the data, and the model was refit. After pruning of variables that had little to no contribution to VTE risk, using the logistic regression, all independent predictors of VTE after TJA were identified in the data. Relative weights for each factor were determined. Hypercoagulability, metastatic cancer, stroke, sepsis, and chronic obstructive pulmonary disease had some of the highest points. Patients with any of these conditions had risk for postoperative VTE that exceeded the 3% rate. Based on the model, an iOS (iPhone operating system) application was developed (VTEstimator) that could be used to assign patients into low or high risk for VTE after TJA. We believe individualization of VTE prophylaxis after TJA can improve the efficacy of preventing VTE while minimizing untoward risks associated with the administration of anticoagulation. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Comparison of correlated correlations.

PubMed

Cohen, A

1989-12-01

We consider a problem where kappa highly correlated variables are available, each being a candidate for predicting a dependent variable. Only one of the kappa variables can be chosen as a predictor and the question is whether there are significant differences in the quality of the predictors. We review several tests derived previously and propose a method based on the bootstrap. The motivating medical problem was to predict 24 hour proteinuria by protein-creatinine ratio measured at either 08:00, 12:00 or 16:00. The tests which we discuss are illustrated by this example and compared using a small Monte Carlo study.

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.

Workplace victimization risk and protective factors for suicidal behavior among active duty military personnel.

PubMed

Hourani, Laurel L; Williams, Jason; Lattimore, Pamela K; Morgan, Jessica K; Hopkinson, Susan G; Jenkins, Linda; Cartwright, Joel

2018-04-22

Workplace victimization is a potential risk factor for suicidal behaviors (SB) among military personnel that has been largely overlooked. This paper examines both the impact of workplace victimization on reported SB and several potential protective factors associated with such suicidal behaviors in a large sample of active duty soldiers. A case-control study was conducted with 71 soldiers who reported SB in the past 12 months, each matched on sociodemographic characteristics to two others without reported suicidal behaviors. A multiple regression model was estimated to assess the effects of risk and protective factors while controlling for other variables. SB was associated with several aspects of victimization, mental health and substance abuse conditions, pain, impulsivity, stressors, negative life events, work-family conflict, active coping behaviors and positive military-related factors. Controlling for other variables, those with SB were more likely to have sought mental health or substance abuse services, to be depressed, anxious, impulsive, and less resilient than non-SB personnel. Study limitations included the use of retrospective self-report data, absence of some known SB predictors, and a population restricted to active duty Army personnel. SB among active duty personnel is associated with victimization since joining the military and is protected by resiliency. These findings suggest that in addition to the usual mental health factors, these additional predictors should be accounted for in SB intervention and prevention planning for active duty personnel. Copyright © 2018 Elsevier B.V. All rights reserved.

Smoking and Female Sex: Independent Predictors of Human Vascular Smooth Muscle Cells Stiffening

PubMed Central

Dinardo, Carla Luana; Santos, Hadassa Campos; Vaquero, André Ramos; Martelini, André Ricardo; Dallan, Luis Alberto Oliveira; Alencar, Adriano Mesquita; Krieger, José Eduardo; Pereira, Alexandre Costa

2015-01-01

Aims Recent evidence shows the rigidity of vascular smooth muscle cells (VSMC) contributes to vascular mechanics. Arterial rigidity is an independent cardiovascular risk factor whose associated modifications in VSMC viscoelasticity have never been investigated. This study’s objective was to evaluate if the arterial rigidity risk factors aging, African ancestry, female sex, smoking and diabetes mellitus are associated with VMSC stiffening in an experimental model using a human derived vascular smooth muscle primary cell line repository. Methods Eighty patients subjected to coronary artery bypass surgery were enrolled. VSMCs were extracted from internal thoracic artery fragments and mechanically evaluated using Optical Magnetic Twisting Cytometry assay. The obtained mechanical variables were correlated with the clinical variables: age, gender, African ancestry, smoking and diabetes mellitus. Results The mechanical variables Gr, G’r and G”r had a normal distribution, demonstrating an inter-individual variability of VSMC viscoelasticity, which has never been reported before. Female sex and smoking were independently associated with VSMC stiffening: Gr (apparent cell stiffness) p = 0.022 and p = 0.018, R2 0.164; G’r (elastic modulus) p = 0.019 and p = 0.009, R2 0.184 and G”r (dissipative modulus) p = 0.011 and p = 0.66, R2 0.141. Conclusion Female sex and smoking are independent predictors of VSMC stiffening. This pro-rigidity effect represents an important element for understanding the vascular rigidity observed in post-menopausal females and smokers, as well as a potential therapeutic target to be explored in the future. There is a significant inter-individual variation of VSMC viscoelasticity, which is slightly modulated by clinical variables and probably relies on molecular factors. PMID:26661469

Climate and soil attributes determine plant species turnover in global drylands.

PubMed

Ulrich, Werner; Soliveres, Santiago; Maestre, Fernando T; Gotelli, Nicholas J; Quero, José L; Delgado-Baquerizo, Manuel; Bowker, Matthew A; Eldridge, David J; Ochoa, Victoria; Gozalo, Beatriz; Valencia, Enrique; Berdugo, Miguel; Escolar, Cristina; García-Gómez, Miguel; Escudero, Adrián; Prina, Aníbal; Alfonso, Graciela; Arredondo, Tulio; Bran, Donaldo; Cabrera, Omar; Cea, Alex; Chaieb, Mohamed; Contreras, Jorge; Derak, Mchich; Espinosa, Carlos I; Florentino, Adriana; Gaitán, Juan; Muro, Victoria García; Ghiloufi, Wahida; Gómez-González, Susana; Gutiérrez, Julio R; Hernández, Rosa M; Huber-Sannwald, Elisabeth; Jankju, Mohammad; Mau, Rebecca L; Hughes, Frederic Mendes; Miriti, Maria; Monerris, Jorge; Muchane, Muchai; Naseri, Kamal; Pucheta, Eduardo; Ramírez-Collantes, David A; Raveh, Eran; Romão, Roberto L; Torres-Díaz, Cristian; Val, James; Veiga, José Pablo; Wang, Deli; Yuan, Xia; Zaady, Eli

2014-12-01

Geographic, climatic, and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. This study aims to: i) characterize patterns of beta diversity in global drylands, ii) detect common environmental drivers of beta diversity, and iii) test for thresholds in environmental conditions driving potential shifts in plant species composition. 224 sites in diverse dryland plant communities from 22 geographical regions in six continents. Beta diversity was quantified with four complementary measures: the percentage of singletons (species occurring at only one site), Whittake's beta diversity (β(W)), a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites (β(R 2 )), and a multivariate abundance-based metric (β(MV)). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographic, climatic, and soil variables. Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity (percentage of singletons and β(W)) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance ((β(R 2 )) and β(MV)) were more associated with climate variability. Interactions among soil variables, climatic factors, and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving ~ 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation.

Multi-linear regression of sea level in the south west Pacific as a first step towards local sea level projections

NASA Astrophysics Data System (ADS)

Kumar, Vandhna; Meyssignac, Benoit; Melet, Angélique; Ganachaud, Alexandre

2017-04-01

Rising sea levels are a critical concern in small island nations. The problem is especially serious in the western south Pacific, where the total sea level rise over the last 60 years is up to 3 times the global average. In this study, we attempt to reconstruct sea levels at selected sites in the region (Suva, Lautoka, Noumea - Fiji and New Caledonia) as a mutiple-linear regression of atmospheric and oceanic variables. We focus on interannual-to-decadal scale variability, and lower (including the global mean sea level rise) over the 1979-2014 period. Sea levels are taken from tide gauge records and the ORAS4 reanalysis dataset, and are expressed as a sum of steric and mass changes as a preliminary step. The key development in our methodology is using leading wind stress curl as a proxy for the thermosteric component. This is based on the knowledge that wind stress curl anomalies can modulate the thermocline depth and resultant sea levels via Rossby wave propagation. The analysis is primarily based on correlation between local sea level and selected predictors, the dominant one being wind stress curl. In the first step, proxy boxes for wind stress curl are determined via regions of highest correlation. The proportion of sea level explained via linear regression is then removed, leaving a residual. This residual is then correlated with other locally acting potential predictors: halosteric sea level, the zonal and meridional wind stress components, and sea surface temperature. The statistically significant predictors are used in a multi-linear regression function to simulate the observed sea level. The method is able to reproduce between 40 to 80% of the variance in observed sea level. Based on the skill of the model, it has high potential in sea level projection and downscaling studies.

Linking the climatic and geochemical controls on global soil carbon cycling

NASA Astrophysics Data System (ADS)

Doetterl, Sebastian; Stevens, Antoine; Six, Johan; Merckx, Roel; Van Oost, Kristof; Casanova Pinto, Manuel; Casanova-Katny, Angélica; Muñoz, Cristina; Boudin, Mathieu; Zagal Venegas, Erick; Boeckx, Pascal

2015-04-01

Climatic and geochemical parameters are regarded as the primary controls for soil organic carbon (SOC) storage and turnover. However, due to the difference in scale between climate and geochemical-related soil research, the interaction of these key factors for SOC dynamics have rarely been assessed. Across a large geochemical and climatic transect in similar biomes in Chile and the Antarctic Peninsula we show how abiotic geochemical soil features describing soil mineralogy and weathering pose a direct control on SOC stocks, concentration and turnover and are central to explaining soil C dynamics at larger scales. Precipitation and temperature had an only indirect control by regulating geochemistry. Soils with high SOC content have low specific potential CO2 respiration rates, but a large fraction of SOC that is stabilized via organo-mineral interactions. The opposite was observed for soils with low SOC content. The observed differences for topsoil SOC stocks along this transect of similar biomes but differing geo-climatic site conditions are of the same magnitude as differences observed for topsoil SOC stocks across all major global biomes. Using precipitation and a set of abiotic geochemical parameters describing soil mineralogy and weathering status led to predictions of high accuracy (R2 0.53-0.94) for different C response variables. Partial correlation analyses revealed that the strength of the correlation between climatic predictors and SOC response variables decreased by 51 - 83% when controlling for geochemical predictors. In contrast, controlling for climatic variables did not result in a strong decrease in the strength of the correlations of between most geochemical variables and SOC response variables. In summary, geochemical parameters describing soil mineralogy and weathering were found to be essential for accurate predictions of SOC stocks and potential CO2 respiration, while climatic factors were of minor importance as a direct control, but are important through governing soil weathering and geochemistry. In conclusion, we pledge for a stronger implementation of geochemical soil properties to predict SOC stocks on a global scale. Understanding the effects of climate (temperature and precipitation) change on SOC dynamics also requires good understanding of the relationship between climate and soil geochemistry.

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.

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.

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.

Testing Components of a Self-Management Theory in Adolescents With Type 1 Diabetes Mellitus.

PubMed

Verchota, Gwen; Sawin, Kathleen J

The role of self-management in adolescents with type 1 diabetes mellitus is not well understood. The purpose of the research was to examine the relationship of key individual and family self-management theory, context, and process variables on proximal (self-management behaviors) and distal (hemoglobin A1c and diabetes-specific health-related quality of life) outcomes in adolescents with type 1 diabetes. A correlational, cross-sectional study was conducted to identify factors contributing to outcomes in adolescents with Type 1 diabetes and examine potential relationships between context, process, and outcome variables delineated in individual and family self-management theory. Participants were 103 adolescent-parent dyads (adolescents ages 12-17) with Type 1 diabetes from a Midwest, outpatient, diabetes clinic. The dyads completed a self-report survey including instruments intended to measure context, process, and outcome variables from individual and family self-management theory. Using hierarchical multiple regression, context (depressive symptoms) and process (communication) variables explained 37% of the variance in self-management behaviors. Regimen complexity-the only significant predictor-explained 11% of the variance in hemoglobin A1c. Neither process variables nor self-management behaviors were significant. For the diabetes-specific health-related quality of life outcome, context (regimen complexity and depressive symptoms) explained 26% of the variance at step 1; an additional 9% of the variance was explained when process (self-efficacy and communication) variables were added at step 2; and 52% of the variance was explained when self-management behaviors were added at Step 3. In the final model, three variables were significant predictors: depressive symptoms, self-efficacy, and self-management behaviors. The individual and family self-management theory can serve as a cogent theory for understanding key concepts, processes, and outcomes essential to self-management in adolescents and families dealing with Type 1 diabetes mellitus.

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.

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.

Variability of North Atlantic Hurricane Frequency in a Large Ensemble of High-Resolution Climate Simulations

NASA Astrophysics Data System (ADS)

Mei, W.; Kamae, Y.; Xie, S. P.

2017-12-01

Forced and internal variability of North Atlantic hurricane frequency during 1951-2010 is studied using a large ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The simulations well capture the interannual-to-decadal variability of hurricane frequency in best track data, and further suggest a possible underestimate of hurricane counts in the current best track data prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the Main Development Region (MDR) accounts for more than 80% of the forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a simple but useful predictor; a one-degree increase in this SST difference produces 7.1±1.4 more hurricanes. The hurricane frequency also exhibits internal variability that is comparable in magnitude to the interannual variability. The 100-member ensemble allows us to address the following important questions: (1) Are the observations equivalent to one realization of such a large ensemble? (2) How many ensemble members are needed to reproduce the variability in observations and in the forced component of the simulations? The sources of the internal variability in hurricane frequency will be identified and discussed. The results provide an explanation for the relatively week correlation ( 0.6) between MDR GPI and hurricane frequency on interannual timescales in observations.

Psychological and Social Work Factors as Predictors of Mental Distress: A Prospective Study

PubMed Central

Finne, Live Bakke; Christensen, Jan Olav; Knardahl, Stein

2014-01-01

Studies exploring psychological and social work factors in relation to mental health problems (anxiety and depression) have mainly focused on a limited set of exposures. The current study investigated prospectively a broad set of specific psychological and social work factors as predictors of potentially clinically relevant mental distress (anxiety and depression), i.e. “caseness” level of distress. Employees were recruited from 48 Norwegian organizations, representing a wide variety of job types. A total of 3644 employees responded at both baseline and at follow-up two years later. Respondents were distributed across 832 departments within the 48 organizations. Nineteen work factors were measured. Two prospective designs were tested: (i) with baseline predictors and (ii) with average exposure over time ([T1+T2]/2) as predictors. Random intercept logistic regressions were conducted to account for clustering of the data. Baseline “cases” were excluded (n = 432). Age, sex, skill level, and mental distress as a continuous variable at T1 were adjusted for. Fourteen of 19 factors showed some prospective association with mental distress. The most consistent risk factor was role conflict (highest odds ratio [OR] 2.08, 99% confidence interval [CI]: 1.45–3.00). The most consistent protective factors were support from immediate superior (lowest OR 0.56, 99% CI: 0.43–0.72), fair leadership (lowest OR 0.52, 99% CI: 0.40–0.68), and positive challenge (lowest OR 0.60, 99% CI: 0.41–0.86). The present study demonstrated that a broad set of psychological and social work factors predicted mental distress of potential clinical relevance. Some of the most consistent predictors were different from those traditionally studied. This highlights the importance of expanding the range of factors beyond commonly studied concepts like the demand-control model and the effort-reward imbalance model. PMID:25048033

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

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.

Examining individual factors according to health risk appraisal data as determinants of absenteeism among US utility employees.

PubMed

Marzec, Mary L; Scibelli, Andrew F; Edington, Dee W

2013-07-01

To investigate predictors of absenteeism and discuss potential implications for policy/program design. Health Risk Appraisal (HRA) data and self-reported and objective absenteeism (personnel records) were used to develop a structural equation model, controlling for age, sex, and job classification. A Medical Condition Burden Index (MCBI) was created by summing the number of self-reported medical conditions. Higher MCBI and stress were direct predictors of absenteeism. Physical activity was not associated with absenteeism but mediated both stress and MCBI. Because stress impacted both absenteeism and MCBI, organizations may benefit by placing stress management as a priority for wellness program and policy focus. Physical activity was not directly associated with absenteeism but was a mediating variable for stress and MCBI. Measures of stress and physical health may be more meaningful as outcome measures for physical activity programs than absenteeism.

Recurrent Childhood Animal Cruelty and Its Link to Recurrent Adult Interpersonal Violence.

PubMed

Trentham, Caleb E; Hensley, Christopher; Policastro, Christina

2018-06-01

In the early 1960s, researchers began to examine the potential link between childhood animal cruelty and future interpersonal violence. Findings since then have been inconsistent in establishing a relationship between the two. This may be due to researchers failing to measure the recurrency of childhood animal abuse and the recurrency of later violent acts committed in adulthood. The current study, using data from 257 inmates at a medium-security prison in a Southern state, is a replication of research conducted by Tallichet and Hensley, and Hensley, Tallichet, and Dutkiewicz, which examined this recurrency issue. The only statistically significant predictor of recurrent adult interpersonal violence in this study was recurrent childhood animal cruelty. Inmates who engaged in recurrent childhood animal cruelty were more likely to commit recurrent adult interpersonal violence. Respondents' race, education, and childhood residence were not significant predictors of the outcome variable.

Predicting long-term citation impact of articles in social and personality psychology.

PubMed

Haslam, Nick; Koval, Peter

2010-06-01

The citation impact of a comprehensive sample of articles published in social and personality psychology journals in 1998 was evaluated. Potential predictors of the 10-yr. citation impact of 1580 articles from 37 journals were investigated, including number of authors, number of references, journal impact factor, author nationality, and article length, using linear regression. The impact factor of the journal in which articles appeared was the primary predictor of the citations that they accrued, accounting for 30% of the total variance. Articles with greater length, more references, and more authors were cited relatively often, although the citation advantage of longer articles was not proportionate to their length. A citation advantage was also enjoyed by authors from the United States of America, Canada, and the United Kingdom. 37% of the variance in the total number of citations was accounted for by the study variables.

Self perceptions as predictors for return to work 2 years after rehabilitation in orthopedic trauma inpatients.

PubMed

Iakova, Maria; Ballabeni, Pierluigi; Erhart, Peter; Seichert, Nikola; Luthi, François; Dériaz, Olivier

2012-12-01

This study aimed to identify self-perception variables which may predict return to work (RTW) in orthopedic trauma patients 2 years after rehabilitation. A prospective cohort investigated 1,207 orthopedic trauma inpatients, hospitalised in rehabilitation, clinics at admission, discharge, and 2 years after discharge. Information on potential predictors was obtained from self administered questionnaires. Multiple logistic regression models were applied. In the final model, a higher likelihood of RTW was predicted by: better general health and lower pain at admission; health and pain improvements during hospitalisation; lower impact of event (IES-R) avoidance behaviour score; higher IES-R hyperarousal score, higher SF-36 mental score and low perceived severity of the injury. RTW is not only predicted by perceived health, pain and severity of the accident at the beginning of a rehabilitation program, but also by the changes in pain and health perceptions observed during hospitalisation.

Parental divorce during early adolescence in Caucasian families: the role of family process variables in predicting the long-term consequences for early adult psychosocial adjustment.

PubMed

Summers, P; Forehand, R; Armistead, L; Tannenbaum, L

1998-04-01

The relationship between parental divorce occurring during adolescence and young adult psychosocial adjustment was examined, as was the role of family process variables in clarifying this relationship. Participants were young Caucasian adults from divorced (n = 119) and married (n = 123) families. Assessments were conducted during adolescence and 6 years later during early adulthood. Young adults from married families reported more secure romantic attachments than those from divorced families; however, differences were not evident in other domains of psychosocial adjustment after demographic variables were controlled. Three family process variables (parent-adolescent relationship, interparental conflict, and maternal depressive symptoms) were examined as potential mediators and moderators of the association between parental divorce and young adult adjustment. No evidence supporting mediation or moderation was found; however, the parent-adolescent and parent-young adult relationships, particularly when the identified parent was the father, emerged as significant predictors of young adult psychosocial adjustment.

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.

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

PubMed

Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

2018-01-01

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes predictors from a MGLMM are always preferable to scatterplots of empirical Bayes predictors generated by separate models, unless the true association between outcomes is zero.

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.

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.

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.

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

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

Prediction of remission of depression with clinical variables, neuropsychological performance, and serotonergic/dopaminergic gene polymorphisms.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Asbun-Bojalil, Juan; Lira-Islas, Yuridia; Reyes-Ponce, Celia; Guàrdia-Olmos, Joan

2012-11-01

The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms on the prediction of depression remission after 12 weeks' treatment with fluoxetine. These variables have been studied as potential predictors of depression remission, but they present poor prognostic sensitivity and specificity by themselves. Seventy-two depressed patients were genotyped according to the aforementioned polymorphisms and were clinically and neuropsychologically assessed before a 12-week fluxetine treatment. Only the La allele of rs25531 polymorphism and the GG and AA forms of the val 108/158 Met polymorphism predict major depressive disorder remission after 12 weeks' treatment with fluoxetine. None of the clinical and neuropsychological variables studied predicted remission. Our results suggest that clinical and neuropsychological variables can initially predict early response to fluoxetine and mask the predictive role of genetic variables; but in remission, where clinical and neuropsychological symptoms associated with depression tend to disappear thanks to the treatment administered, the polymorphisms studied are the only variables in our model capable of predicting remission. However, placebo effects that are difficult to control require cautious interpretation of the results.

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

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).

Training programmes can change behaviour and encourage the cultivation of over-harvested plant species.

PubMed

Williams, Sophie J; Jones, Julia P G; Clubbe, Colin; Gibbons, James M

2012-01-01

Cultivation of wild-harvested plant species has been proposed as a way of reducing over-exploitation of wild populations but lack of technical knowledge is thought to be a barrier preventing people from cultivating a new species. Training programmes are therefore used to increase technical knowledge to encourage people to adopt cultivation. We assessed the impact of a training programme aiming to encourage cultivation of xaté (Chamaedorea ernesti-augusti), an over-harvested palm from Central America. Five years after the training programme ended, we surveyed untrained and trained individuals focusing on four potential predictors of behaviour: technical knowledge, attitudes (what individuals think about a behaviour), subjective norms (what individuals perceive others to think of a behaviour) and perceived behavioural control (self assessment of whether individuals can enact the behaviour successfully). Whilst accounting for socioeconomic variables, we investigate the influence of training upon these behavioural predictors and examine the factors that determine whether people adopt cultivation of a novel species. Those who had been trained had higher levels of technical knowledge about xaté cultivation and higher belief in their ability to cultivate it while training was not associated with differences in attitudes or subjective norms. Technical knowledge and perceived behavioural control (along with socio-economic variables such as forest ownership and age) were predictors of whether individuals cultivate xaté. We suggest that training programmes can have a long lasting effect on individuals and can change behaviour. However, in many situations other barriers to cultivation, such as access to seeds or appropriate markets, will need to be addressed.

Training Programmes Can Change Behaviour and Encourage the Cultivation of Over-Harvested Plant Species

PubMed Central

Williams, Sophie J.; Jones, Julia P. G.; Clubbe, Colin; Gibbons, James M.

2012-01-01

Cultivation of wild-harvested plant species has been proposed as a way of reducing over-exploitation of wild populations but lack of technical knowledge is thought to be a barrier preventing people from cultivating a new species. Training programmes are therefore used to increase technical knowledge to encourage people to adopt cultivation. We assessed the impact of a training programme aiming to encourage cultivation of xaté (Chamaedorea ernesti-augusti), an over-harvested palm from Central America. Five years after the training programme ended, we surveyed untrained and trained individuals focusing on four potential predictors of behaviour: technical knowledge, attitudes (what individuals think about a behaviour), subjective norms (what individuals perceive others to think of a behaviour) and perceived behavioural control (self assessment of whether individuals can enact the behaviour successfully). Whilst accounting for socioeconomic variables, we investigate the influence of training upon these behavioural predictors and examine the factors that determine whether people adopt cultivation of a novel species. Those who had been trained had higher levels of technical knowledge about xaté cultivation and higher belief in their ability to cultivate it while training was not associated with differences in attitudes or subjective norms. Technical knowledge and perceived behavioural control (along with socio-economic variables such as forest ownership and age) were predictors of whether individuals cultivate xaté. We suggest that training programmes can have a long lasting effect on individuals and can change behaviour. However, in many situations other barriers to cultivation, such as access to seeds or appropriate markets, will need to be addressed. PMID:22431993

Predictors of Poor Sleep Quality Among Head and Neck Cancer Patients

PubMed Central

Shuman, Andrew G.; Duffy, Sonia A.; Ronis, David L.; Garetz, Susan L.; McLean, Scott A.; Fowler, Karen E.; Terrell, Jeffrey E.

2013-01-01

Objectives/Hypothesis The objective of this study was to determine the predictors of sleep quality among head and neck cancer patients 1 year after diagnosis. Study Design This was a prospective, multisite cohort study of head and neck cancer patients (N = 457). Methods Patients were surveyed at baseline and 1 year after diagnosis. Chart audits were also conducted. The dependent variable was a self-assessed sleep score 1 year after diagnosis. The independent variables were a 1 year pain score, xerostomia, treatment received (radiation, chemotherapy, and/or surgery), presence of a feeding tube and/or tracheotomy, tumor site and stage, comorbidities, depression, smoking, problem drinking, age, and sex. Results Both baseline (67.1) and 1-year post-diagnosis (69.3) sleep scores were slightly lower than population means (72). Multivariate analyses showed that pain, xerostomia, depression, presence of a tracheotomy tube, comorbidities, and younger age were statistically significant predictors of poor sleep 1 year after diagnosis of head and neck cancer (P < .05). Smoking, problem drinking, and female sex were marginally significant (P < .09). Type of treatment (surgery, radiation and/or chemotherapy), primary tumor site, and cancer stage were not significantly associated with 1-year sleep scores. Conclusions Many factors adversely affecting sleep in head and neck cancer patients are potentially modifiable and appear to contribute to decreased quality of life. Strategies to reduce pain, xerostomia, depression, smoking, and problem drinking may be warranted, not only for their own inherent value, but also for improvement of sleep and the enhancement of quality of life. PMID:20513034

Predictors of Entering a Hearing Aid Evaluation Period: A Prospective Study in Older Hearing-Help Seekers

PubMed Central

Deeg, Dorly J.H.; Versfeld, Niek J.; Heymans, Martijn W.; Naylor, Graham; Kramer, Sophia E.

2017-01-01

This study aimed to determine the predictors of entering a hearing aid evaluation period (HAEP) using a prospective design drawing on the health belief model and the transtheoretical model. In total, 377 older persons who presented with hearing problems to an Ear, Nose, and Throat specialist (n = 110) or a hearing aid dispenser (n = 267) filled in a baseline questionnaire. After 4 months, it was determined via a telephone interview whether or not participants had decided to enter a HAEP. Multivariable logistic regression analyses were applied to determine which baseline variables predicted HAEP status. A priori, candidate predictors were divided into ‘likely’ and ‘novel’ predictors based on the literature. The following variables turned out to be significant predictors: more expected hearing aid benefits, greater social pressure, and greater self-reported hearing disability. In addition, greater hearing loss severity and stigma were predictors in women but not in men. Of note, the predictive effect of self-reported hearing disability was modified by readiness such that with higher readiness, the positive predictive effect became stronger. None of the ‘novel’ predictors added significant predictive value. The results support the notion that predictors of hearing aid uptake are also predictive of entering a HAEP. This study shows that some of these predictors appear to be gender specific or are dependent on a person’s readiness for change. After assuring the external validity of the predictors, an important next step would be to develop prediction rules for use in clinical practice, so that older persons’ hearing help-seeking journey can be facilitated. PMID:29237333

Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning

PubMed Central

Casanova, Ramon; Saldana, Santiago; Simpson, Sean L.; Lacy, Mary E.; Subauste, Angela R.; Blackshear, Chad; Wagenknecht, Lynne; Bertoni, Alain G.

2016-01-01

Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set of observational data, and 2) uncover potential predictors of diabetes. The Jackson Heart Study collected data at baseline and in two follow-up visits from 5,301 African Americans. We excluded those with baseline diabetes and no follow-up, leaving 3,633 individuals for analyses. Over a mean 8-year follow-up, 584 participants developed diabetes. The full RF model evaluated 93 variables including demographic, anthropometric, blood biomarker, medical history, and echocardiogram data. We also used RF metrics of variable importance to rank variables according to their contribution to diabetes prediction. We implemented other models based on logistic regression and RF where features were preselected. The RF full model performance was similar (AUC = 0.82) to those more parsimonious models. The top-ranked variables according to RF included hemoglobin A1C, fasting plasma glucose, waist circumference, adiponectin, c-reactive protein, triglycerides, leptin, left ventricular mass, high-density lipoprotein cholesterol, and aldosterone. This work shows the potential of RF for incident diabetes prediction while dealing with high-dimensional data. PMID:27727289

Developing and validating a predictive model for stroke progression.

PubMed

Craig, L E; Wu, O; Gilmour, H; Barber, M; Langhorne, P

2011-01-01

Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Two patient cohorts were used for this study - the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p < 0.1) on univariate analysis were included in the multivariate model. Logistic regression was the technique employed using backward stepwise regression to drop the least significant variables (p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72-0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50-0.92)]. The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.

Relations that affect the probability and prediction of nitrate concentration in private wells in the glacial aquifer system in the United States

USGS Publications Warehouse

Warner, Kelly L.; Arnold, Terri L.

2010-01-01

Nitrate in private wells in the glacial aquifer system is a concern for an estimated 17 million people using private wells because of the proximity of many private wells to nitrogen sources. Yet, less than 5 percent of private wells sampled in this study contained nitrate in concentrations that exceeded the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 mg/L (milligrams per liter) as N (nitrogen). However, this small group with nitrate concentrations above the USEPA MCL includes some of the highest nitrate concentrations detected in groundwater from private wells (77 mg/L). Median nitrate concentration measured in groundwater from private wells in the glacial aquifer system (0.11 mg/L as N) is lower than that in water from other unconsolidated aquifers and is not strongly related to surface sources of nitrate. Background concentration of nitrate is less than 1 mg/L as N. Although overall nitrate concentration in private wells was low relative to the MCL, concentrations were highly variable over short distances and at various depths below land surface. Groundwater from wells in the glacial aquifer system at all depths was a mixture of old and young water. Oxidation and reduction potential changes with depth and groundwater age were important influences on nitrate concentrations in private wells. A series of 10 logistic regression models was developed to estimate the probability of nitrate concentration above various thresholds. The threshold concentration (1 to 10 mg/L) affected the number of variables in the model. Fewer explanatory variables are needed to predict nitrate at higher threshold concentrations. The variables that were identified as significant predictors for nitrate concentration above 4 mg/L as N included well characteristics such as open-interval diameter, open-interval length, and depth to top of open interval. Environmental variables in the models were mean percent silt in soil, soil type, and mean depth to saturated soil. The 10-year mean (1992-2001) application rate of nitrogen fertilizer applied to farms was included as the potential source variable. A linear regression model also was developed to predict mean nitrate concentrations in well networks. The model is based on network averages because nitrate concentrations are highly variable over short distances. Using values for each of the predictor variables averaged by network (network mean value) from the logistic regression models, the linear regression model developed in this study predicted the mean nitrate concentration in well networks with a 95 percent confidence in predictions.

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.

[Predicting the outcome in severe injuries: an analysis of 2069 patients from the trauma register of the German Society of Traumatology (DGU)].

PubMed

Rixen, D; Raum, M; Bouillon, B; Schlosser, L E; Neugebauer, E

2001-03-01

On hospital admission numerous variables are documented from multiple trauma patients. The value of these variables to predict outcome are discussed controversially. The aim was the ability to initially determine the probability of death of multiple trauma patients. Thus, a multivariate probability model was developed based on data obtained from the trauma registry of the Deutsche Gesellschaft für Unfallchirurgie (DGU). On hospital admission the DGU trauma registry collects more than 30 variables prospectively. In the first step of analysis those variables were selected, that were assumed to be clinical predictors for outcome from literature. In a second step a univariate analysis of these variables was performed. For all primary variables with univariate significance in outcome prediction a multivariate logistic regression was performed in the third step and a multivariate prognostic model was developed. 2069 patients from 20 hospitals were prospectively included in the trauma registry from 01.01.1993-31.12.1997 (age 39 +/- 19 years; 70.0% males; ISS 22 +/- 13; 18.6% lethality). From more than 30 initially documented variables, the age, the GCS, the ISS, the base excess (BE) and the prothrombin time were the most important prognostic factors to predict the probability of death (P(death)). The following prognostic model was developed: P(death) = 1/1 + e(-[k + beta 1(age) + beta 2(GCS) + beta 3(ISS) + beta 4(BE) + beta 5(prothrombin time)]) where: k = -0.1551, beta 1 = 0.0438 with p < 0.0001, beta 2 = -0.2067 with p < 0.0001, beta 3 = 0.0252 with p = 0.0071, beta 4 = -0.0840 with p < 0.0001 and beta 5 = -0.0359 with p < 0.0001. Each of the five variables contributed significantly to the multifactorial model. These data show that the age, GCS, ISS, base excess and prothrombin time are potentially important predictors to initially identify multiple trauma patients with a high risk of lethality. With the base excess and prothrombin time value, as only variables of this multifactorial model that can be therapeutically influenced, it might be possible to better guide early and aggressive therapy.

Advance in prediction of soil slope instabilities

NASA Astrophysics Data System (ADS)

Sigarán-Loría, C.; Hack, R.; Nieuwenhuis, J. D.

2012-04-01

Six generic soils (clays and sands) were systematically modeled with plane-strain finite elements (FE) at varying heights and inclinations. A dataset was generated in order to develop predictive relations of soil slope instabilities, in terms of co-seismic displacements (u), under strong motions with a linear multiple regression. For simplicity, the seismic loads are monochromatic artificial sinusoidal functions at four frequencies: 1, 2, 4, and 6 Hz, and the slope failure criterion used corresponds to near 10% Cartesian shear strains along a continuous region comparable to a slip surface. The generated dataset comprises variables from the slope geometry and site conditions: height, H, inclination, i, shear wave velocity from the upper 30 m, vs30, site period, Ts; as well as the input strong motion: yield acceleration, ay (equal to peak ground acceleration, PGA in this research), frequency, f; and in some cases moment magnitude, M, and Arias intensity, Ia, assumed from empirical correlations. Different datasets or scenarios were created: "Magnitude-independent", "Magnitude-dependent", and "Soil-dependent", and the data was statistically explored and analyzed with varying mathematical forms. Qualitative relations show that the permanent deformations are highly related to the soil class for the clay slopes, but not for the sand slopes. Furthermore, the slope height does not constrain the variability in the co-seismic displacements. The input frequency decreases the variability of the co-seismic displacements for the "Magnitude-dependent" and "Soil-dependent" datasets. The empirical models were developed with two and three predictors. For the sands it was not possible because they could not satisfy the constrains from the statistical method. For the clays, the best models with the smallest errors coincided with the simple general form of multiple regression with three predictors (e.g. near 0.16 and 0.21 standard error, S.E. and 0.75 and 0.55 R2 for the "M-independent" and "M-dependent" datasets correspondingly). From the models with two predictors, a 2nd-order polynom gave the best performance but with a not-significant parameter. The best models with both predictors significant have slightly larger error and smaller R2, e.g. 0.15 S.E., 44% R2 with ay and i. The predictive models obtained with the three scenarios from the clay slopes provide well-constrained predictions but low R2, suggesting the predictors are "not complete", most likely in relation to the simplicity used in the strong motion characterization. Nevertheless, the findings from this work demonstrate the potential from analytical methods in developing more precise predictions as well as the importance on treating different different ground types.

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets.

PubMed

Shuryak, Igor

2017-01-01

The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected "signal"; (5) using several machine learning methods to test the "signal's" sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation.

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets

PubMed Central

Shuryak, Igor

2017-01-01

The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected “signal”; (5) using several machine learning methods to test the “signal’s” sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation. PMID:28068401

Investigations on indoor Radon in Austria, part 2: Geological classes as categorical external drift for spatial modelling of the Radon potential.

PubMed

Bossew, Peter; Dubois, Grégoire; Tollefsen, Tore

2008-01-01

Geological classes are used to model the deterministic (drift or trend) component of the Radon potential (Friedmann's RP) in Austria. It is shown that the RP can be grouped according to geological classes, but also according to individual geological units belonging to the same class. Geological classes can thus serve as predictors for mean RP within the classes. Variability of the RP within classes or units is interpreted as the stochastic part of the regionalized variable RP; however, there does not seem to exist a smallest unit which would naturally divide the RP into a deterministic and a stochastic part. Rather, this depends on the scale of the geological maps used, down to which size of geological units is used for modelling the trend. In practice, there must be a sufficient number of data points (measurements) distributed as uniformly as possible within one unit to allow reasonable determination of the trend component.

The effect of genotype on methotrexate polyglutamate variability in juvenile idiopathic arthritis and association with drug response.

PubMed

Becker, Mara L; Gaedigk, Roger; van Haandel, Leon; Thomas, Bradley; Lasky, Andrew; Hoeltzel, Mark; Dai, Hongying; Stobaugh, John; Leeder, J Steven

2011-01-01

The response to and toxicity of methotrexate (MTX) are unpredictable in patients with juvenile idiopathic arthritis (JIA). Intracellular polyglutamation of MTX, assessed by measuring concentrations of MTX polyglutamates (MTXGlu), has been demonstrated to be a promising predictor of drug response. Therefore, this study was aimed at investigating the genetic predictors of MTXGlu variability and associations between MTXGlu and drug response in JIA. The study was designed as a single-center cross-sectional analysis of patients with JIA who were receiving stable doses of MTX at a tertiary care children's hospital. After informed consent was obtained from the 104 patients with JIA, blood was withdrawn during routine MTX-screening laboratory testing. Clinical data were collected by chart review. Genotyping for 34 single-nucleotide polymorphisms (SNPs) in 18 genes within the MTX metabolic pathway was performed. An ion-pair chromatographic procedure with mass spectrometric detection was used to measure MTXGlu1-7. Analysis and genotyping of MTXGlu was completed in the 104 patients. K-means clustering resulted in 3 distinct patterns of MTX polyglutamation. Cluster 1 had low red blood cell (RBC) MTXGlu concentrations, cluster 2 had moderately high RBC MTXGlu1+2 concentrations, and cluster 3 had high concentrations of MTXGlu, specifically MTXGlu3-5. SNPs in the purine and pyrimidine synthesis pathways, as well as the adenosine pathway, were significantly associated with cluster subtype. The cluster with high concentrations of MTXGlu3-5 was associated with elevated liver enzyme levels on liver function tests (LFTs), and there were higher concentrations of MTXGlu3-5 in children who reported gastrointestinal side effects and had abnormal findings on LFTs. No association was noted between MTXGlu and active arthritis. MTXGlu remains a potentially useful tool for determining outcomes in patients with JIA being treated with MTX. The genetic predictors of MTXGlu variability may also contribute to a better understanding of the intracellular biotransformation of MTX in these patients. Copyright © 2011 by the American College of Rheumatology.

Predictors of contemporary coronary artery bypass grafting outcomes.

PubMed

Weisel, Richard D; Nussmeier, Nancy; Newman, Mark F; Pearl, Ronald G; Wechsler, Andrew S; Ambrosio, Giuseppe; Pitt, Bertram; Clare, Robert M; Pieper, Karen S; Mongero, Linda; Reece, Tammy L; Yau, Terrence M; Fremes, Stephen; Menasché, Philippe; Lira, Armando; Harrington, Robert A; Ferguson, T Bruce

2014-12-01

The study objective was to identify the predictors of outcomes in a contemporary cohort of patients from the Reduction in cardiovascular Events by acaDesine in patients undergoing CABG (RED-CABG) trial. Despite the increasing risk profile of patients who undergo coronary artery bypass grafting, morbidity and mortality have remained low, and identification of the current predictors of adverse outcomes may permit new treatments to further improve outcomes. The RED-CABG trial was a multicenter, randomized, double-blind, placebo-controlled study that determined that acadesine did not reduce adverse events in moderately high-risk patients undergoing nonemergency coronary artery bypass grafting. The primary efficacy end point was a composite of all-cause death, nonfatal stroke, or the need for mechanical support for severe left ventricular dysfunction through postoperative day 28. Logistic regression modeling with stepwise variable selection identified which prespecified baseline characteristics were associated with the primary outcome. A second logistic model included intraoperative variables as potential covariates. The 4 independent preoperative risk factors predictive of the composite end point were (1) a history of heart failure (odds ratio, 2.9); (2) increasing age (odds ratio, 1.033 per decade); (3) a history of peripheral vascular disease (odds ratio, 1.6); and (4) receiving aspirin before coronary artery bypass grafting (odds ratio, 0.5), which was protective. The duration of the cardiopulmonary bypass (odds ratio, 1.8) was the only intraoperative variable that contributed to adverse outcomes. Patients who had heart failure and preserved systolic function had a similar high risk of adverse outcomes as those with low ejection fractions, and new approaches may mitigate this risk. Recognition of patients with excessive atherosclerotic burden may permit perioperative interventions to improve their outcomes. The contemporary risks of coronary artery bypass grafting have changed, and their identification may permit new methods to improve outcomes. Copyright © 2014 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

The Influence of Organized Physical Activity (including Gymnastics) on Young Adult Skeletal Traits: Is Maturity Phase Important?

PubMed Central

Bernardoni, Brittney; Scerpella, Tamara A.; Rosenbaum, Paula F.; Kanaley, Jill A.; Raab, Lindsay N.; Li, Quefeng; Wang, Sijian; Dowthwaite, Jodi N.

2015-01-01

We prospectively evaluated adolescent organized physical activity (PA) as a factor in adult female bone traits. Annual DXA scans accompanied semi-annual records of anthropometry, maturity and PA for 42 participants in this preliminary analysis (criteria: appropriately timed DXA scans at ~1 year pre-menarche [predictor] and ~5 years post-menarche [dependent variable]). Regression analysis evaluated total adolescent inter-scan PA and PA over 3 maturity sub-phases as predictors of young adult bone outcomes: 1) bone mineral content (BMC), geometry and strength indices at non-dominant distal radius and femoral neck; 2) sub-head BMC; 3) lumbar spine BMC. Analyses accounted for baseline gynecological age (years pre- or post-menarche), baseline bone status, adult body size and inter-scan body size change. Gymnastics training was evaluated as a potentially independent predictor, but did not improve models for any outcomes (p<0.07). Pre-menarcheal bone traits were strong predictors of most adult outcomes (semi-partial r2 = 0.21-0.59, p≤0.001). Adult 1/3 radius and sub-head BMC were predicted by both total PA and PA 1-3 years post-menarche (p<0.03). PA 3-5 years post-menarche predicted femoral narrow neck width, endosteal diameter and buckling ratio (p<0.05). Thus, participation in organized physical activity programs throughout middle and high school may reduce lifetime fracture risk in females. PMID:25386845

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

PubMed Central

Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-01-01

Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan–Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Results: Kaplan–Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. PMID:27319577

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

PubMed

Molina, David; Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-07-04

The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Heterogeneity measures computed on the post-contrast pre-operative T 1 weighted MR images of patients with GBM are predictors of survival. Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.

Regretting Ever Starting to Smoke: Results from a 2014 National Survey.

PubMed

Nayak, Pratibha; Pechacek, Terry F; Slovic, Paul; Eriksen, Michael P

2017-04-06

Background : The majority of smokers regret ever starting to smoke, yet the vast majority continue to smoke despite the fact that smoking kills nearly 50% of lifetime users. This study examined the relationships between regret and smoker characteristics, quit history, risk perceptions, experiential thinking, and beliefs and intentions at time of smoking initiation. Methods : Data from the 2014 Tobacco Products and Risk Perceptions Survey, a nationally representative survey of United States adults, were analyzed to provide the latest prevalence estimates of regret and potential predictors. Relationships among predictor variables and regret were analyzed using correlations, t -tests, and multinomial logistic regression. Results : The majority of smokers (71.5%) regretted starting to smoke. Being older and non-Hispanic white were significant predictors of regret. Smokers having a high intention to quit, having made quit attempts in the past year, worrying about getting lung cancer, believing smoking every day can be risky for your health, perceiving a risk of being diagnosed with lung cancer during one's lifetime, and considering themselves addicted to cigarettes were significant predictors of regret for smoking initiation. Conclusions : This study provides updated prevalence data on regret using a national sample, and confirms that regret is associated with perceived risk. The findings from this study can be used to inform smoking intervention programs and support the inclusion of smoker regret in cost-benefit analyses of the economic impact of tobacco regulations.

Understanding attrition from international Internet health interventions: a step towards global eHealth.

PubMed

Geraghty, Adam W A; Torres, Leandro D; Leykin, Yan; Pérez-Stable, Eliseo J; Muñoz, Ricardo F

2013-09-01

Worldwide automated Internet health interventions have the potential to greatly reduce health disparities. High attrition from automated Internet interventions is ubiquitous, and presents a challenge in the evaluation of their effectiveness. Our objective was to evaluate variables hypothesized to be related to attrition, by modeling predictors of attrition in a secondary data analysis of two cohorts of an international, dual language (English and Spanish) Internet smoking cessation intervention. The two cohorts were identical except for the approach to follow-up (FU): one cohort employed only fully automated FU (n = 16 430), while the other cohort also used 'live' contact conditional upon initial non-response (n = 1000). Attrition rates were 48.1 and 10.8% for the automated FU and live FU cohorts, respectively. Significant attrition predictors in the automated FU cohort included higher levels of nicotine dependency, lower education, lower quitting confidence and receiving more contact emails. Participants' younger age was the sole predictor of attrition in the live FU cohort. While research on large-scale deployment of Internet interventions is at an early stage, this study demonstrates that differences in attrition from trials on this scale are (i) systematic and predictable and (ii) can largely be eliminated by live FU efforts. In fully automated trials, targeting the predictors we identify may reduce attrition, a necessary precursor to effective behavioral Internet interventions that can be accessed globally.

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.

Drug Concentration Thresholds Predictive of Therapy Failure and Death in Children With Tuberculosis: Bread Crumb Trails in Random Forests.

PubMed

Swaminathan, Soumya; Pasipanodya, Jotam G; Ramachandran, Geetha; Hemanth Kumar, A K; Srivastava, Shashikant; Deshpande, Devyani; Nuermberger, Eric; Gumbo, Tawanda

2016-11-01

 The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown.  Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables.  Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28-5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08-4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99-11.82).  We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America.

More active pre-school children have better motor competence at school starting age: an observational cohort study.

PubMed

Barnett, Lisa M; Salmon, Jo; Hesketh, Kylie D

2016-10-10

Almost half of young children do not achieve minimum recommendations of 60 daily minutes in physical activity. Physical activity is potentially an important determinant of the development of motor competence in children. This study is one of very few longitudinal studies in this area and the first to investigate early childhood physical activity as a predictor of subsequent motor skill competence. Children were assessed as part of the Melbourne InFANT Program longitudinal cohort study at 19 months, 3.5 years and 5 years. Moderate-to-vigorous physical activity (MVPA) (accelerometry) was assessed at each time point. At age 5, children were also assessed in actual (Test of Gross Motor Development-2) and perceived motor competence (Pictorial Scale of Perceived Movement Skill Competence). General linear models were performed with all 12 skills (six object control and six locomotor skills), both actual and perceived, at age 5 as the respective outcome variables. Predictor variables alternated between MVPA at 19 months, 3.5 years and 5 years. Based on standardized TGMD-2 scores most children were average or below in their skill level at age 5. MVPA at 19 months was not a predictor of actual or perceived skill at age 5. MVPA at 3.5 years was associated with actual locomotor skill (B = 0.073, p = 0.033) and perceived total skill at 5 years of age (B = 0.059, p = 0.044). MVPA was not a predictor of actual or perceived object control skill at any age. Parents and preschool staff should be informed that more time in MVPA as a preschool child contributes to locomotor skill and to perceptions of skill ability in a child of school starting age. Understanding this relationship will assist in intervention development.

The "doses" of initial, untreated hallucinations and delusions: a proof-of-concept study of enhanced predictors of first-episode symptomatology and functioning relative to duration of untreated psychosis.

PubMed

Compton, Michael T; Gordon, Tynessa L; Weiss, Paul S; Walker, Elaine F

2011-11-01

A prominent limitation of literature on duration of untreated psychosis (DUP) is that researchers have studied only unidimensional duration as an early-course predictor, neglecting potential effects of frequency/severity of initial, untreated psychosis. This study demonstrates utility of the concept of "doses" of initial, untreated hallucinations and delusions-representing more complete measures of "exposure"-as enhanced predictors of symptomatology/functioning relative to DUP alone. 109 first-episode patients with a psychotic disorder based on Structured Clinical Interview for DSM-IV Axis I Disorders criteria were assessed at 3 public-sector psychiatric units serving an urban, socially disadvantaged, predominantly African American community between July 2004 and June 2008. Dependent variables included negative symptoms, general psychopathology, insight, and global functioning at initial hospitalization. When added to a baseline model (age, gender, and premorbid academic and social functioning), DUP predicted current negative symptoms (P = .02, model R(2) = 0.20), though dose of hallucinations and dose of delusions did not. However, regarding general psychopathology symptoms, DUP was not predictive, though dose of delusions was, when controlling for the other 5 variables (P = .02, model R(2) = 0.15). DUP was not a significant predictor of insight, though dose of hallucinations was, such that a greater dose of initial, untreated hallucinations was associated with better insight at initial hospitalization (P < .01, model R(2) = 0.20). DUP was associated with global functioning (P = .05), and dose of delusions added significantly to this prediction (P = .04; model R(2) = 0.13). Doses of initial, untreated hallucinations and delusions add substantively, though differentially, to the prediction of early-course symptomatology and functioning. Findings suggest a need for focused research on frequency/severity of pretreatment psychotic symptoms beyond duration measures. © Copyright 2011 Physicians Postgraduate Press, Inc.

Improving organ donation rates by modifying the family approach process.

PubMed

Ebadat, Aileen; Brown, Carlos V R; Ali, Sadia; Guitierrez, Tim; Elliot, Eric; Dworaczyk, Sarah; Kadric, Carie; Coopwood, Ben

2014-06-01

The purpose of this study was to identify steps during family approach for organ donation that may be modified to improve consent rates of potential organ donors. Retrospective study of our local organ procurement organization (OPO) database of potential organ donors. Modifiable variables involved in the family approach of potential organ donors were collected and included race and sex of OPO representative, individual initiating approach discussion with family (RN or MD vs. OPO), length of donation discussion, use of a translator, and time of day of approach. Of 1137 potential organ donors, 661 (58%) consented and 476 (42%) declined. Consent rates were higher with matched race of donor and OPO representative (66% vs. 52%, p < 0.001), family approach by female OPO representative (67% vs. 56%, p = 0.002), if approach was initiated by OPO representative (69% vs. 49%, p < 0.001), and if consent rate was dependent on time of day the approach occurred: 6:00 am to noon (56%), noon to 6:00 pm (67%), 6:00 pm to midnight (68%), and midnight to 6:00 am (45%), p = 0.04. Family approach that led to consent lasted longer than those declining (67 vs. 43 minutes, p < 0.001). Independent predictors of consent to donation included female OPO representative (odds ratio [OR], 1.7; p = 0.006), approach discussion initiated by OPO representative (OR, 1.9; p = 0.001), and longer approach discussions (OR, 1.02; p < 0.001). The independent predictor of declined donation was the use of a translator (OR, 0.39; p = 0.01). Variables such as race and sex of OPO representative and time of day should be considered before approaching a family for organ donation. Avoiding translators during the approach process may improve donation rates. Education for health care providers should reinforce the importance of allowing OPO representatives to initiate the family approach for organ donation. Epidemiologic study, level IV. Therapeutic study, level IV.

Predictors of victim disclosure in child sexual abuse: Additional evidence from a sample of incarcerated adult sex offenders.

PubMed

Leclerc, Benoit; Wortley, Richard

2015-05-01

The under-reporting of child sexual abuse by victims is a serious problem that may prolong the suffering of victims and leave perpetrators free to continue offending. Yet empirical evidence indicates that victim disclosure rates are low. In this study, we perform regression analysis with a sample of 369 adult child sexual offenders to examine potential predictors of victim disclosure. Specifically, we extend the range of previously examined potential predictors of victim disclosure and investigate interaction effects in order to better capture under which circumstances victim disclosure is more likely. The current study differs from previous studies in that it examines the impact of victim and offense variables on victim disclosure from the perspective of the offender. In line with previous studies, we found that disclosure increased with the age of the victim and if penetration had occurred. In addition, we found that disclosure increased when the victim came from a non-dysfunctional family and resisted the abuse. The presence of an interaction effect highlighted the impact of the situation on victim disclosure. This effect indicated that as victims get older, they are more likely to disclose the abuse when they are not living with the offender at the time of abuse, but less likely to do so when they are living with the offender at the time of abuse. These findings are discussed in relation to previous studies and the need to facilitate victim disclosure. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving observational study estimates of treatment effects using joint modeling of selection effects and outcomes: the case of AAA repair.

PubMed

O'Malley, A James; Cotterill, Philip; Schermerhorn, Marc L; Landon, Bruce E

2011-12-01

When 2 treatment approaches are available, there are likely to be unmeasured confounders that influence choice of procedure, which complicates estimation of the causal effect of treatment on outcomes using observational data. To estimate the effect of endovascular (endo) versus open surgical (open) repair, including possible modification by institutional volume, on survival after treatment for abdominal aortic aneurysm, accounting for observed and unobserved confounding variables. Observational study of data from the Medicare program using a joint model of treatment selection and survival given treatment to estimate the effects of type of surgery and institutional volume on survival. We studied 61,414 eligible repairs of intact abdominal aortic aneurysms during 2001 to 2004. The outcome, perioperative death, is defined as in-hospital death or death within 30 days of operation. The key predictors are use of endo, transformed endo and open volume, and endo-volume interactions. There is strong evidence of nonrandom selection of treatment with potential confounding variables including institutional volume and procedure date, variables not typically adjusted for in clinical trials. The best fitting model included heterogeneous transformations of endo volume for endo cases and open volume for open cases as predictors. Consistent with our hypothesis, accounting for unmeasured selection reduced the mortality benefit of endo. The effect of endo versus open surgery varies nonlinearly with endo and open volume. Accounting for institutional experience and unmeasured selection enables better decision-making by physicians making treatment referrals, investigators evaluating treatments, and policy makers.

Feature Screening in Ultrahigh Dimensional Cox's Model.

PubMed

Yang, Guangren; Yu, Ye; Li, Runze; Buu, Anne

Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other fields. In this work, we propose a feature screening procedure for the Cox model with ultrahigh dimensional covariates. The proposed procedure is distinguished from the existing sure independence screening (SIS) procedures (Fan, Feng and Wu, 2010, Zhao and Li, 2012) in that the proposed procedure is based on joint likelihood of potential active predictors, and therefore is not a marginal screening procedure. The proposed procedure can effectively identify active predictors that are jointly dependent but marginally independent of the response without performing an iterative procedure. We develop a computationally effective algorithm to carry out the proposed procedure and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. That is, with the probability tending to one, the selected variable set includes the actual active predictors. We conduct Monte Carlo simulation to evaluate the finite sample performance of the proposed procedure and further compare the proposed procedure and existing SIS procedures. The proposed methodology is also demonstrated through an empirical analysis of a real data example.

Triggers of Eating in Everyday Life

PubMed Central

Tomiyama, A. Janet; Mann, Traci; Comer, Lisa

2009-01-01

Understanding the triggers of eating in everyday life is crucial for the creation of interventions to promote healthy eating and to prevent overeating. Here, the proximal predictors of eating are explored in a natural setting. Research from laboratory settings suggests that restrained eaters overeat after experiencing anxiety, distraction, and the presence of positive or negative moods, but not hunger; whereas the only factor that triggers eating in unrestrained eaters is hunger. In this study, 137 female participants reported hourly for two days on these potential predictors and their eating using electronic diaries, allowing us to establish the relationships between these factors while participants went about their normal daily activities. The main outcome variables were the number of servings eaten and whether or not food was eaten. Contrary to findings from laboratory settings, in everyday life restrained eaters (1) did not overeat in response to anxiety; (2) ate less in the presence of positive or negative moods; and (3) ate more in response to hunger. The relationships between these factors and eating among unrestrained eaters were closer to those found in laboratory settings. In conclusion, predictors of eating must be studied in everyday life to develop successful interventions. PMID:18773931

Predictors of hepatitis B vaccination status in healthcare workers in Belgrade, Serbia, December 2015

PubMed Central

Kisic-Tepavcevic, Darija; Kanazir, Milena; Gazibara, Tatjana; Maric, Gorica; Makismovic, Natasa; Loncarevic, Goranka; Pekmezovic, Tatjana

2017-01-01

Despite the availability of a safe and effective vaccine since 1982, overall coverage of hepatitis B vaccination among healthcare workers (HCWs) has not reached a satisfactory level in many countries worldwide. The aim of this study was to estimate the prevalence of hepatitis B vaccination, and to assess the predictors of hepatitis B vaccination status among HCWs in Serbia. Of 380 randomly selected HCWs, 352 (92.6%) were included in the study. The prevalence of hepatitis B vaccination acceptance was 66.2%. The exploratory factor analyses using the vaccination-refusal scale showed that items clustered under ‘threat of disease’ explained the highest proportion (30.4%) of variance among those declining vaccination. The factor analyses model of the potential reasons for receiving the hepatitis B vaccine showed that ‘social influence’ had the highest contribution (47.5%) in explaining variance among those vaccinated. In the multivariate adjusted model the following variables were independent predictors of hepatitis B vaccination status: occupation, duration of work experience, exposure to blood in the previous year, and total hepatitis B-related knowledge score. Our results highlight the need for well-planned national policies, possibly including mandatory hepatitis B immunisation, in the Serbian healthcare environment. PMID:28449736