Toward the Multivariate Modeling of Achievement, Aptitude, and Personality.

ERIC Educational Resources Information Center

Foshay, Wellesley R.; Misanchuk, Earl R.

1981-01-01

A multivariate investigation of the dynamics of cumulative achievement studied the influence of course grades, personality traits, environmental variables, and previous performance. The latter was the best single predictor of performance. (CJ)

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.

IAD value at harvest as a predictor for ‘Anjou’ fruit storage performance

USDA-ARS?s Scientific Manuscript database

Anjou pears produced in the PNW are single picked and can have considerable fruit maturity variability at harvest. Assessment of chlorophyll in and several mm below the peel using differential absorbance (IAD) is a means to identify populations of fruit of varying maturity within single trees. Thi...

The effects of maternal psychosocial factors on parenting attitudes of low-income, single mothers with young children.

PubMed

Lutenbacher, M; Hall, L A

1998-01-01

Although recent evidence implies linkages among depression or depressive symptoms, self-esteem, history of childhood abuse, and parenting attitudes, the evidence does not clearly elucidate the relationships among these variables. To investigate the relationships among maternal psychosocial factors (history of childhood abuse, everyday stressors, self-esteem, and depressive symptoms) and parenting attitudes of low-income, single mothers who have young children. Secondary analyses of data from in-home interviews with 206 low-income, single mothers from a southeastern United States urban area were conducted. A variety of scales, including the Adult-Adolescent Parenting Inventory (AAPI), were used to measure maternal psychosocial factors. Using the AAPI, a Modified Parenting Attitudes Measure (MPAM), and subscales, a three-stage regression procedure was used to test the model. For stages 1 and 2, everyday stressors were the strongest predictor of self-esteem. Childhood sexual abuse, everyday stressors, low self-esteem, and control variables accounted for 58% of variance in depressive symptoms. In the third stage for the AAPI, only control variables were retained except in the Lack of Empathy subscale, where depressive symptoms and control variables accounted for 16% of the variance. The third stage for the MPAM yielded, by subscale: Only control variables predicted Corporal Punishment Beliefs; depressive symptoms were the strongest predictor for the total MPAM (19% of variance) and of the Inappropriate Emotional Expectations subscale (17%); and childhood physical abuse was the only predictor of Role Reversal. Depressive symptoms mediated the effects of childhood abuse, everyday stressors, and self-esteem and provided the linkage between these variables and at-risk parenting attitudes. Self-esteem decreased as everyday stressors increased but did not directly affect parenting attitudes. A relationship was not found between childhood abuse and low self-esteem. This study highlights the complexity of parenting and the need to identify other factors of at-risk parenting not accounted for in this study.

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

Medication adherence among patients in a chronic disease clinic.

PubMed

Tourkmani, Ayla M; Al Khashan, Hisham I; Albabtain, Monirah A; Al Harbi, Turki J; Al Qahatani, Hala B; Bakhiet, Ahmed H

2012-12-01

To assess motivation and knowledge domains of medication adherence intention, and to determine their predictors in an ambulatory setting. We conducted a cross-sectional survey study among patients attending a chronic disease clinic at the Family and Community Medicine Department, Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia between June and September 2010. Adherence intention was assessed using Modified Morisky Scale. Predictors of low motivation and/or knowledge were determined using logistic regression models. A total of 347 patients were interviewed during the study duration. Most patients (75.5%) had 2 or more chronic diseases with an average of 6.3 +/- 2.3 medications, and 6.5 +/- 2.9 pills per prescription. The frequency of adherence intention was low (4.6%), variable (37.2%), and high (58.2%). In multivariate logistic regression analysis, younger age and having asthma were significantly associated with low motivation, while male gender, single status, and not having hypertension were significantly associated with low knowledge. Single status was the only independent predictor of low adherence intention. In a population with multiple chronic diseases and high illiteracy rate, more than 40% had low/variable intention to adhere to prescribed medications. Identifying predictors of this group may help in providing group-specific interventional programs.

Predictors of Sensitivity to Perceptual Learning in Children With Infantile Nystagmus.

PubMed

Huurneman, Bianca; Boonstra, F Nienke; Goossens, Jeroen

2017-08-01

To identify predictors of sensitivity to perceptual learning on a computerized, near-threshold letter discrimination task in children with infantile nystagmus (idiopathic IN: n = 18; oculocutaneous albinism accompanied by IN: n = 18). Children were divided into two age-, acuity-, and diagnosis-matched training groups: a crowded (n = 18) and an uncrowded training group (n = 18). Training consisted of 10 sessions spread out over 5 weeks (grand total of 3500 trials). Baseline performance, age, diagnosis, training condition, and perceived pleasantness of training (training joy) were entered as linear regression predictors of training-induced changes on a single- and a crowded-letter task. An impressive 57% of the variability in improvements of single-letter visual acuity was explained by age, training condition, and training joy. Being older and training with uncrowded letters were associated with larger single-letter visual acuity improvements. More training joy was associated with a larger gain from the uncrowded training and a smaller gain from the crowded training. Fifty-six percent of the variability in crowded-letter task improvements was explained by baseline performance, age, diagnosis, and training condition. After regressing out the variability induced by training condition, baseline performance, and age, perceptual learning proved more effective for children with idiopathic IN than for children with albinism accompanied by IN. Training gains increased with poorer baseline performance in idiopaths, but not in children with albinism accompanied by IN. Age and baseline performance, but not training joy, are important prognostic factors for the effect of perceptual learning in children with IN. However, their predictive value for achieving improvements in single-letter acuity and crowded letter acuity, respectively, differs between diagnostic subgroups and training condition. These findings may help with personalized treatment of individuals likely to benefit from perceptual learning.

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.

Predictors and Variability of Urinary Paraben Concentrations in Men and Women, Including before and during Pregnancy

PubMed Central

Smith, Kristen W.; Braun, Joe M.; Williams, Paige L.; Ehrlich, Shelley; Correia, Katharine F.; Calafat, Antonia M.; Ye, Xiaoyun; Ford, Jennifer; Keller, Myra; Meeker, John D.

2012-01-01

Background: Parabens are suspected endocrine disruptors and ubiquitous preservatives used in personal care products, pharmaceuticals, and foods. No studies have assessed the variability of parabens in women, including during pregnancy. Objective: We evaluated predictors and variability of urinary paraben concentrations. Methods: We measured urinary concentrations of methyl (MP), propyl (PP), and butyl paraben (BP) among couples from a fertility center. Mixed-effects regression models were fit to examine demographic predictors of paraben concentrations and to calculate intraclass correlation coefficients (ICCs). Results: Between 2005 and 2010, we collected 2,721 spot urine samples from 245 men and 408 women. The median concentrations were 112 µg/L (MP), 24.2 µg/L (PP), and 0.70 µg/L (BP). Urinary MP and PP concentrations were 4.6 and 7.8 times higher in women than men, respectively, and concentrations of both MP and PP were 3.8 times higher in African Americans than Caucasians. MP and PP concentrations we CI re slightly more variable in women (ICC = 0.42, 0.43) than men (ICC = 0.54, 0.51), and were weakly correlated between partners (r = 0.27–0.32). Among 129 pregnant women, urinary paraben concentrations were 25–45% lower during pregnancy than before pregnancy, and MP and PP concentrations were more variable (ICCs of 0.38 and 0.36 compared with 0.46 and 0.44, respectively). Conclusions: Urinary paraben concentrations were more variable in women compared with men, and during pregnancy compared with before pregnancy. However, results for this study population suggest that a single urine sample may reasonably represent an individual’s exposure over several months, and that a single sample collected during pregnancy may reasonably classify gestational exposure. PMID:22721761

Predictors and variability of urinary paraben concentrations in men and women, including before and during pregnancy.

PubMed

Smith, Kristen W; Braun, Joe M; Williams, Paige L; Ehrlich, Shelley; Correia, Katharine F; Calafat, Antonia M; Ye, Xiaoyun; Ford, Jennifer; Keller, Myra; Meeker, John D; Hauser, Russ

2012-11-01

Parabens are suspected endocrine disruptors and ubiquitous preservatives used in personal care products, pharmaceuticals, and foods. No studies have assessed the variability of parabens in women, including during pregnancy. We evaluated predictors and variability of urinary paraben concentrations. We measured urinary concentrations of methyl (MP), propyl (PP), and butyl paraben (BP) among couples from a fertility center. Mixed-effects regression models were fit to examine demographic predictors of paraben concentrations and to calculate intraclass correlation coefficients (ICCs). Between 2005 and 2010, we collected 2,721 spot urine samples from 245 men and 408 women. The median concentrations were 112 µg/L (MP), 24.2 µg/L (PP), and 0.70 µg/L (BP). Urinary MP and PP concentrations were 4.6 and 7.8 times higher in women than men, respectively, and concentrations of both MP and PP were 3.8 times higher in African Americans than Caucasians. MP and PP concentrations were slightly more variable in women (ICC = 0.42, 0.43) than men (ICC = 0.54, 0.51), and were weakly correlated between partners (r = 0.27-0.32). Among 129 pregnant women, urinary paraben concentrations were 25-45% lower during pregnancy than before pregnancy, and MP and PP concentrations were more variable (ICCs of 0.38 and 0.36 compared with 0.46 and 0.44, respectively). Urinary paraben concentrations were more variable in women compared with men, and during pregnancy compared with before pregnancy. However, results for this study population suggest that a single urine sample may reasonably represent an individual's exposure over several months, and that a single sample collected during pregnancy may reasonably classify gestational exposure.

Factors predicting recall of mathematics terms by deaf students: implications for teaching.

PubMed

Lang, Harry; Pagliaro, Claudia

2007-01-01

In this study of deaf high school students, imagery and familiarity were found to be the best predictors of geometry word recall, whereas neither concreteness nor signability of the terms was a significant predictor variable. Recall of high imagery terms was significantly better than for low imagery terms, and the same result was found for high- over low-familiarity and signability. Concrete terms were recalled significantly better than abstract terms. Geometry terms that could be represented with single signs were recalled significantly better than those that are usually fingerspelled or those represented by compound signs. Teachers with degrees and/or certification in mathematics had significantly higher self-ratings for the strongest predictor variables, imagery (visualization), and familiarity, as compared with those without such formal training. Based on these findings, implications for mathematics instruction, teacher education, and research are provided.

Using a Market Ratio Factor in Faculty Salary Equity Studies. Professional File Number 103, Spring 2007

ERIC Educational Resources Information Center

Luna, Andrew L.

2007-01-01

This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. The central purpose of the study was to determine if using a single market ratio variable was a stronger predictor for faculty salaries than the use of dummy variables representing various disciplines.…

Predictors of failure after single faecal microbiota transplantation in patients with recurrent Clostridium difficile infection: results from a 3-year, single-centre cohort study.

PubMed

Ianiro, G; Valerio, L; Masucci, L; Pecere, S; Bibbò, S; Quaranta, G; Posteraro, B; Currò, D; Sanguinetti, M; Gasbarrini, A; Cammarota, G

2017-05-01

Faecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection (CDI). Although a single faecal infusion is usually sufficient to eradicate CDI, a considerable number of patients need multiple infusions to be cured. The aim of this study was to identify predictors of failure after single faecal infusion in patients with recurrent CDI. We included patients with recurrent CDI prospectively treated with FMT by colonoscopy. By means of univariate and multivariate analysis, variables including female gender, age, number of CDI recurrences, severity of CDI, hospitalization, inadequate bowel preparation, unrelated donor, and use of frozen faeces, were assessed to predict failure after single faecal infusion. Sixty-four patients (39 women; mean age 74 years) were included. Of them, 44 (69%) were cured by a single faecal infusion, whereas 20 (31%) needed repeat infusions. Overall, FMT cured 62 of 64 (97%) patients. In the subgroup of patients with severe CDI, only eight of 26 (30%) were cured with a single infusion. At multivariate analysis, severe CDI (OR 24.66; 95% CI 4.44-242.08; p 0.001) and inadequate bowel preparation (OR 11.53; 95% CI 1.71-115.51; p 0.019) were found to be independent predictors of failure after single faecal infusion. Severe CDI and inadequate bowel preparation appear to be independent predictors of failure after single faecal infusion in patients treated with FMT by colonoscopy for recurrent CDI. Our results may help to optimize protocols and outcomes of FMT in patients with recurrent CDI. Copyright © 2016 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Solving Ordinary Differential Equations

NASA Technical Reports Server (NTRS)

Krogh, F. T.

1987-01-01

Initial-value ordinary differential equation solution via variable order Adams method (SIVA/DIVA) package is collection of subroutines for solution of nonstiff ordinary differential equations. There are versions for single-precision and double-precision arithmetic. Requires fewer evaluations of derivatives than other variable-order Adams predictor/ corrector methods. Option for direct integration of second-order equations makes integration of trajectory problems significantly more efficient. Written in FORTRAN 77.

Depression and quality of life for women in single-parent and nuclear families.

PubMed

Landero Hernández, René; Estrada Aranda, Benito; González Ramírez, Mónica Teresa

2009-05-01

This is a cross-sectional study which objectives are 1) to determine the predictors for perceived quality of life and 2) to analyze the differences between women from single-parent families and bi-parent families, about their quality of life, depression and familiar income. We worked with a non-probabilistic sample of 140 women from Monterrey, N.L, Mexico, 107 are from bi-parent families and 33 from single parent families. Some of the results show that women from single-parent families have lower quality of life (Z = -2.224, p = .026), lower income (Z = -2.727, p = .006) and greater depression (Z = -6.143, p = .001) than women from bi-parental families. The perceived quality of life's predictors, using a multiple regression model (n = 140) were depression, income and number of children, those variables explaining 25.4% of variance.

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.

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

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.

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.

Disease phobia and disease conviction are separate dimensions underlying hypochondriasis.

PubMed

Fergus, Thomas A; Valentiner, David P

2010-12-01

The current study uses data from a large nonclinical college student sample (N = 503) to examine a structural model of hypochondriasis (HC). This model predicts the distinctiveness of two dimensions (disease phobia and disease conviction) purported to underlie the disorder, and that these two dimensions are differentially related to variables important to health anxiety and somatoform disorders, respectively. Results were generally consistent with the hypothesized model. Specifically, (a) body perception variables (somatosensory amplification and anxiety sensitivity - physical) emerged as significant predictors of disease phobia, but not disease conviction; (b) emotion dysregulation variables (cognitive avoidance and cognitive reappraisal) emerged as significant predictors of disease conviction, but not disease phobia; and (c) both disease phobia and disease conviction independently predicted medical utilization. Further, collapsing disease phobia and disease conviction onto a single latent factor provided an inadequate fit to the data. Conceptual and therapeutic implications of these results are discussed. 2010 Elsevier Ltd. All rights reserved.

Estimating Interaction Effects With Incomplete Predictor Variables

PubMed Central

Enders, Craig K.; Baraldi, Amanda N.; Cham, Heining

2014-01-01

The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of probing interaction effects with maximum likelihood and multiple imputation. For both missing data handling techniques, we outline centering and transformation strategies that researchers can implement in popular software packages, and we use a series of real data analyses to illustrate these methods. Finally, we use computer simulations to evaluate the performance of the proposed techniques. PMID:24707955

Predictability of Bristol Bay, Alaska, sockeye salmon returns one to four years in the future

USGS Publications Warehouse

Adkison, Milo D.; Peterson, R.M.

2000-01-01

Historically, forecast error for returns of sockeye salmon Oncorhynchus nerka to Bristol Bay, Alaska, has been large. Using cross-validation forecast error as our criterion, we selected forecast models for each of the nine principal Bristol Bay drainages. Competing forecast models included stock-recruitment relationships, environmental variables, prior returns of siblings, or combinations of these predictors. For most stocks, we found prior returns of siblings to be the best single predictor of returns; however, forecast accuracy was low even when multiple predictors were considered. For a typical drainage, an 80% confidence interval ranged from one half to double the point forecast. These confidence intervals appeared to be appropriately wide.

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

DTIC Science & Technology

2017-02-01

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

Gender Inequality and Rates of Female Homicide Victimization across U.S. Cities.

ERIC Educational Resources Information Center

Brewer, Victoria E.; Smith, M. Dwayne

1995-01-01

Explores the possibility that female victimization rates are influenced by conditions of sex-based inequality. No single inequality variable was found to be a statistically significant predictor of female homicide rates when controlling for social structural effects. Found little support for gender inequality/female homicide connection. (JBJ)

Age, Body Mass Index, and Daytime and Nocturnal Hypoxia as Predictors of Hypertension in Patients With Obstructive Sleep Apnea.

PubMed

Natsios, Georgios; Pastaka, Chaido; Vavougios, Georgios; Zarogiannis, Sotirios G; Tsolaki, Vasiliki; Dimoulis, Andreas; Seitanidis, Georgios; Gourgoulianis, Konstantinos I

2016-02-01

A growing body of evidence links obstructive sleep apnea (OSA) with hypertension. The authors performed a retrospective cohort study using the University Hospital of Larissa Sleep Apnea Database (1501 patients) to determine predictors of in-laboratory diagnosed OSA for development of hypertension. Differences in continuous variables were assessed via independent samples t test, whereas discrete variables were compared by Pearson's chi-square test. Multivariate analysis was performed via discriminant function analysis. There were several significant differences between hypertensive and normotensive patients. Age, body mass index, comorbidity, daytime oxygen saturation, and indices of hypoxia during sleep were deemed the most accurate predictors of hypertension, whereas apnea-hypopnea index and desaturation index were not. The single derived discriminant function was statistically significant (Wilk's lambda=0.771, χ(2) =289.070, P<.0001). Daytime and nocturnal hypoxia as consequences of chronic intermittent hypoxia play a central role in OSA-related hypertension and should be further evaluated as possible severity markers in OSA. ©2015 Wiley Periodicals, Inc.

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

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

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) 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 National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Single-breath CO2 analysis as a predictor of lung volume in a healthy animal model during controlled ventilation.

PubMed

Stenz, R I; Grenier, B; Thompson, J E; Arnold, J H

1998-08-01

To examine the utility of single-breath CO2 analysis as a measure of lung volume. A prospective, animal cohort study comparing 21 parameters derived from single-breath CO2 analysis with lung volume measurements determined by nitrogen washout in animals during controlled ventilation. An animal laboratory in a university-affiliated medical center. Seven healthy lambs. The single-breath CO2 analysis station consists of a mainstream capnometer, a variable orifice pneumotachometer, a signal processor and computer software with capability for both on- and off-line data analysis. Twenty-one derived components of the CO2 expirogram were evaluated as predictors of lung volume. Lung volume was manipulated by 3 cm H2O incremental increases in positive end-expiratory pressure from 0 to 21 cm H2O, and ranged between 147 and 942 mL. Fifty-five measurements of lung volume were available for comparison with derived variables from the CO2 expirogam. Stepwise linear regression identified four variables that were most predictive of lung volume: a) dynamic lung compliance; b) the slope of phase 3; c) the slope of phase 2 divided by the mixed expired CO2 tension; and d) airway deadspace. The multivariate equation was highly statistically significant and explained 94% of the variance (adjusted r2 =.94, p < .0001). The bias and precision of the calculated lung volume was .00 and 51, respectively. The mean percent difference for the lung volume estimate derived from the single-breath CO2 analysis station was 0.79%. Our data indicate that analysis of the CO2 expirogram can yield accurate information about lung volume. Specifically, four variables derived from a plot of expired CO2 concentration vs. expired volume predict changes in lung volume in healthy lambs with an adjusted coefficient of determination of .94. Prospective application of this technology in the setting of lung injury and rapidly changing physiology is essential in determining the clinical usefulness of the technique.

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

PubMed

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

2018-02-12

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

Isolating the Role of Psychological Dysfunction in Smoking Cessation Failure: Relations of Personality and Psychopathology to Attaining Smoking Cessation Milestones

PubMed Central

Leventhal, Adam M.; Japuntich, Sandra J.; Piper, Megan E.; Jorenby, Douglas E.; Schlam, Tanya R.; Baker, Timothy B.

2012-01-01

Research exploring psychological dysfunction as a predictor of smoking cessation success may be limited by nonoptimal predictor variables (i.e., categorical psychodiagnostic measures vs. continuous personality-based manifestations of dysfunction) and imprecise outcomes (i.e., summative point prevalence abstinence vs. constituent cessation milestone measures). Accordingly, this study evaluated the unique and overlapping relations of broad-spectrum personality traits (positive emotionality, negative emotionality, and constraint) and past-year psychopathology (anxiety, mood, and substance use disorder) to point prevalence abstinence and three smoking cessation milestones: (1) initiating abstinence; (2) first lapse; and (3) transition from lapse to relapse. Participants were daily smokers (N=1365) enrolled in a smoking cessation treatment study. In single predictor regression models, each manifestation of internalizing dysfunction (lower positive emotionality, higher negative emotionality, and anxiety and mood disorder) predicted failure at one or more cessation milestone. In simultaneous predictor models, lower positive and higher negative emotionality significantly predicted failure to achieve milestones after controlling for psychopathology. Psychopathology did not predict any outcome when controlling for personality. Negative emotionality showed the most robust and consistent effects, significantly predicting failure to initiate abstinence, earlier lapse, and lower point prevalence abstinence rates. Substance use disorder and constraint did not predict cessation outcomes, and no single variable predicted lapse-to-relapse transition. These findings suggest that personality-related manifestations of internalizing dysfunction are more accurate markers of affective sources of relapse risk than mood and anxiety disorders. Further, individuals with high trait negative emotionality may require intensive intervention to promote the initiation and early maintenance of abstinence. PMID:22642858

Personality: A Predictor of Theoretical Orientation of Students Enrolled in a Counseling Theories Course

ERIC Educational Resources Information Center

Freeman, Mark S.; Hayes, B. Grant; Kuch, Tyson H.; Taub, Gordon

2007-01-01

Selecting a single psychotherapeutic orientation can be a challenge for counselor education students. The authors examined the relationship between counseling theory selection and personality variables of students enrolled in a counseling theories course. A discriminant function analysis was used to identify the personality traits that would…

Bayesian whole-genome prediction and genome-wide association analysis with missing genotypes using variable selection

USDA-ARS?s Scientific Manuscript database

Single-step Genomic Best Linear Unbiased Predictor (ssGBLUP) has become increasingly popular for whole-genome prediction (WGP) modeling as it utilizes any available pedigree and phenotypes on both genotyped and non-genotyped individuals. The WGP accuracy of ssGBLUP has been demonstrated to be greate...

Prediction of placebo responses: a systematic review of the literature

PubMed Central

Horing, Bjoern; Weimer, Katja; Muth, Eric R.; Enck, Paul

2014-01-01

Objective: Predicting who responds to placebo treatment—and under which circumstances—has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed. Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted. PMID:25324797

The Importance of Gestational Sac Size of Ectopic Pregnancy in Response to Single-Dose Methotrexate

PubMed Central

Kimiaei, Parichehr; Khani, Zahra; Marefian, Azadeh; Gholampour Ghavamabadi, Maryam; Salimnejad, Maryam

2013-01-01

This retrospective cohort study was designed in a selective group of 185 patients diagnosed with and treated for ectopic pregnancy. Intramuscular administration of a single dose of methotrexate (50 mg/m2) was performed to measure predictors of failure or resistance to treatment necessitating surgical intervention. During the time of treatment with a single dose of MTX, 20 patients (10.8%) failed to response, in which 6 of 20 (30%) indicated side effects to MTX and rupture of the ectopic pregnancy. Remaining cases (n = 14) showed resistance to the drug; the level of β-hCG did not fall at least 15% during 7 days after treatment and necessitated laparotomy. In backward-step analysis by multiple logistic regressions of various types of predictor factors, size of gestational sac (coefficient = 1.91, OR = 6.78, 95% confidence interval = 3.18–8.22) and baseline level β-hCG (coefficient = 1.60, OR = 5.0, 95% confidence interval = 4.26–6.72) had significant correlation with leading EP patients failing to response to MTX. This study suggests that further investigation for finding relative contraindications of MTX treatment in EP women should be considered on the gestational sac size because other variables are in the causal pathway of this variable. PMID:23762575

The unusual suspect: Land use is a key predictor of biodiversity patterns in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Martins, Inês Santos; Proença, Vânia; Pereira, Henrique Miguel

2014-11-01

Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species-area relationship with a multi-habitat model, the countryside species-area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.

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…

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.

Variation in transfusion rates within a single institution: exploring the effect of differing practice patterns on the likelihood of blood product transfusion in patients undergoing cardiac surgery.

PubMed

Cote, Claudia; MacLeod, Jeffrey B; Yip, Alexandra M; Ouzounian, Maral; Brown, Craig D; Forgie, Rand; Pelletier, Marc P; Hassan, Ansar

2015-01-01

Rates of perioperative transfusion vary widely among patients undergoing cardiac surgery. Few studies have examined factors beyond the clinical characteristics of the patients that may be responsible for such variation. The purpose of this study was to determine whether differing practice patterns had an impact on variation in perioperative transfusion at a single center. Patients who underwent cardiac surgery at a single center between 2004 and 2011 were considered. Comparisons were made between patients who had received a perioperative transfusion and those who had not from the clinical factors at baseline, intraoperative variables, and differing practice patterns, as defined by the surgeon, anesthesiologist, perfusionist, and the year in which the procedure was performed. The risk-adjusted effect of these factors on perioperative transfusion rates was determined using multivariable regression modeling techniques. The study population comprised 4823 patients, of whom 1929 (40.0%) received a perioperative transfusion. Significant variation in perioperative transfusion rates was noted between surgeons (from 32.4% to 51.5%, P < .0001), anesthesiologists (from 34.4% to 51.9%, P < .0001) and across year (from 28.2% in 2004 to 48.8% in 2008, P < .0001). After adjustment for baseline and intraoperative variables, surgeon, anesthesiologist, and year of procedure were each found to be independent predictors of perioperative transfusion. Differing practice patterns contribute to significant variation in rates of perioperative transfusion within a single center. Strategies aimed at reducing overall transfusion rates must take into account such variability in practice patterns and account for nonclinical factors as well as known clinical predictors of blood transfusions. Copyright © 2015 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Predicting In-State Workforce Retention After Graduate Medical Education Training.

PubMed

Koehler, Tracy J; Goodfellow, Jaclyn; Davis, Alan T; Spybrook, Jessaca; vanSchagen, John E; Schuh, Lori

2017-02-01

There is a paucity of literature when it comes to identifying predictors of in-state retention of graduate medical education (GME) graduates, such as the demographic and educational characteristics of these physicians. The purpose was to use demographic and educational predictors to identify graduates from a single Michigan GME sponsoring institution, who are also likely to practice medicine in Michigan post-GME training. We included all residents and fellows who graduated between 2000 and 2014 from 1 of 18 GME programs at a Michigan-based sponsoring institution. Predictor variables identified by logistic regression with cross-validation were used to create a scoring tool to determine the likelihood of a GME graduate to practice medicine in the same state post-GME training. A 6-variable model, which included 714 observations, was identified. The predictor variables were birth state, program type (primary care versus non-primary care), undergraduate degree location, medical school location, state in which GME training was completed, and marital status. The positive likelihood ratio (+LR) for the scoring tool was 5.31, while the negative likelihood ratio (-LR) was 0.46, with an accuracy of 74%. The +LR indicates that the scoring tool was useful in predicting whether graduates who trained in a Michigan-based GME sponsoring institution were likely to practice medicine in Michigan following training. Other institutions could use these techniques to identify key information that could help pinpoint matriculating residents/fellows likely to practice medicine within the state in which they completed their training.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

A Polychoric Correlation to Identify the Principle Component in Classifying Single Tuition Fee Capabilities on the Students Socio-Economic Database

NASA Astrophysics Data System (ADS)

Yustanti, W.; Anistyasari, Y.

2018-01-01

The government has issued the regulation number 55 of 2013 about the enactment of a single tuition fee based on the socio-economic conditions of each student. All public universities are required to implement this policy. Therefore, each university needs to create a formulation that can be used to categorize a student into which cost group. The results of the data collection found that the parameters used to determine the classification of tuition fees between one universities with another are different. In this research, taken a sampling of student data at one public university which is using 43 predictor variables and 8 categories of single tuition. The sample data used are socioeconomic data of students of 2016 and 2017 classes received through public university entrance selections. The results of this study reveal that from 43 variables, there are 16 variables which are the most significant in influencing single tuition category with goodness-of-fit index is 0.866. This value means that the proposed model can indicate student’s ability to pay the tuition fee.

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

PubMed Central

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

2011-01-01

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

A SIGNIFICANCE TEST FOR THE LASSO1

PubMed Central

Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert

2014-01-01

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model). Our proof of this result for the special case of the first predictor to enter the model (i.e., testing for a single significant predictor variable against the global null) requires only weak assumptions on the predictor matrix X. On the other hand, our proof for a general step in the lasso path places further technical assumptions on X and the generative model, but still allows for the important high-dimensional case p > n, and does not necessarily require that the current lasso model achieves perfect recovery of the truly active variables. Of course, for testing the significance of an additional variable between two nested linear models, one typically uses the chi-squared test, comparing the drop in residual sum of squares (RSS) to a χ12 distribution. But when this additional variable is not fixed, and has been chosen adaptively or greedily, this test is no longer appropriate: adaptivity makes the drop in RSS stochastically much larger than χ12 under the null hypothesis. Our analysis explicitly accounts for adaptivity, as it must, since the lasso builds an adaptive sequence of linear models as the tuning parameter λ decreases. In this analysis, shrinkage plays a key role: though additional variables are chosen adaptively, the coefficients of lasso active variables are shrunken due to the l1 penalty. Therefore, the test statistic (which is based on lasso fitted values) is in a sense balanced by these two opposing properties—adaptivity and shrinkage—and its null distribution is tractable and asymptotically Exp(1). PMID:25574062

Prediction of objectively measured physical activity and sedentariness among blue-collar workers using survey questionnaires.

PubMed

Gupta, Nidhi; Heiden, Marina; Mathiassen, Svend Erik; Holtermann, Andreas

2016-05-01

We aimed at developing and evaluating statistical models predicting objectively measured occupational time spent sedentary or in physical activity from self-reported information available in large epidemiological studies and surveys. Two-hundred-and-fourteen blue-collar workers responded to a questionnaire containing information about personal and work related variables, available in most large epidemiological studies and surveys. Workers also wore accelerometers for 1-4 days measuring time spent sedentary and in physical activity, defined as non-sedentary time. Least-squares linear regression models were developed, predicting objectively measured exposures from selected predictors in the questionnaire. A full prediction model based on age, gender, body mass index, job group, self-reported occupational physical activity (OPA), and self-reported occupational sedentary time (OST) explained 63% (R (2)adjusted) of the variance of both objectively measured time spent sedentary and in physical activity since these two exposures were complementary. Single-predictor models based only on self-reported information about either OPA or OST explained 21% and 38%, respectively, of the variance of the objectively measured exposures. Internal validation using bootstrapping suggested that the full and single-predictor models would show almost the same performance in new datasets as in that used for modelling. Both full and single-predictor models based on self-reported information typically available in most large epidemiological studies and surveys were able to predict objectively measured occupational time spent sedentary or in physical activity, with explained variances ranging from 21-63%.

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

Demographic and psychosocial risk factors for preterm delivery in an active duty pregnant population.

PubMed

Evans, M A; Rosen, L N

2000-01-01

The effects of work climate, pregnancy transitions stress, maternal medical conditions, health risk behaviors, psychological health, and demographic characteristics were examined among 269 pregnant military women. The study found that single and separated/divorced military women were at greater risk for preterm delivery than married women. Unmarried participants were more likely to belong to ethnic minorities, were lower ranking, less educated, and reported a greater number of medical conditions than married participants. Psychosocial variables distinguished the three marital status groups--married, single, and separated/divorced--but none of these variables was related to preterm delivery. In a logistic regression analysis, marital status was a more significant predictor of preterm delivery than were medical conditions.

Relating forest attributes with area- and tree-based light detection and ranging metrics for western Oregon

Treesearch

Michael E. Goerndt; Vincente J. Monleon; Hailemariam. Temesgen

2010-01-01

Three sets of linear models were developed to predict several forest attributes, using stand-level and single-tree remote sensing (STRS) light detection and ranging (LiDAR) metrics as predictor variables. The first used only area-level metrics (ALM) associated with first-return height distribution, percentage of cover, and canopy transparency. The second alternative...

Predicting Students' Skills in the Context of Scientific Inquiry with Cognitive, Motivational, and Sociodemographic Variables

ERIC Educational Resources Information Center

Nehring, Andreas; Nowak, Kathrin H.; zu Belzen, Annette Upmeier; Tiemann, Rüdiger

2015-01-01

Research on predictors of achievement in science is often targeted on more traditional content-based assessments and single student characteristics. At the same time, the development of skills in the field of scientific inquiry constitutes a focal point of interest for science education. Against this background, the purpose of this study was to…

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.

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…

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

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

2012-01-01

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

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.

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.

The variable clinical presentation of tuberculosis otitis media and the importance of early detection.

PubMed

Abes, Generoso T; Abes, Franco Louie L B; Jamir, Joselito C

2011-06-01

Tuberculosis (TB) is a rare cause of otitis media. This study aims to increase awareness on the clinical presentation of TB otitis media and illustrate how early detection affects treatment outcome. Chart review of 12 patients (13 ears) from a tertiary hospital in Manila, Philippines, seen from 2004 to 2009. Clinical predictors of the disease were summarized. Clinical, radiologic, and audiometric outcomes after treatment were compared between treatment groups. The 5 otoscopic presentations were multiple perforations, single perforation with refractory otorrhea and exuberant granulation tissue formation, single perforation with minimal otorrhea and no granulation tissue formation, intact tympanic membrane with middle ear effusion, and intact tympanic membrane with tumorlike tissue in the middle ear. Clinical predictors of the disease were history of pulmonary TB, work-related contamination of the infection, positive purified protein derivative test, positive chest radiographic finding and intraoperative granulation tissue with cheesy material, and temporal bone computed tomographic scan findings. Patients who had no middle ear surgery showed significantly better clinical, radiologic, and audiometric outcomes than those who were diagnosed late and had more complicated surgical procedure. The clinical presentation of TB otitis media is variable. Early detection of the early forms entail less surgical intervention and favors better treatment results.

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.

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…

Length of positive surgical margin after radical prostatectomy as a predictor of biochemical recurrence.

PubMed

Shikanov, Sergey; Song, Jie; Royce, Cassandra; Al-Ahmadie, Hikmat; Zorn, Kevin; Steinberg, Gary; Zagaja, Gregory; Shalhav, Arieh; Eggener, Scott

2009-07-01

Length and location of positive surgical margins are independent predictors of biochemical recurrence after open radical prostatectomy. We assessed their impact on biochemical recurrence in a large robotic prostatectomy series. Data were collected prospectively from 1,398 men undergoing robotic radical prostatectomy for clinically localized prostate cancer from 2003 to 2008 at a single institution. The associations of preoperative prostate specific antigen, pathological Gleason score, pathological stage and positive surgical margin parameters (location, length and focality) with biochemical recurrence rate were evaluated. Margin status and length were measured by a single uropathologist. Biochemical recurrence was defined as serum prostate specific antigen greater than 0.1 ng/ml on 2 consecutive tests. Cox regression models were constructed to evaluate predictors of biochemical recurrence. Of 1,398 consecutive patients who underwent robotic prostatectomy positive margins were present in 243 (17%) (11% of pathological T2 and 41% of T3). Preoperative prostate specific antigen, pathological stage, Gleason score, margin status, and margin length as a continuous and categorical variable (less than 1, 1 to 3, more than 3 mm) were independent predictors of biochemical recurrence. Patients with negative margins and those with a positive margin less than 1 mm had similar rates of biochemical recurrence (log rank test p = 0.18). Surgical margin location was not independently associated with biochemical recurrence. Margin status and length are independent predictors of biochemical recurrence following robotic radical prostatectomy. Although longer followup and validation studies are necessary for confirmation, patients with a positive margin less than 1 mm appear to have similar recurrence rates as those with negative margins.

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.

Laboratory- and field-based testing as predictors of skating performance in competitive-level female ice hockey.

PubMed

Henriksson, Tommy; Vescovi, Jason D; Fjellman-Wiklund, Anncristine; Gilenstam, Kajsa

2016-01-01

The purpose of this study was to examine whether field-based and/or laboratory-based assessments are valid tools for predicting key performance characteristics of skating in competitive-level female hockey players. Cross-sectional study. Twenty-three female ice hockey players aged 15-25 years (body mass: 66.1±6.3 kg; height: 169.5±5.5 cm), with 10.6±3.2 years playing experience volunteered to participate in the study. The field-based assessments included 20 m sprint, squat jump, countermovement jump, 30-second repeated jump test, standing long jump, single-leg standing long jump, 20 m shuttle run test, isometric leg pull, one-repetition maximum bench press, and one-repetition maximum squats. The laboratory-based assessments included body composition (dual energy X-ray absorptiometry), maximal aerobic power, and isokinetic strength (Biodex). The on-ice tests included agility cornering s-turn, cone agility skate, transition agility skate, and modified repeat skate sprint. Data were analyzed using stepwise multivariate linear regression analysis. Linear regression analysis was used to establish the relationship between key performance characteristics of skating and the predictor variables. Regression models (adj R (2)) for the on-ice variables ranged from 0.244 to 0.663 for the field-based assessments and from 0.136 to 0.420 for the laboratory-based assessments. Single-leg tests were the strongest predictors for key performance characteristics of skating. Single leg standing long jump alone explained 57.1%, 38.1%, and 29.1% of the variance in skating time during transition agility skate, agility cornering s-turn, and modified repeat skate sprint, respectively. Isokinetic peak torque in the quadriceps at 90° explained 42.0% and 32.2% of the variance in skating time during agility cornering s-turn and modified repeat skate sprint, respectively. Field-based assessments, particularly single-leg tests, are an adequate substitute to more expensive and time-consuming laboratory assessments if the purpose is to gain knowledge about key performance characteristics of skating.

Laboratory- and field-based testing as predictors of skating performance in competitive-level female ice hockey

PubMed Central

Henriksson, Tommy; Vescovi, Jason D; Fjellman-Wiklund, Anncristine; Gilenstam, Kajsa

2016-01-01

Objectives The purpose of this study was to examine whether field-based and/or laboratory-based assessments are valid tools for predicting key performance characteristics of skating in competitive-level female hockey players. Design Cross-sectional study. Methods Twenty-three female ice hockey players aged 15–25 years (body mass: 66.1±6.3 kg; height: 169.5±5.5 cm), with 10.6±3.2 years playing experience volunteered to participate in the study. The field-based assessments included 20 m sprint, squat jump, countermovement jump, 30-second repeated jump test, standing long jump, single-leg standing long jump, 20 m shuttle run test, isometric leg pull, one-repetition maximum bench press, and one-repetition maximum squats. The laboratory-based assessments included body composition (dual energy X-ray absorptiometry), maximal aerobic power, and isokinetic strength (Biodex). The on-ice tests included agility cornering s-turn, cone agility skate, transition agility skate, and modified repeat skate sprint. Data were analyzed using stepwise multivariate linear regression analysis. Linear regression analysis was used to establish the relationship between key performance characteristics of skating and the predictor variables. Results Regression models (adj R2) for the on-ice variables ranged from 0.244 to 0.663 for the field-based assessments and from 0.136 to 0.420 for the laboratory-based assessments. Single-leg tests were the strongest predictors for key performance characteristics of skating. Single leg standing long jump alone explained 57.1%, 38.1%, and 29.1% of the variance in skating time during transition agility skate, agility cornering s-turn, and modified repeat skate sprint, respectively. Isokinetic peak torque in the quadriceps at 90° explained 42.0% and 32.2% of the variance in skating time during agility cornering s-turn and modified repeat skate sprint, respectively. Conclusion Field-based assessments, particularly single-leg tests, are an adequate substitute to more expensive and time-consuming laboratory assessments if the purpose is to gain knowledge about key performance characteristics of skating. PMID:27574474

Healthcare-associated pneumonia with positive respiratory methicillin-resistant Staphylococcus aureus culture: Predictors of the true pathogenicity.

PubMed

Enomoto, Yasunori; Yokomura, Koshi; Hasegawa, Hirotsugu; Ozawa, Yuichi; Matsui, Takashi; Suda, Takafumi

2017-03-01

Although methicillin-resistant Staphylococcus aureus (MRSA) is commonly isolated from respiratory specimens in healthcare-associated pneumonia (HCAP), it is difficult to determine the causative pathogen because of the possibilities of contamination/colonization. The present study aimed to identify clinical predictors of the true pathogenicity of MRSA in HCAP. Patients with HCAP with positive MRSA cultures in the sputum or endotracheal aspirates who were admitted to Seirei Mikatahara General Hospital, Hamamatsu, Japan, from 2009 to 2014 were enrolled. According to the administered drugs and the treatment outcomes, patients with true MRSA pneumonia (MP) and those with contamination/colonization of MRSA (false MP) were identified. Baseline characteristics were compared between groups, and clinical predictors of true MP were evaluated by logistic regression analyses. A total of 93 patients (mean age 78.7 ± 12.6 years) were identified and classified into the true MP (n = 16) or false MP (n = 77) groups. Although baseline characteristics were broadly similar between groups, the true MP group had significantly more patients with PaO 2  ≤ 60 Torr/pulse oximetry saturation ≤90% and those with MRSA single cultivation. Both variables were significant predictors of true MP in multivariate analysis (odds ratio of PaO 2  ≤ 60 Torr/pulse oximetry saturation ≤90%: 5.64, 95% confidence interval 1.17-27.32; odds ratio of MRSA single cultivation: 4.76, 95% confidence interval 1.22-18.60). Poor oxygenation and MRSA single cultivation imply the true pathogenicity of MRSA in HCAP with positive respiratory MRSA cultures. The present results might be helpful for the proper use of anti-MRSA drugs in this population. Geriatr Gerontol Int 2017; 17: 456-462. © 2016 Japan Geriatrics Society.

Some difficulties and inconsistencies when using habit strength and reasoned action variables in models of metered household water conservation.

PubMed

Jorgensen, Bradley S; Martin, John F; Pearce, Meryl; Willis, Eileen

2013-01-30

Research employing household water consumption data has sought to test models of water demand and conservation using variables from attitude theory. A significant, albeit unrecognised, challenge has been that attitude models describe individual-level motivations while consumption data is recorded at the household level thereby creating inconsistency between units of theory and measurement. This study employs structural equation modelling and moderated regression techniques to addresses the level of analysis problem, and tests hypotheses by isolating effects on water conservation in single-person households. Furthermore, the results question the explanatory utility of habit strength, perceived behavioural control, and intentions for understanding metered water conservation in single-person households. For example, evidence that intentions predict water conservation or that they interact with habit strength in single-person households was contrary to theoretical expectations. On the other hand, habit strength, self-reports of past water conservation, and perceived behavioural control were good predictors of intentions to conserve water. Copyright © 2012 Elsevier Ltd. All rights reserved.

Ten month outcome of cognitive behavioural therapy v. interpersonal psychotherapy in patients with major depression: a randomised trial of acute and maintenance psychotherapy.

PubMed

Mulder, R; Boden, J; Carter, J; Luty, S; Joyce, P

2017-10-01

Cognitive behaviour therapy (CBT) and interpersonal psychotherapy (IPT) are the most studied psychotherapies for treatment of depression, but they are rarely directly compared particularly over the longer term. This study compares the outcomes of patients treated with CBT and IPT over 10 months and tests whether there are differential or general predictors of outcome. A single centre randomised controlled trial (RCT) of depressed outpatients treated with weekly CBT or IPT sessions for 16 weeks and then 24 weeks of maintenance CBT or IPT. The principle outcome was depression severity measured using the MADRS. Pre-specified predictors of response were in four domains: demographic depression, characteristics, comorbidity and personality. Data were analysed over 16 weeks and 40 weeks using general linear mixed effects regression models. CBT was significantly more effective than IPT in reducing depressive symptoms over the 10 month study largely because it appeared to work more quickly. There were no differential predictors of response to CBT v. IPT at 16 weeks or 40 weeks. Personality variables were most strongly associated with overall outcome at both 16 weeks and 40 weeks. The number of personality disorder symptoms and lower self-directness and reward dependence scores were associated with poorer outcome for both CBT and IPT at 40 weeks. CBT and IPT are effective treatments for major depression over the longer term. CBT may work more quickly. Personality variables are the most relevant predictors of outcome.

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 Variables for Marathon Race Time in Recreational Female Runners

PubMed Central

Schmid, Wiebke; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald

2012-01-01

Purpose We intended to determine predictor variables of anthropometry and training for marathon race time in recreational female runners in order to predict marathon race time for future novice female runners. Methods Anthropometric characteristics such as body mass, body height, body mass index, circumferences of limbs, thicknesses of skin-folds and body fat as well as training variables such as volume and speed in running training were related to marathon race time using bi- and multi-variate analysis in 29 female runners. Results The marathoners completed the marathon distance within 251 (26) min, running at a speed of 10.2 (1.1) km/h. Body mass (r=0.37), body mass index (r=0.46), the circumferences of thigh (r=0.51) and calf (r=0.41), the skin-fold thicknesses of front thigh (r=0.38) and of medial calf (r=0.40), the sum of eight skin-folds (r=0.44) and body fat percentage (r=0.41) were related to marathon race time. For the variables of training, maximal distance ran per week (r=− 0.38), number of running training sessions per week (r=− 0.46) and the speed of the training sessions (r= − 0.60) were related to marathon race time. In the multi-variate analysis, the circumference of calf (P=0.02) and the speed of the training sessions (P=0.0014) were related to marathon race time. Marathon race time might be partially (r 2=0.50) predicted by the following equation: Race time (min)=184.4 + 5.0 x (circumference calf, cm) –11.9 x (speed in running during training, km/h) for recreational female marathoners. Conclusions Variables of both anthropometry and training were related to marathon race time in recreational female marathoners and cannot be reduced to one single predictor variable. For practical applications, a low circumference of calf and a high running speed in training are associated with a fast marathon race time in recreational female runners. PMID:22942994

Predictor variables for marathon race time in recreational female runners.

PubMed

Schmid, Wiebke; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald

2012-06-01

We intended to determine predictor variables of anthropometry and training for marathon race time in recreational female runners in order to predict marathon race time for future novice female runners. Anthropometric characteristics such as body mass, body height, body mass index, circumferences of limbs, thicknesses of skin-folds and body fat as well as training variables such as volume and speed in running training were related to marathon race time using bi- and multi-variate analysis in 29 female runners. The marathoners completed the marathon distance within 251 (26) min, running at a speed of 10.2 (1.1) km/h. Body mass (r=0.37), body mass index (r=0.46), the circumferences of thigh (r=0.51) and calf (r=0.41), the skin-fold thicknesses of front thigh (r=0.38) and of medial calf (r=0.40), the sum of eight skin-folds (r=0.44) and body fat percentage (r=0.41) were related to marathon race time. For the variables of training, maximal distance ran per week (r=- 0.38), number of running training sessions per week (r=- 0.46) and the speed of the training sessions (r= - 0.60) were related to marathon race time. In the multi-variate analysis, the circumference of calf (P=0.02) and the speed of the training sessions (P=0.0014) were related to marathon race time. Marathon race time might be partially (r(2)=0.50) predicted by the following equation: Race time (min)=184.4 + 5.0 x (circumference calf, cm) -11.9 x (speed in running during training, km/h) for recreational female marathoners. Variables of both anthropometry and training were related to marathon race time in recreational female marathoners and cannot be reduced to one single predictor variable. For practical applications, a low circumference of calf and a high running speed in training are associated with a fast marathon race time in recreational female runners.

Rainfall Results of the Florida Area Cumulus Experiment, 1970-76.

NASA Astrophysics Data System (ADS)

Woodley, William L.; Jordan, Jill; Barnston, Anthony; Simpson, Joanne; Biondini, Ron; Flueck, John

1982-02-01

The Florida Area Cumulus Experiment of 1970-76 (FACE-1) is a single-area, randomized, exploratory experiment to determine whether seeding cumuli for dynamic effects (dynamic seeding) can be used to augment convective rainfall over a substantial target area (1.3 × 104 km2) in south Florida. Rainfall is estimated using S-band radar observations after adjustment by raingages. The two primary response variables are rain volumes in the total target (TT) and in the floating target (FT), the most intensely treated portion of the target. The experimental unit is the day and the main observational period is the 6 h after initiation of treatment (silver iodide flares on seed days and either no flares or placebos on control days). Analyses without predictors suggest apparent increases in both the location (means and medians) and the dispersion (standard deviation and interquartile range) characteristics of rainfall due to seeding in the FT and TT variables with substantial statistical support for the FT results and lesser statistical support for the TT results. Analyses of covariance using meteorologically meaningful predictor variables suggest a somewhat larger effect of seeding with stronger statistical support. These results are interpreted in terms of the FACE conceptual model.

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…

Predicting quality of work life on nurses' intention to leave.

PubMed

Lee, Ya-Wen; Dai, Yu-Tzu; Park, Chang-Gi; McCreary, Linda L

2013-06-01

The purpose of this study was to explore the relationship between quality of work life (QWL) and nurses' intention to leave their organization (ITLorg). A descriptive cross-sectional survey design was conducted using purposive sampling of 1,283 nurses at seven hospitals in Taiwan. Data were collected from March to June 2012. Three questionnaires, including the Chinese version of the Quality of Nursing Work Life scale (C-QNWL), a questionnaire of intention to leave the organization, and a demographic questionnaire, with two informed consent forms were delivered to the nurses at their workplaces. Descriptive data, Pearson's correlations, and the ordinal regression model were analyzed. Over half (52.5%) of nurses had ITLorg. Seven QWL dimensions were significantly negatively correlated with ITLorg (r = -0.17 to -0.37, p < .01). Significant predictors (p < .05) of ITLorg (the pseudo R(2) = 0.282) were being single, having a diploma or lower educational level, working in a nonteaching hospital. Four of the QWL dimensions--supportive milieu with job security and professional recognition, work arrangement and workload, work or home life balance, and nursing staffing and patient care--were also predictors of ITLorg. Three QWL dimensions were not predictors of ITLorg. This study showed that individual-related variables (being single, having a diploma or lower educational level), a work-related variable (working at a nonteaching hospital), and the four QWL dimensions play a significant role in nurses' ITLorg. After the QWL dimensions were added to the regression, the variance explained by the model more than doubled. To reduce nurses' ITLorg, nursing administrators may offer more focused interventions to improve the supportive milieu with job security and professional recognition, work arrangement and workload, work or home life balance, and nursing staffing and patient care. © 2013 Sigma Theta Tau International.

Recurrent transient ischaemic attack and early risk of stroke: data from the PROMAPA study.

PubMed

Purroy, Francisco; Jiménez Caballero, Pedro Enrique; Gorospe, Arantza; Torres, María José; Alvarez-Sabin, José; Santamarina, Estevo; Martínez-Sánchez, Patricia; Cánovas, David; Freijo, María José; Egido, Jose Antonio; Ramírez-Moreno, Jose M; Alonso-Arias, Arantza; Rodríguez-Campello, Ana; Casado, Ignacio; Delgado-Mederos, Raquel; Martí-Fàbregas, Joan; Fuentes, Blanca; Silva, Yolanda; Quesada, Helena; Cardona, Pere; Morales, Ana; de la Ossa, Natalia Pérez; García-Pastor, Antonio; Arenillas, Juan F; Segura, Tomas; Jiménez, Carmen; Masjuán, Jaime

2013-06-01

Many guidelines recommend urgent intervention for patients with two or more transient ischaemic attacks (TIAs) within 7 days (multiple TIAs) to reduce the early risk of stroke. To determine whether all patients with multiple TIAs have the same high early risk of stroke. Between April 2008 and December 2009, we included 1255 consecutive patients with a TIA from 30 Spanish stroke centres (PROMAPA study). We prospectively recorded clinical characteristics. We also determined the short-term risk of stroke (at 7 and 90 days). Aetiology was categorised using the TOAST (Trial of Org 10172 in Acute Stroke Treatment) classification. Clinical variables and extracranial vascular imaging were available and assessed in 1137/1255 (90.6%) patients. 7-Day and 90-day stroke risk were 2.6% and 3.8%, respectively. Large-artery atherosclerosis (LAA) was confirmed in 190 (16.7%) patients. Multiple TIAs were seen in 274 (24.1%) patients. Duration <1 h (OR=2.97, 95% CI 2.20 to 4.01, p<0.001), LAA (OR=1.92, 95% CI 1.35 to 2.72, p<0.001) and motor weakness (OR=1.37, 95% CI 1.03 to 1.81, p=0.031) were independent predictors of multiple TIAs. The subsequent risk of stroke in these patients at 7 and 90 days was significantly higher than the risk after a single TIA (5.9% vs 1.5%, p<0.001 and 6.8% vs 3.0%, respectively). In the logistic regression model, among patients with multiple TIAs, no variables remained as independent predictors of stroke recurrence. According to our results, multiple TIAs within 7 days are associated with a greater subsequent risk of stroke than after a single TIA. Nevertheless, we found no independent predictor of stroke recurrence among these patients.

Outcome definitions and clinical predictors influence pharmacogenetic associations between HTR3A gene polymorphisms and response to clozapine in patients with schizophrenia.

PubMed

Rajkumar, A P; Poonkuzhali, B; Kuruvilla, A; Srivastava, A; Jacob, M; Jacob, K S

2012-12-01

Pharmacogenetics of schizophrenia has not yet delivered anticipated clinical dividends. Clinical heterogeneity of schizophrenia contributes to the poor replication of the findings of pharmacogenetic association studies. Functionally important HTR3A gene single-nucleotide polymorphisms (SNPs) were reported to be associated with response to clozapine. The aim of this study was to investigate how the association between HTR3A gene SNP and response to clozapine is influenced by various clinical predictors and by differing outcome definitions in patients with treatment-resistant schizophrenia (TRS). We recruited 101 consecutive patients with TRS, on stable doses of clozapine, and evaluated their HTR3A gene SNP (rs1062613 and rs2276302), psychopathology, and serum clozapine levels. We assessed their socio-demographic and clinical profiles, premorbid adjustment, traumatic events, cognition, and disability using standard assessment schedules. We evaluated their response to clozapine, by employing six differing outcome definitions. We employed appropriate multivariate statistics to calculate allelic and genotypic association, accounting for the effects of various clinical variables. T allele of rs1062613 and G allele of rs2276302 were significantly associated with good clinical response to clozapine (p = 0.02). However, varying outcome definitions make these associations inconsistent. rs1062613 and rs2276302 could explain only 13.8 % variability in the responses to clozapine, while combined clinical predictors and HTR3A pharmacogenetic association model could explain 38 % variability. We demonstrated that the results of pharmacogenetic studies in schizophrenia depend heavily on their outcome definitions and that combined clinical and pharmacogenetic models have better predictive values. Future pharmacogenetic studies should employ multiple outcome definitions and should evaluate associated clinical variables.

Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

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…

Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers.

PubMed

Vaegter, Katarina Kebbon; Lakic, Tatevik Ghukasyan; Olovsson, Matts; Berglund, Lars; Brodin, Thomas; Holte, Jan

2017-03-01

To construct a prediction model for live birth after in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2 days of embryo culture. Prospective observational cohort study. University-affiliated private infertility center. SET in 8,451 IVF/ICSI treatments in 5,699 unselected consecutive couples during 1999-2014. A total of 100 basal patient characteristics and treatment data were analyzed for associations with live birth after IVF/ICSI (adjusted for repeated treatments) and subsequently combined for prediction model construction. Live birth rate (LBR) and performance of live birth prediction model. Embryo score, treatment history, ovarian sensitivity index (OSI; number of oocytes/total dose of FSH administered), female age, infertility cause, endometrial thickness, and female height were all independent predictors of live birth. A prediction model (training data set; n = 5,722) based on these variables showed moderate discrimination, but predicted LBR with high accuracy in subgroups of patients, with LBR estimates ranging from <10% to >40%. Outcomes were similar in an internal validation data set (n = 2,460). Based on 100 variables prospectively recorded during a 15-year period, a model for live birth prediction after strict SET was constructed and showed excellent calibration in internal validation. For the first time, female height qualified as a predictor of live birth after IVF/ICSI. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2010-05-01

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

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

USGS Publications Warehouse

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

2007-01-01

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

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

PubMed Central

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

2007-01-01

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

Comparing initial diagnostic excision biopsy of cutaneous malignant melanoma in primary versus secondary care: A study of Irish National data.

PubMed

Doherty, Sarah M; Jackman, Louise M; Kirwan, John F; Dunne, Deirdre; O'Connor, Kieran G; Rouse, John M

2016-12-01

The incidence of melanoma is rising worldwide. Current Irish guidelines from the National Cancer Control Programme state suspicious pigmented lesions should not be removed in primary care. There are conflicting guidelines and research advising who should remove possible melanomas. To determine whether initial diagnostic excision biopsy of cutaneous malignant melanoma in primary versus secondary care leads to poorer survival. Analysis of data comprising 7116 cases of cutaneous malignant melanoma from the National Cancer Registry Ireland between January 2002 and December 2011. Single predictor variables were examined by the chi-square or Mann-Whitney U test. The effects of single predictor variables on survival were examined by Cox proportionate hazards modelling and a multivariate Cox model of survival based on excision in a non-hospital setting versus hospital setting was derived with adjusted and unadjusted hazard ratios. Over a 10-year period 8.5% of melanomas in Ireland were removed in a non-hospital setting. When comparing melanoma death between the hospital and non-hospital groups, the adjusted hazard ratio was 1.56 (95%CI: 1.08-2.26); (P = .02), indicating a non-inferior outcome for the melanoma cases initially treated in the non-hospital group, after adjustment for significant covariates. This study suggests that initial excision biopsy carried out in general practice does not lead to a poorer outcome. [Box: see text].

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.

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.

IRB Process Improvements: A Machine Learning Analysis.

PubMed

Shoenbill, Kimberly; Song, Yiqiang; Cobb, Nichelle L; Drezner, Marc K; Mendonca, Eneida A

2017-06-01

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

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.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

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

Changes in Situational and Dispositional Factors as Predictors of Job Satisfaction

ERIC Educational Resources Information Center

Keller, Anita C.; Semmer, Norbert K.

2013-01-01

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

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.

Intraindividual Variability in Executive Functions but Not Speed of Processing or Conflict Resolution Predicts Performance Differences in Gait Speed in Older Adults

PubMed Central

Mahoney, Jeannette; Verghese, Joe

2014-01-01

Background. The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Methods. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19–38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = −.606; 95% CI = −1.11 to −.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = −.901; 95% CI = −1.557 to −.245). Conclusion. Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. PMID:24285744

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

PubMed

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

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.

Pouch dilatation following laparoscopic adjustable gastric banding: psychobehavioral factors (can psychiatrists predict pouch dilatation?).

PubMed

Poole, Norman; Al Atar, Ashraf; Bidlake, Louise; Fienness, Alberic; McCluskey, Sara; Nussey, S; Bano, Gal; Morgan, John

2004-01-01

Laparoscopic adjustable gastric banding is increasingly being performed in morbidly obese individuals for weight loss. Some patients develop pouch dilatation as a postoperative complication that limits the utility of the procedure. Surgical variables are poor predictors of this complication. 5 patients from a series of 157 who underwent LAGB at a single center developed the condition. Psychiatric and surgical case-notes were analyzed retrospectively for the presence of operationally defined psychiatric disorders and compared to 10 controls from the same population. Cases were significantly more likely to have past or current binge eating, emotionally triggered eating with reduced awareness of the link, a history of affective disorder, reduced sexual functioning and successful preoperative weight loss. No difference between groups was observed for compliance with orlistat, childhood sexual abuse, relationships with parents, history of bulimia nervosa, rate of band inflation or preoperative BMI. Psychological factors may be better predictors of pouch dilatation than biomedical variables. Disordered eating can be an attempt to modulate negative emotions. Pouch dilatation may be a consequence of this eating behavior.

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

An assessment of morphometric indices, blood chemistry variables and an energy meter as indicators of the whole body lipid content in Micropterus dolomieu, Sander vitreus and Ictalurus punctatus

USGS Publications Warehouse

Mesa, Matthew G.; Rose, Brien P.

2015-01-01

The effectiveness of several non-lethal techniques as indicators of total lipid content in smallmouth bass Micropterus dolomieu, walleye Sander vitreus and channel catfish Ictalurus punctatus was investigated. The techniques included (1) the Fulton and relative condition factors, (2) relative mass, (3) plasma indicators of nutritional status (alkaline phosphatase, calcium, cholesterol, protein, triglycerides and glucose) and (4) readings from a hand-held, microwave energy meter. Although simple linear regression analysis showed that lipid content was significantly correlated with several predictor variables in each species, the r2 values for the relations ranged from 0·17 to 0·50 and no single approach was consistent for all species. Only one model, between energy-meter readings and lipid content in I. punctatus, had an r2 value (0·83) high enough to justify using it as a predictive tool. Results indicate that no single variable was an accurate and reliable indicator of whole body lipid content in these fishes, except the energy meter for I. punctatus.

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.

Predictors of condom use and refusal among the population of Free State province in South Africa

PubMed Central

2012-01-01

Background This study investigated the extent and predictors of condom use and condom refusal in the Free State province in South Africa. Methods Through a household survey conducted in the Free Sate province of South Africa, 5,837 adults were interviewed. Univariate and multivariate survey logistic regressions and classification trees (CT) were used for analysing two response variables ‘ever used condom’ and ‘ever refused condom’. Results Eighty-three per cent of the respondents had ever used condoms, of which 38% always used them; 61% used them during the last sexual intercourse and 9% had ever refused to use them. The univariate logistic regression models and CT analysis indicated that a strong predictor of condom use was its perceived need. In the CT analysis, this variable was followed in importance by ‘knowledge of correct use of condom’, condom availability, young age, being single and higher education. ‘Perceived need’ for condoms did not remain significant in the multivariate analysis after controlling for other variables. The strongest predictor of condom refusal, as shown by the CT, was shame associated with condoms followed by the presence of sexual risk behaviour, knowing one’s HIV status, older age and lacking knowledge of condoms (i.e., ability to prevent sexually transmitted diseases and pregnancy, availability, correct and consistent use and existence of female condoms). In the multivariate logistic regression, age was not significant for condom refusal while affordability and perceived need were additional significant variables. Conclusions The use of complementary modelling techniques such as CT in addition to logistic regressions adds to a better understanding of condom use and refusal. Further improvement in correct and consistent use of condoms will require targeted interventions. In addition to existing social marketing campaigns, tailored approaches should focus on establishing the perceived need for condom-use and improving skills for correct use. They should also incorporate interventions to reduce the shame associated with condoms and individual counselling of those likely to refuse condoms. PMID:22639964

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…

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

PubMed

Richardson, Alice M; Lidbury, Brett A

2017-08-14

Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Random forests (RFs) for predictor variable selection, and data reshaping to overcome a large imbalance of negative to positive test results in relation to HBV and HCV immunoassay results, are examined. The methodology is illustrated using data from ACT Pathology (Canberra, Australia), consisting of laboratory test records from 18,625 individuals who underwent hepatitis virus testing over the decade from 1997 to 2007. Overall, the prediction of HCV test results by immunoassay was more accurate than for HBV immunoassay results associated with identical routine pathology predictor variable data. HBV and HCV negative results were vastly in excess of positive results, so three approaches to handling the negative/positive data imbalance were compared. Generating datasets by the Synthetic Minority Oversampling Technique (SMOTE) resulted in significantly more accurate prediction than single downsizing or multiple downsizing (MDS) of the dataset. For downsized data sets, applying a RF for predictor variable selection had a small effect on the performance, which varied depending on the virus. For SMOTE, a RF had a negative effect on performance. An analysis of variance of the performance across settings supports these findings. Finally, age and assay results for alanine aminotransferase (ALT), sodium for HBV and urea for HCV were found to have a significant impact upon laboratory diagnosis of HBV or HCV infection using an optimised SVM model. Laboratories looking to include machine learning via SVM as part of their decision support need to be aware that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied. This awareness should lead to careful use of existing machine learning methods, thus improving the quality of laboratory diagnosis.

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

PubMed

Alverson, Charlotte Y; Yamamoto, Scott H

2018-01-01

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

Modeling Predictors of Duties Not Including Flying Status.

PubMed

Tvaryanas, Anthony P; Griffith, Converse

2018-01-01

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

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

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

2012-01-01

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

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.

Predictors of clinical outcome following lumbar disc surgery: the value of historical, physical examination, and muscle function variables.

PubMed

Hebert, Jeffrey J; Fritz, Julie M; Koppenhaver, Shane L; Thackeray, Anne; Kjaer, Per

2016-01-01

Explore the relationships between preoperative findings and clinical outcome following lumbar disc surgery, and investigate the prognostic value of physical examination findings after accounting for information acquired from the clinical history. We recruited 55 adult patients scheduled for first time, single-level lumbar discectomy. Participants underwent a standardized preoperative evaluation including real-time ultrasound imaging assessment of lumbar multifidus function, and an 8-week postoperative rehabilitation programme. Clinical outcome was defined by change in disability, and leg and low back pain (LBP) intensity at 10 weeks. Linear regression models were used to identify univariate and multivariate predictors of outcome. Univariate predictors of better outcome varied depending on the outcome measure. Clinical history predictors included a greater proportion of leg pain to LBP, pain medication use, greater time to surgery, and no history of previous physical or injection therapy. Physical examination predictors were a positive straight or cross straight leg raise test, diminished lower extremity strength, sensation or reflexes, and the presence of postural abnormality or pain peripheralization. Preoperative pain peripheralization remained a significant predictor of improved disability (p = 0.04) and LBP (p = 0.02) after accounting for information from the clinical history. Preoperative lumbar multifidus function was not associated with clinical outcome. Information gleaned from the clinical history and physical examination helps to identify patients more likely to succeed with lumbar disc surgery. While this study helps to inform clinical practice, additional research confirming these results is required prior to confident clinical implementation.

Resentment of paternalism as system change sentiment: hostile sexism toward men and actual behavior in the 2008 U.S. presidential election.

PubMed

Tate, Charlotte Chuck

2014-01-01

Taking inspiration from Glick and colleagues (2004), this study tested the idea that resentment of paternalism (which is part of the hostile sexism toward men construct) might approximate desire for system change by correlating this variable with actual behavior associated with system change in a single culture. Specifically, voting behavior in the 2008 U.S. presidential election was predicted from political party affiliation, measures of hostile and benevolent sexism toward both women and men, and egalitarian racial attitudes using a U.S. college student sample. Results indicated that the only significant predictors of voting behavior were political party affiliation, resentment of paternalism, and egalitarian racial attitudes. Higher levels of resentment of paternalism were in fact associated with voting for the ticket that represented system change-holding the other predictors constant.

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…

A latent class distance association model for cross-classified data with a categorical response variable.

PubMed

Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

2014-11-01

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

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.

Efficient conservative ADER schemes based on WENO reconstruction and space-time predictor in primitive variables

NASA Astrophysics Data System (ADS)

Zanotti, Olindo; Dumbser, Michael

2016-01-01

We present a new version of conservative ADER-WENO finite volume schemes, in which both the high order spatial reconstruction as well as the time evolution of the reconstruction polynomials in the local space-time predictor stage are performed in primitive variables, rather than in conserved ones. To obtain a conservative method, the underlying finite volume scheme is still written in terms of the cell averages of the conserved quantities. Therefore, our new approach performs the spatial WENO reconstruction twice: the first WENO reconstruction is carried out on the known cell averages of the conservative variables. The WENO polynomials are then used at the cell centers to compute point values of the conserved variables, which are subsequently converted into point values of the primitive variables. This is the only place where the conversion from conservative to primitive variables is needed in the new scheme. Then, a second WENO reconstruction is performed on the point values of the primitive variables to obtain piecewise high order reconstruction polynomials of the primitive variables. The reconstruction polynomials are subsequently evolved in time with a novel space-time finite element predictor that is directly applied to the governing PDE written in primitive form. The resulting space-time polynomials of the primitive variables can then be directly used as input for the numerical fluxes at the cell boundaries in the underlying conservative finite volume scheme. Hence, the number of necessary conversions from the conserved to the primitive variables is reduced to just one single conversion at each cell center. We have verified the validity of the new approach over a wide range of hyperbolic systems, including the classical Euler equations of gas dynamics, the special relativistic hydrodynamics (RHD) and ideal magnetohydrodynamics (RMHD) equations, as well as the Baer-Nunziato model for compressible two-phase flows. In all cases we have noticed that the new ADER schemes provide less oscillatory solutions when compared to ADER finite volume schemes based on the reconstruction in conserved variables, especially for the RMHD and the Baer-Nunziato equations. For the RHD and RMHD equations, the overall accuracy is improved and the CPU time is reduced by about 25 %. Because of its increased accuracy and due to the reduced computational cost, we recommend to use this version of ADER as the standard one in the relativistic framework. At the end of the paper, the new approach has also been extended to ADER-DG schemes on space-time adaptive grids (AMR).

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.

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.

Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss

PubMed Central

Bermudez, Eduardo B.; Klerman, Elizabeth B.; Czeisler, Charles A.; Cohen, Daniel A.; Wyatt, James K.; Phillips, Andrew J. K.

2016-01-01

Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss. PMID:27019198

A novel and simple test of gait adaptability predicts gold standard measures of functional mobility in stroke survivors.

PubMed

Hollands, K L; Pelton, T A; van der Veen, S; Alharbi, S; Hollands, M A

2016-01-01

Although there is evidence that stroke survivors have reduced gait adaptability, the underlying mechanisms and the relationship to functional recovery are largely unknown. We explored the relationships between walking adaptability and clinical measures of balance, motor recovery and functional ability in stroke survivors. Stroke survivors (n=42) stepped to targets, on a 6m walkway, placed to elicit step lengthening, shortening and narrowing on paretic and non-paretic sides. The number of targets missed during six walks and target stepping speed was recorded. Fugl-Meyer (FM), Berg Balance Scale (BBS), self-selected walking speed (SWWS) and single support (SS) and step length (SL) symmetry (using GaitRite when not walking to targets) were also assessed. Stepwise multiple-linear regression was used to model the relationships between: total targets missed, number missed with paretic and non-paretic legs, target stepping speed, and each clinical measure. Regression revealed a significant model for each outcome variable that included only one independent variable. Targets missed by the paretic limb, was a significant predictor of FM (F(1,40)=6.54, p=0.014,). Speed of target stepping was a significant predictor of each of BBS (F(1,40)=26.36, p<0.0001), SSWS (F(1,40)=37.00, p<0.0001). No variables were significant predictors of SL or SS asymmetry. Speed of target stepping was significantly predictive of BBS and SSWS and paretic targets missed predicted FM, suggesting that fast target stepping requires good balance and accurate stepping demands good paretic leg function. The relationships between these parameters indicate gait adaptability is a clinically meaningful target for measurement and treatment of functionally adaptive walking ability in stroke survivors. Copyright © 2015 Elsevier B.V. All rights reserved.

Intraindividual variability in executive functions but not speed of processing or conflict resolution predicts performance differences in gait speed in older adults.

PubMed

Holtzer, Roee; Mahoney, Jeannette; Verghese, Joe

2014-08-01

The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19-38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = -.606; 95% CI = -1.11 to -.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = -.901; 95% CI = -1.557 to -.245). Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Impact of Tricuspid Regurgitation on the Success of Atrioventricular Node Ablation for Rate Control in Patients With Atrial Fibrillation: The Node Blast Study.

PubMed

Reddy, Yeruva Madhu; Gunda, Sampath; Vallakati, Ajay; Kanmanthareddy, Arun; Pillarisetti, Jayasree; Atkins, Donita; Bommana, Sudharani; Emert, Martin P; Pimentel, Rhea; Dendi, Raghuveer; Berenbom, Loren D; Lakkireddy, Dhanunjaya

2015-09-15

Atrioventricular node (AVN) ablation is an effective treatment for symptomatic patients with atrial arrhythmias who are refractory to rhythm and rate control strategies where optimal ventricular rate control is desired. There are limited data on the predictors of failure of AVN ablation. Our objective was to identify the predictors of failure of AVN ablation. This is an observational single-center study of consecutive patients who underwent AVN ablation in a large academic center. Baseline characteristics, procedural variables, and outcomes of AVN ablation were collected. AVN "ablation failure" was defined as resumption of AVN conduction resulting in recurrence of either rapid ventricular response or suboptimal biventricular pacing. A total of 247 patients drug refractory AF who underwent AVN ablation at our center with a mean age of 71 ± 12 years with 46% being males were included. Ablation failure was seen in 11 (4.5%) patients. There were no statistical differences between patients with "ablation failure" versus "ablation success" in any of the baseline clinical variables. Patients with moderate-to-severe tricuspid regurgitation (TR) were much more likely to have ablation failure than those with ablation success (8 [73%] vs 65 [27%]; p = 0.003). All 11 patients with ablation failure had a successful redo procedure, 9 with right and 2 with the left sided approach. On multivariate analysis, presence of moderate-to-severe TR was found to be the only predictor of failure of AVN ablation (odds ratio 9.1, confidence interval 1.99 to 42.22, p = 0.004). In conclusion, moderate-to-severe TR is a strong and independent predictor of failure of AVN ablation. Copyright © 2015 Elsevier Inc. All rights reserved.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a Predictor of Outcome in Head-and-Neck Squamous Cell Carcinoma Patients With Nodal Metastases

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

Shukla-Dave, Amita, E-mail: davea@mskcc.org; Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY; Lee, Nancy Y.

2012-04-01

Purpose: Dynamic contrast-enhanced MRI (DCE-MRI) can provide information regarding tumor perfusion and permeability and has shown prognostic value in certain tumors types. The goal of this study was to assess the prognostic value of pretreatment DCE-MRI in head and neck squamous cell carcinoma (HNSCC) patients with nodal disease undergoing chemoradiation therapy or surgery. Methods and Materials: Seventy-four patients with histologically proven squamous cell carcinoma and neck nodal metastases were eligible for the study. Pretreatment DCE-MRI was performed on a 1.5T MRI. Clinical follow-up was a minimum of 12 months. DCE-MRI data were analyzed using the Tofts model. DCE-MRI parameters weremore » related to treatment outcome (progression-free survival [PFS] and overall survival [OS]). Patients were grouped as no evidence of disease (NED), alive with disease (AWD), dead with disease (DOD), or dead of other causes (DOC). Prognostic significance was assessed using the log-rank test for single variables and Cox proportional hazards regression for combinations of variables. Results: At last clinical follow-up, for Stage III, all 12 patients were NED. For Stage IV, 43 patients were NED, 4 were AWD, 11 were DOD, and 4 were DOC. K{sup trans} is volume transfer constant. In a stepwise Cox regression, skewness of K{sup trans} (volume transfer constant) was the strongest predictor for Stage IV patients (PFS and OS: p <0.001). Conclusion: Our study shows that skewness of K{sup trans} was the strongest predictor of PFS and OS in Stage IV HNSCC patients with nodal disease. This study suggests an important role for pretreatment DCE-MRI parameter K{sup trans} as a predictor of outcome in these patients.« less

Computed tomographic colonography for colorectal cancer screening: risk factors for the detection of advanced neoplasia.

PubMed

Hassan, Cesare; Pooler, B Dustin; Kim, David H; Rinaldi, Antonio; Repici, Alessandro; Pickhardt, Perry J

2013-07-15

The objective of this study was to determine whether age, sex, a positive family history of colorectal cancer, and body mass index (BMI) are important predictors of advanced neoplasia in the setting of screening computed tomographic colonography (CTC). Consecutive patients who were referred for first-time screening CTC from 2004 to 2011 at a single medical center were enrolled. Results at pathology were recorded for all patients who underwent polypectomy. Logistic regression was used to identify significant predictor variables for advanced neoplasia (any adenoma ≥ 10 mm or with villous component, high-grade dysplasia, or adenocarcinoma). Odds ratios (ORs) were used to express associations between the study variables (age, sex, BMI, and a positive family history of colorectal cancer) and advanced neoplasia. In total, 7620 patients underwent CTC screening. Of these, 276 patients (3.6%; 95% confidence interval [CI], 3.2%-4.1%) ultimately were diagnosed with advanced neoplasia. At multivariate analysis, age (mean OR per 10-year increase, 1.8; 95% CI, 1.6-2.0) and being a man (OR, 1.7; 95% CI, 1.3-2.2) were independent predictors of advanced neoplasia, whereas BMI and a positive family history of colorectal cancer were not. The number needed to screen to detect 1 case of advanced neoplasia varied from 51 among women aged ≤ 55 years to 10 among men aged >65 years. The number of post-CTC colonoscopies needed to detect 1 case of advanced neoplasia varied from 2 to 4. Age and sex were identified as important independent predictors of advanced neoplasia risk in individuals undergoing screening CTC, whereas BMI and a positive family history of colorectal cancer were not. These results have implications for appropriate patient selection. © 2013 American Cancer Society.

[Nasal flaring as a predictor of mortality in patients with severe dyspnea].

PubMed

Zorrilla Riveiro, José Gregorio; Arnau Bartés, Anna; García Pérez, Dolors; Rafat Sellarés, Ramón; Mas Serra, Arantxa; Fernández Fernández, Rafael

2015-02-01

To determine whether the presence of nasal flaring is a clinical sign of severity and a predictor of hospital mortality in emergency patients with dyspnea. Prospective, observational, single-center study. We enrolled patients older than 15 years of age who required attention for dyspnea categorized as level II or III emergencies according to the Andorran Medical Triage system. Two observers evaluated the presence of nasal flaring. We recorded demographic and clinical variables, including respiratory effort, vital signs, arterial blood gases, and clinical course (hospital admission and mortality). Bivariable analysis was performed and multivariable logistic regression models were constructed. We enrolled 246 patients with a mean (SD) age of 77 (13) years; 52% were female. Nasal flaring was present in 19.5%. Patients with nasal flaring had triage levels indicating greater severity and they had more severe tachypnea, worse oxygenation, and greater acidosis and hypercapnia. Bivariable analysis detected that the following variables were associated with mortality: age (odds ratio [OR], 1.05; 95% CI, 1.01-1.10), prehospital care from the emergency medical service (OR, 3.97; 95% CI, 1.39-11.39), triage level II (OR, 4.19; 95% CI, 1.63-10.78), signs of respiratory effort such as nasal flaring (OR, 3.79; 95% CI, 1.65-8.69), presence of acidosis (OR, 7.09; 95% CI, 2.97-16.94), and hypercapnia (OR, 2.67; 95% CI, 1,11-6,45). The factors that remained independent predictors of mortality in the multivariable analysis were age, severity (triage level), and nasal flaring. In patients requiring emergency care for dyspnea, nasal flaring is a clinical sign of severity and a predictor of mortality.

Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.

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…

Predictors of Long-Term Healthy Arterial Aging: Coronary Artery Calcium Nondevelopment in the MESA Study.

PubMed

Whelton, Seamus P; Silverman, Michael G; McEvoy, John W; Budoff, Matthew J; Blankstein, Ron; Eng, John; Blumenthal, Roger S; Szklo, Moyses; Nasir, Khurram; Blaha, Michael J

2015-12-01

This study sought to determine the predictors of healthy arterial aging. Long-term nondevelopment of coronary artery calcification (persistent CAC = 0) is a marker of healthy arterial aging. The predictors of this phenotype are not known. We analyzed 1,850 participants from MESA (Multi-Ethnic Study of Atherosclerosis) with baseline CAC = 0 who underwent a follow-up CAC scan at visit 5 (median 9.6 years after baseline). We examined the proportion with persistent CAC = 0 and calculated multivariable relative risks and area under the receiver operating characteristic curve for prediction of this healthy arterial aging phenotype. We found that 55% of participants (n = 1,000) had persistent CAC = 0, and these individuals were significantly more likely to be younger, female, and have fewer traditional risk factors (RF). Participants with an ASCVD (Atherosclerotic Cardiovascular Disease Risk Score) risk score <2.5% were 53% more likely to have healthy arterial aging than were participants with an ASCVD score ≥7.5%. There was no significant association between the Healthy Lifestyle variables (body mass index, physical activity, Mediterranean diet, and never smoking) and persistent CAC = 0. The area under the receiver operating characteristic curve incorporating age, sex, and ethnicity was 0.65, indicating fair to poor discrimination. No single traditional RF or combination of other risk factors increased the area under the receiver operating characteristic curve by more than 0.05. Whereas participants free of traditional cardiovascular disease RF were significantly more likely to have persistent CAC = 0, there was no single RF or specific low-risk RF phenotype that markedly improved the discrimination of persistent CAC = 0 over demographic variables. Therefore, we conclude that healthy arterial aging may be predominantly influenced by the long-term maintenance of a low cardiovascular disease risk profile or yet to be determined genetic factors rather than the absence of any specific RF cluster identified in late adulthood. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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.

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

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

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

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

PubMed Central

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

2011-01-01

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

Stone Attenuation Values Measured by Average Hounsfield Units and Stone Volume as Predictors of Total Laser Energy Required During Ureteroscopic Lithotripsy Using Holmium:Yttrium-Aluminum-Garnet Lasers.

PubMed

Ofude, Mitsuo; Shima, Takashi; Yotsuyanagi, Satoshi; Ikeda, Daisuke

2017-04-01

To evaluate the predictors of the total laser energy (TLE) required during ureteroscopic lithotripsy (URS) using the holmium:yttrium-aluminum-garnet (Ho:YAG) laser for a single ureteral stone. We retrospectively analyzed the data of 93 URS procedures performed for a single ureteral stone in our institution from November 2011 to September 2015. We evaluated the association between TLE and preoperative clinical data, such as age, sex, body mass index, and noncontrast computed tomographic findings, including stone laterality, location, maximum diameter, volume, stone attenuation values measured using average Hounsfield units (HUs), and presence of secondary signs (severe hydronephrosis, tissue rim sign, and perinephric stranding). The mean maximum stone diameter, volume, and average HUs were 9.2 ± 3.8 mm, 283.2 ± 341.4 mm 3 , and 863 ± 297, respectively. The mean TLE and operative time were 2.93 ± 3.27 kJ and 59.1 ± 28.1 minutes, respectively. Maximum stone diameter, volume, average HUs, severe hydronephrosis, and tissue rim sign were significantly correlated with TLE (Spearman's rho analysis). Stepwise multiple linear regression analysis defining stone volume, average HUs, severe hydronephrosis, and tissue rim sign as explanatory variables showed that stone volume and average HUs were significant predictors of TLE (standardized coefficients of 0.565 and 0.320, respectively; adjusted R 2  = 0.55, F = 54.7, P <.001). Stone attenuation values measured by average HUs and stone volume were strong predictors of TLE during URS using Ho:YAG laser procedures. Copyright © 2016 Elsevier Inc. All rights reserved.

Birth Weight Is Associated With the IGF-1 Response to GH in Children: Programming of the Anabolic Action of GH?

PubMed

Donzeau, Aurélie; Bouhours-Nouet, Natacha; Fauchard, Mathilde; Decrequy, Anne; Mathieu, Elisabeth; Boux de Casson, Florence; Gascoin, Geraldine; Coutant, Régis

2015-08-01

Intrauterine programming of the somatotropic axis has been hypothesized in cases of intrauterine growth retardation. The objective of the study was to study the effects of birth weight and body composition on GH sensitivity. This was a cross-sectional study with a single GH administration to assess GH sensitivity. The study was conducted at the Department of Pediatric Endocrinology of an academic medical center. One hundred normal short children aged from 4 to 17 years old (44 girls, 56 boys) separated into four groups: early childhood (aged 4-8 y, n = 14), late childhood (aged 9-12 y, pubertal stage 1, n = 30), early puberty (aged 10-15 y, stage 2, n = 32), and midpuberty (aged 12-17 y, stages 3 and 4, n = 24). Serum IGF-1 at baseline and 24 hours after a single administration of GH (2 mg/m(2)) were measured. δIGF-1 significantly increased across the groups (P < .0001) with no gender difference, whereas the percentage of change in IGF-1 was similar (47% ± 32%). Independent predictors of δIGF-1 were birth weight SD score, fat percentage, fasting insulin (all positive predictors), and free fatty acids (negative predictor), with age, puberty, and baseline IGF-1 as adjusting variables (multiple R = 0.73, P < .0001). Independent predictors of the percentage of change in IGF-1 were birth weight SD score, fat percentage, and baseline IGF-1 (multiple R = 0.43, P < .001). This study suggests that in cases of low birth weight, intrauterine programming of GH sensitivity may be an adaptation to an expected poor postnatal nutritional environment, serving to restrict the anabolic action of GH. Conversely, postnatal excess energy stores may promote the anabolic action of GH.

The National Environmental Respiratory Center (NERC) experiment in multi-pollutant air quality health research: III. Components of diesel and gasoline engine exhausts, hardwood smoke and simulated downwind coal emissions driving non-cancer biological responses in rodents.

PubMed

Mauderly, Joe L; Seilkop, Steven K

2014-09-01

An approach to identify causal components of complex air pollution mixtures was explored. Rats and mice were exposed by inhalation 6 h daily for 1 week or 6 months to dilutions of simulated downwind coal emissions, diesel and gasoline exhausts and wood smoke. Organ weights, hematology, serum chemistry, bronchoalveolar lavage, central vascular and respiratory allergic responses were measured. Multiple additive regression tree (MART) analysis of the combined database ranked 45 exposure (predictor) variables for importance to models best fitting 47 significant responses. Single-predictor concentration-response data were examined for evidence of single response functions across all exposure groups. Replication of the responses by the combined influences of the two most important predictors was tested. Statistical power was limited by inclusion of only four mixtures, albeit in multiple concentrations each and with particles removed for some groups. Results gave suggestive or strong evidence of causation of 19 of the 47 responses. The top two predictors of the 19 responses included only 12 organic and 6 inorganic species or classes. An increase in red blood cell count of rats by ammonia and pro-atherosclerotic vascular responses of mice by inorganic gases yielded the strongest evidence for causation and the best opportunity for confirmation. The former was a novel finding; the latter was consistent with other results. The results demonstrated the plausibility of identifying putative causal components of highly complex mixtures, given a database in which the ratios of the components are varied sufficiently and exposures and response measurements are conducted using a consistent protocol.

Predictors of fielding performance in professional baseball players.

PubMed

Mangine, Gerald T; Hoffman, Jay R; Vazquez, Jose; Pichardo, Napoleon; Fragala, Maren S; Stout, Jeffrey R

2013-09-01

The ultimate zone-rating extrapolation (UZR/150) rates fielding performance by runs saved or cost within a zone of responsibility in comparison with the league average (150 games) for a position. Spring-training anthropometric and performance measures have been previously related to hitting performance; however, their relationships with fielding performance measures are unknown. To examine the relationship between anthropometric and performance measurements on fielding performance in professional baseball players. Body mass, lean body mass (LBM), grip strength, 10-yd sprint, proagility, and vertical-jump mean (VJMP) and peak power (VJPP) were collected during spring training over the course of 5 seasons (2007-2011) for professional corner infielders (CI; n = 17, fielding opportunities = 420.7 ± 307.1), middle infielders (MI; n = 14, fielding opportunities = 497.3 ± 259.1), and outfielders (OF; n = 16, fielding opportunities = 227.9 ± 70.9). The relationships between these data and regular-season (100-opportunity minimum) fielding statistics were examined using Pearson correlation coefficients, while stepwise regression identified the single best predictor of UZR/150. Significant correlations (P < .05) were observed between UZR/150 and body mass (r = .364), LBM (r = .396), VJPP (r = .397), and VJMP (r = .405). Of these variables, stepwise regression indicated VJMP (R = .405, SEE = 14.441, P = .005) as the single best predictor for all players, although the addition of proagility performance strengthened (R = .496, SEE = 13.865, P = .002) predictive ability by 8.3%. The best predictor for UZR/150 was body mass for CI (R = .519, SEE = 15.364, P = .033) and MI (R = .672, SEE = 12.331, P = .009), while proagility time was the best predictor for OF (R = .514, SEE = 8.850, P = .042). Spring-training measurements of VJMP and proagility time may predict the defensive run value of a player over the course of a professional baseball season.

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.

The effects of a confidant and a peer group on the well-being of single elders.

PubMed

Gupta, V; Korte, C

1994-01-01

A study of 100 elderly people was carried out to compare the predictions of well-being derived from the confidant model with those derived from the Weiss model. The confidant model predicts that the most important feature of a person's social network for the well-being of that person is whether or not the person has a confidant. The Weiss model states that different persons are needed to fulfill the different needs of the person and in particular that a confidant is important to the need for intimacy and emotional security while a peer group of social friends is needed to fulfill sociability and identity needs. The two models were evaluated by comparing the relative influence of the confidant variable with the peer group variable on subject's well-being. Regression analysis was carried out on the well-being measure using as predictor variables the confidant variable, peer group variable, age, health, and financial status. The confidant and peer group variables were of equal importance to well-being, thus confirming the Weiss model.

Lymph node ratio may predict relapse free survival and overall survival in patients with stage II & III colorectal carcinoma.

PubMed

Zekri, Jamal; Ahmad, Imran; Fawzy, Ehab; Elkhodary, Tawfik R; Al-Gahmi, Aboelkhair; Hassouna, Ashraf; El Sayed, Mohamed E; Ur Rehman, Jalil; Karim, Syed M; Bin Sadiq, Bakr

2015-01-01

Lymph node ratio (LNR) defined as the number of lymph nodes (LNs) involved with metastases divided by number of LNs examined, has been shown to be an independent prognostic factor in breast, stomach and various other solid tumors. Its significance as a prognostic determinant in colorectal cancer (CRC) is still under investigation. This study investigated the prognostic value of LNR in patients with resected CRC. We retrospectively ex- amined 145 patients with stage II & III CRC diagnosed and treated at a single institution during 9 years pe- riod. Patients were grouped according to LNR in three groups. Group 1; LNR < 0.05, Group 2; LNR = 0.05-0.19 & Group 3 > 0.19. Chi square, life table analysis and multivariate Cox regression were used for statistical analysis. On multivariate analysis, number of involved LNs (NILN) (HR = 1.15, 95% CI 1.055-1.245; P = 0.001) and pathological T stage (P = 0.002) were statistically significant predictors of relapse free survival (RFS). LNR as a continuous variable (but not as a categorical variable) was statistically significant predictor of RFS (P = 0.02). LNR was also a statistically significant predictor of overall survival (OS) (P = 0.02). LNR may predict RFS and OS in patients with resected stage II & III CRC. Studies with larger cohorts and longer follow up are needed to further examine and validate theprognostic value of LNR.

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.

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

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

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

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

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

A quadratic regression modelling on paddy production in the area of Perlis

NASA Astrophysics Data System (ADS)

Goh, Aizat Hanis Annas; Ali, Zalila; Nor, Norlida Mohd; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2017-08-01

Polynomial regression models are useful in situations in which the relationship between a response variable and predictor variables is curvilinear. Polynomial regression fits the nonlinear relationship into a least squares linear regression model by decomposing the predictor variables into a kth order polynomial. The polynomial order determines the number of inflexions on the curvilinear fitted line. A second order polynomial forms a quadratic expression (parabolic curve) with either a single maximum or minimum, a third order polynomial forms a cubic expression with both a relative maximum and a minimum. This study used paddy data in the area of Perlis to model paddy production based on paddy cultivation characteristics and environmental characteristics. The results indicated that a quadratic regression model best fits the data and paddy production is affected by urea fertilizer application and the interaction between amount of average rainfall and percentage of area defected by pest and disease. Urea fertilizer application has a quadratic effect in the model which indicated that if the number of days of urea fertilizer application increased, paddy production is expected to decrease until it achieved a minimum value and paddy production is expected to increase at higher number of days of urea application. The decrease in paddy production with an increased in rainfall is greater, the higher the percentage of area defected by pest and disease.

Predictors of thrombotic complications and mass effect exacerbation after pipeline embolization: The significance of adenosine diphosphate inhibition, fluoroscopy time, and aneurysm size.

PubMed

Raychev, Radoslav; Tateshima, Satoshi; Vinuela, Fernando; Sayre, Jim; Jahan, Reza; Gonzalez, Nestor; Szeder, Viktor; Duckwiler, Gary

2016-02-01

The mechanisms leading to delayed rupture, distal emboli and intraparenchymal hemorrhage in relation to pipeline embolization device (PED) placement remain debatable and poorly understood. The aim of this study was to identify clinical and procedural predictors of these perioperative complications. We conducted a retrospective review of consecutive patients who underwent PED placement. We utilized a non-commercial platelet aggregation method measuring adenosine diphosphate (ADP)% inhibition for evaluation of clopidogrel response. To our knowledge, this is the first study to test ADP in neurovascular procedures. Multivariable regression analysis was used to identify the strongest predictor of three separate outcomes: (1) thrombotic complications, (2) hemorrhagic complications, and (3) aneurysm mass effect exacerbation Permanent complication-related morbidity and mortality at 3 months was 6% (3/48). No specific predictors of hemorrhagic complications were identified. In the univariate analysis, the strongest predictors of thrombotic complications were: ADP% inhibition<49 (p=0.01), aneurysm size (p=0.04) and fluoroscopy time (p=0.002). In the final multivariate analysis, among all baseline variables, fluoroscopy time exceeding 52 min was the only factor associated with thrombotic complications (p=0.007). Aneurysm size≥18 mm was the single predictor of mass effect exacerbation (p=0.039). Procedural complexity, reflected by fluoroscopy time, is the strongest predictor of thrombotic complications in this study. ADP% inhibition is a reliable method of testing clopidogrel response in neurovascular procedures and values of <50% may predict thrombotic complications. Interval mass effect exacerbation after PED placement may be anticipated in large aneurysms exceeding 18 mm. © The Author(s) 2015.

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.

Predictors of Acute Bacterial Meningitis in Children from a Malaria-Endemic Area of Papua New Guinea

PubMed Central

Laman, Moses; Manning, Laurens; Greenhill, Andrew R.; Mare, Trevor; Michael, Audrey; Shem, Silas; Vince, John; Lagani, William; Hwaiwhanje, Ilomo; Siba, Peter M.; Mueller, Ivo; Davis, Timothy M. E.

2012-01-01

Predictors of acute bacterial meningitis (ABM) were assessed in 554 children in Papua New Guinea 0.2–10 years of age who were hospitalized with culture-proven meningitis, probable meningitis, or non-meningitic illness investigated by lumbar puncture. Forty-seven (8.5%) had proven meningitis and 36 (6.5%) had probable meningitis. Neck stiffness, Kernig’s and Brudzinski’s signs and, in children < 18 months of age, a bulging fontanel had positive likelihood ratios (LRs) ≥ 4.3 for proven/probable ABM. Multiple seizures and deep coma were less predictive (LR = 1.5–2.1). Single seizures and malaria parasitemia had low LRs (≤ 0.5). In logistic regression including clinical variables, Kernig’s sign and deep coma were positively associated with ABM, and a single seizure was negatively associated (P ≤ 0.01). In models including microscopy, neck stiffness and deep coma were positively associated with ABM and parasitemia was negatively associated with ABM (P ≤ 0.04). In young children, a bulging fontanel added to the model (P < 0.001). Simple clinical features predict ABM in children in Papua New Guinea but malaria microscopy augments diagnostic precision. PMID:22302856

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

PubMed Central

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

2009-01-01

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

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

PubMed

Du, Juan; Leigh, J Paul

2011-10-01

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

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

EPA Science Inventory

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

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

PubMed

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

1978-12-01

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

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

PubMed

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

2016-09-01

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

Logic regression and its extensions.

PubMed

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

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

PubMed

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

2015-03-01

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

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

PubMed

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

2006-04-21

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

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

PubMed

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

2014-02-10

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

Miscarriage: A Special Type of Family Crisis.

ERIC Educational Resources Information Center

Day, Randal D.; Hooks, Daniel

1987-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Fatigue in sarcoidosis and idiopathic pulmonary fibrosis: differences in character and severity between diseases.

PubMed

Atkins, Christopher Peter; Gilbert, Daniel; Brockwell, Claire; Robinson, Sue; Wilson, Andrew Malcolm

2016-08-01

Sarcoidosis and idiopathic pulmonary fibrosis (IPF) are two common forms of interstitial lung disease. Fatigue is a recognised feature of sarcoidosis but an association between IPF and fatigue has not been investigated. To investigate the frequency and severity of fatigue in these groups, and variables affecting fatigue scores. A cross-sectional questionnaire study of patients with sarcoidosis and IPF followed-up at a single hospital was undertaken. Questionnaire data included validated measures of fatigue, anxiety, depression, sleepiness and dyspnoea, plus measures of disease severity including spirometry data. Questionnaires were administered to 232 patients (82 healthy volunteers, 73 sarcoidosis patients and 77 IPF patients). Sarcoidosis patients had statistically higher sleepiness scores but no significant difference was seen between overall measures of fatigue, anxiety or depression. Stratification by severity revealed a non-statistically significant tendency towards more severe fatigue scores in sarcoidosis. Regression analysis failed to identify any significant predictor variables measured in the sarcoidosis cohort, though in the IPF group both dyspnoea and sleepiness scores were significant predictors of fatigue (R2=0.74). Both sarcoidosis and IPF patients suffer with fatigue, although sarcoidosis patients tended towards reporting more severe fatigue scores, suggesting a subgroup with severe fatigue. The fatigue experienced by the two groups appears to be different; sarcoidosis patients report greater frequency of mental fatigue whereas IPF patients appear to suffer exhaustion, potentially related to dyspnoea. Dyspnoea and sleepiness scores modeled the majority of fatigue in the IPF group, whereas no single factor was able to predict fatigue in sarcoidosis.

The relative importance of physicochemical factors to stream biological condition in urbanizing basins: Evidence from multimodel inference

USGS Publications Warehouse

Carlisle, Daren M.; Bryant, Wade L.

2011-01-01

Many physicochemical factors potentially impair stream ecosystems in urbanizing basins, but few studies have evaluated their relative importance simultaneously, especially in different environmental settings. We used data collected in 25 to 30 streams along a gradient of urbanization in each of 6 metropolitan areas (MAs) to evaluate the relative importance of 11 physicochemical factors on the condition of algal, macroinvertebrate, and fish assemblages. For each assemblage, biological condition was quantified using 2 separate metrics, nonmetric multidimensional scaling ordination site scores and the ratio of observed/expected taxa, both derived in previous studies. Separate linear regression models with 1 or 2 factors as predictors were developed for each MA and assemblage metric. Model parsimony was evaluated based on Akaike’s Information Criterion for small sample size (AICc) and Akaike weights, and variable importance was estimated by summing the Akaike weights across models containing each stressor variable. Few of the factors were strongly correlated (Pearson |r| > 0.7) within MAs. Physicochemical factors explained 17 to 81% of variance in biological condition. Most (92 of 118) of the most plausible models contained 2 predictors, and generally more variance could be explained by the additive effects of 2 factors than by any single factor alone. None of the factors evaluated was universally important for all MAs or biological assemblages. The relative importance of factors varied for different measures of biological condition, biological assemblages, and MA. Our results suggest that the suite of physicochemical factors affecting urban stream ecosystems varies across broad geographic areas, along gradients of urban intensity, and among basins within single MAs.

Compatible Models of Carbon Content of Individual Trees on a Cunninghamia lanceolata Plantation in Fujian Province, China

PubMed Central

Zhuo, Lin; Tao, Hong; Wei, Hong; Chengzhen, Wu

2016-01-01

We tried to establish compatible carbon content models of individual trees for a Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantation from Fujian province in southeast China. In general, compatibility requires that the sum of components equal the whole tree, meaning that the sum of percentages calculated from component equations should equal 100%. Thus, we used multiple approaches to simulate carbon content in boles, branches, foliage leaves, roots and the whole individual trees. The approaches included (i) single optimal fitting (SOF), (ii) nonlinear adjustment in proportion (NAP) and (iii) nonlinear seemingly unrelated regression (NSUR). These approaches were used in combination with variables relating diameter at breast height (D) and tree height (H), such as D, D2H, DH and D&H (where D&H means two separate variables in bivariate model). Power, exponential and polynomial functions were tested as well as a new general function model was proposed by this study. Weighted least squares regression models were employed to eliminate heteroscedasticity. Model performances were evaluated by using mean residuals, residual variance, mean square error and the determination coefficient. The results indicated that models with two dimensional variables (DH, D2H and D&H) were always superior to those with a single variable (D). The D&H variable combination was found to be the most useful predictor. Of all the approaches, SOF could establish a single optimal model separately, but there were deviations in estimating results due to existing incompatibilities, while NAP and NSUR could ensure predictions compatibility. Simultaneously, we found that the new general model had better accuracy than others. In conclusion, we recommend that the new general model be used to estimate carbon content for Chinese fir and considered for other vegetation types as well. PMID:26982054

Variability of Organophosphorous Pesticide Metabolite Levels in Spot and 24-hr Urine Samples Collected from Young Children during 1 Week

PubMed Central

Kogut, Katherine; Eisen, Ellen A.; Jewell, Nicholas P.; Quirós-Alcalá, Lesliam; Castorina, Rosemary; Chevrier, Jonathan; Holland, Nina T.; Barr, Dana Boyd; Kavanagh-Baird, Geri; Eskenazi, Brenda

2012-01-01

Background: Dialkyl phosphate (DAP) metabolites in spot urine samples are frequently used to characterize children’s exposures to organophosphorous (OP) pesticides. However, variable exposure and short biological half-lives of OP pesticides could result in highly variable measurements, leading to exposure misclassification. Objective: We examined within- and between-child variability in DAP metabolites in urine samples collected during 1 week. Methods: We collected spot urine samples over 7 consecutive days from 25 children (3–6 years of age). On two of the days, we collected 24-hr voids. We assessed the reproducibility of urinary DAP metabolite concentrations and evaluated the sensitivity and specificity of spot urine samples as predictors of high (top 20%) or elevated (top 40%) weekly average DAP metabolite concentrations. Results: Within-child variance exceeded between-child variance by a factor of two to eight, depending on metabolite grouping. Although total DAP concentrations in single spot urine samples were moderately to strongly associated with concentrations in same-day 24-hr samples (r ≈ 0.6–0.8, p < 0.01), concentrations in spot samples collected > 1 day apart and in 24-hr samples collected 3 days apart were weakly correlated (r ≈ –0.21 to 0.38). Single spot samples predicted high (top 20%) and elevated (top 40%) full-week average total DAP excretion with only moderate sensitivity (≈ 0.52 and ≈ 0.67, respectively) but relatively high specificity (≈ 0.88 and ≈ 0.78, respectively). Conclusions: The high variability we observed in children’s DAP metabolite concentrations suggests that single-day urine samples provide only a brief snapshot of exposure. Sensitivity analyses suggest that classification of cumulative OP exposure based on spot samples is prone to type 2 classification errors. PMID:23052012

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.

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…

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.

PubMed

Preacher, Kristopher J; Hayes, Andrew F

2008-08-01

Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

Hospital Admissions for Malnutrition and Dehydration in Patients With Dementia.

PubMed

Marshall, Katherine A; Burson, Rosanne; Gall, Kristyn; Saunders, Mitzi M

2016-01-01

Dehydration and malnutrition are commonly experienced by patients with dementia and can result in hospitalizations and decreased quality of life. The purpose of this study was to explore and describe retrospectively, the incidence and correlations of variables that may precede hospitalizations for dehydration/malnutrition in the community-dwelling patient with dementia. Data from the Outcome and Assessment Information Set (OASIS) Start of Care (SOC) on 44 patients served by a Michigan home care agency were retrieved for analysis. This study did not reveal any single or collection of variables that would predict risk for hospitalization for dehydration/malnutrition. With the lack of specific predictors of hospitalization related to dehydration and malnutrition, clinicians need to place high priority on risk-lowering strategies and preventive education for patients, family, and caregivers.

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

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

ERIC Educational Resources Information Center

Mascia, George V.; And Others

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

Draft-camp predictors of subsequent career success in the Australian Football League.

PubMed

Burgess, Darren; Naughton, Geraldine; Hopkins, Will

2012-11-01

The National Draft Camp results are generally considered to be important for informing talent scouts about the physical performance capacities of talented young Australian Rules Football (AFL) players. The purpose of this project was to determine magnitude of associations between five year career success in the AFL and physical draft camp tests, final draft selection order and previous match physical performance. Physical testing data of 99 players from the National Under 18 (U 18) competition were retrospectively analysed across 2002 and 2003 National Draft Camps. Physical match data was collected on these players and links with subsequent early career success (AFL games played) were explored. TrakPerformance Software was used to quantify the movement of 92 players during competitive games of the National U 18 Championships. Linear modelling using results from draft camp data involving 95 U 18 players, along with final draft selection order, was used to predict five year career success in senior AFL. Multiple U 18 match variables demonstrated large associations (sprints/min=43% more games, % sprint=43% more games) with five year career success in AFL. Final draft order and single variable predictors had moderate associations with career success. Neither U 18 matches nor draft camp testing was predictive of injuries incurring over the five years. Variability in senior AFL career success had a large association with a combination of match physical variables and draft test results. The objective data available should be considered in the selection of prospective player success. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

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.

Influence of family structure on obesogenic behaviors and placement of bedroom TVs of American children: National Survey of Children's Health 2007.

PubMed

Sisson, Susan B; Sheffield-Morris, Amanda; Spicer, Paul; Lora, Karina; Latorre, Chelsea

2014-04-01

To explore the relation between family structure and obesogenic attributes. Publicly available data from the 2007 National Survey of Children's Health (n=55,094; 11.6 ± 0.04 years; 51.2% male) was analyzed in fall 2012. Predictor variables included marital status (two-parent biological [referent], two-parent blended, single-mother, and other) and number of children. Outcome variables included the presence of a bedroom television (BTV), elevated television (TV) viewing time, insufficient physical activity, and infrequent family meals. Analysis of family structure revealed 63% biological, 11% blended, and 20% single-mother families. Twenty-three percent of children did not have siblings. When family structure variables were considered independently, children in blended (odds ratio (OR): 1.75; 95% confidence interval (CI) 1.45, 2.10) and single-mother homes (1.49; 1.28, 1.74) had higher odds of BTV. Children in blended families had higher odds of elevated TV viewing time (1.28; 1.08, 1.51). Single-mother homes had higher odds of infrequent family meals (1.28; 1.07, 1.52). Families with ≥ 2 children were less likely to have BTV (0.60; 0.54, 0.66) or elevated TV viewing time (0.74; 0.67, 0.82), and to irregularly dine together (0.89; 0.80, 0.99). Diverse family structure was associated with more obesogenic behaviors and environments. The presence of siblings diminished, but did not eliminate, the risk. Copyright © 2014. Published by Elsevier Inc.

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

PubMed

Dekker, I; Barling, J

1998-01-01

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

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

PubMed

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

2012-03-01

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

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

NASA Astrophysics Data System (ADS)

Sobolewski, Stanley John

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

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

PubMed

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

2015-01-01

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

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.

A predictive study of reading comprehension in third-grade Spanish students.

PubMed

López-Escribano, Carmen; Elosúa de Juan, María Rosa; Gómez-Veiga, Isabel; García-Madruga, Juan Antonio

2013-01-01

The study of the contribution of language and cognitive skills to reading comprehension is an important goal of current reading research. However, reading comprehension is not easily assessed by a single instrument, as different comprehension tests vary in the type of tasks used and in the cognitive demands required. This study examines the contribution of basic language and cognitive skills (decoding, word recognition, reading speed, verbal and nonverbal intelligence and working memory) to reading comprehension, assessed by two tests utilizing various tasks that require different skill sets in third-grade Spanish-speaking students. Linguistic and cognitive abilities predicted reading comprehension. A measure of reading speed (the reading time of pseudo-words) was the best predictor of reading comprehension when assessed by the PROLEC-R test. However, measures of word recognition (the orthographic choice task) and verbal working memory were the best predictors of reading comprehension when assessed by means of the DARC test. These results show, on the one hand, that reading speed and word recognition are better predictors of Spanish language comprehension than reading accuracy. On the other, the reading comprehension test applied here serves as a critical variable when analyzing and interpreting results regarding this topic.

Factors influencing teamwork and collaboration within a tertiary medical center

PubMed Central

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

2012-01-01

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

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

ERIC Educational Resources Information Center

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

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

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

ERIC Educational Resources Information Center

Ruble, Lisa; McGrew, John H.

2013-01-01

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

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

ERIC Educational Resources Information Center

Shieh, Gwowen

2010-01-01

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

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

PubMed

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

2016-05-01

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

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

PubMed

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

2014-01-01

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

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

PubMed

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

2016-04-25

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

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

PubMed Central

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

2016-01-01

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

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.

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

PubMed

Borisenkov, Mikhail F

2011-03-01

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

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

Treesearch

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

2007-01-01

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

Late Language Emergence at 24 Months: An Epidemiological Study of Prevalence, Predictors, and Covariates

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Rice, Mabel L.

2012-01-01

Purpose The primary objectives of this study were to determine the prevalence of late language emergence (LLE) and to investigate the predictive status of maternal, family, and child variables. Method This is a prospective cohort study of 1766 epidemiologically ascertained twenty-four-month singleton children. The framework was an ecological model of child development, encompassing a wide range of maternal, family, and child variables. Data were obtained using postal questionnaire. Item analyses of the 6-item Ages and Stages Questionnaire (ASQ) Communication Scale yielded a composite score encompassing comprehension as well as production items. One standard deviation below the mean yielded good separation of affected from unaffected children. Analyses of bivariate relationships with maternal, family, and child variables were carried out, followed by multivariate logistic regression to predict LLE group membership. Results 13.4% of the sample showed late language emergence via the ASQ criterion; 19.1% using a single item “combining words.” Risk for LLE at 24 months was not associated with particular strata of parental educational levels, socioeconomic resources, parental mental health, parenting practices or family functioning. Significant predictors included familial history of late language emergence, male gender and early neurobiological growth. Covariates included psychosocial indicators. Conclusion Results are congruent with models of language emergence and impairment that posit a strong role for neurobiological and genetic mechanisms of onset that operate across a wide variation in maternal and family characteristics. PMID:18055773

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

The course of major depressive disorder from childhood to young adulthood: Recovery and recurrence in a longitudinal observational study.

PubMed

Kovacs, Maria; Obrosky, Scott; George, Charles

2016-10-01

The episodic nature of major depressive disorder (MDD) in clinically referred adults has been well-characterized, particularly by the NIMH Collaborative Depression Study. Previous work has established that MDD also is episodic prior to adulthood, but no study has yet provided comprehensive information on the actual course of MDD in clinically referred juveniles. Thus, the present investigation sought to characterize recovery, recurrence, and their predictors across multiple episodes of MDD in initially 8- to 13-year-old outpatients (N=102), and to estimate freedom from morbidity ("well-time") across the years. Clinically referred youngsters with MDD were repeatedly assessed in an observational study across two decades (median follow up length: 15 years). Survival analytic techniques served to model recovery from the 1st, 2nd and 3rd lifetime episodes of MDD, the risk of developing the 2nd, 3rd, and 4th episodes, and the effects of traditional psychosocial and clinical predictors of outcomes. "Well-time" across the follow-up and its predictors also were examined. Recovery rates ranged from 96% to 100% across MDD episodes; episode lengths ranged from 6 to 7 months. Up to 72% of those recovered from the first episode of MDD had a further episode; median inter-episode intervals were about 3-5 years. No single demographic, social, or clinical variable, nor treatment, consistently predicted recovery/recurrence. Psychiatric morbidity over time derived mostly from non-affective disorders, which, however, did not alter the course of MDD. The sample was relatively small and power to detect small effects further declined with each MDD episode recurrence. Echoing findings on adults, the course of pediatric-onset MDD in this clinical sample was unequivocally episodic. Traditional course predictors had limited temporal stability, highlighting the need to examine novel predictor variables. The ongoing risk of depression episodes into the second and third decades of life suggests that prevention efforts should start in late childhood. Copyright © 2016 Elsevier B.V. All rights reserved.

Demographic characteristics and clinical predictors of patients discharged from university hospital-affiliated pain clinic due to breach in narcotic use contract.

PubMed

Chakrabortty, Shushovan; Gupta, Deepak; Rustom, David; Berry, Hussein; Rai, Ajit

2014-01-01

The current retrospective study was completed with the aim to identify demographic characteristics and clinical predictors (if any) of the patients discharged from our pain clinic due to breach in narcotic use contract (BNUC). Retrospective patient charts' review and data audit. University hospital-affiliated pain clinic in the United States. All patient charts in our pain clinic for a 2-year period (2011-2012). The patients with BNUC were delineated from the patients who had not been discharged from our pain clinic. Pain characteristics, pain management, and substance abuse status were compared in each patient with BNUC between the time of admission and the time of discharge. The patients with BNUC discharges showed significant variability for the discharging factors among the pain physicians within a single pain clinic model with this variability being dependent on their years of experience and their proactive interventional pain management. The patients with BNUC in our pain clinic setting were primarily middle-aged, obese, unmarried males with nondocumented stable occupational history who were receiving only noninterventional pain management. Substance abuse, doctor shopping, and potential diversion were the top three documented reasons for BNUC discharges. In 2011-2012, our pain clinic discharged 1-in-16 patients due to breach in narcotic use contract.

Edmondson-Steiner grade: A crucial predictor of recurrence and survival in hepatocellular carcinoma without microvascular invasio.

PubMed

Zhou, Li; Rui, Jing-An; Zhou, Wei-Xun; Wang, Shao-Bin; Chen, Shu-Guang; Qu, Qiang

2017-07-01

Microvascular invasion (MVI), an important pathologic parameter, has been proven to be a powerful predictor of long-term prognosis in hepatocellular carcinoma (HCC). However, prognostic factors in HCC without MVI remain unknown. The present study aimed to identify the risk factors of recurrence and poor post-resectional survival in this type of HCC. A total of 109 patients with MVI-absent HCC underwent radical hepatectomy were enrolled. The influence of clinicopathologic variables on recurrence and patient survival was assessed using univariate and multivariate analyses. Chi-square test found that Edmondson-Steiner grade and satellite nodule were significantly associated with recurrence, while the former was the single marker for early recurrence. Stepwise logistic regression analysis demonstrated the independent predictive role of Edmondson-Steiner grade for recurrence. On the other hand, Edmondson-Steiner grade, serum AFP level and satellite nodule were significant for overall and disease-free survival in univariate analysis, whereas tumor size was linked to disease-free survival. Of the variables, Edmondson-Steiner grade, serum AFP level and satellite nodule were independent indicators. Edmondson-Steiner grade, a histological classification, carries robust prognostic implications for all the endpoints for prognosis, thus being potential to be a crucial prognosticator in HCC without MVI. Copyright © 2017 Elsevier GmbH. All rights reserved.

Edaphic controls on soil organic carbon stocks in restored grasslands

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

O'Brien, Sarah L.; Jastrow, Julie D.; Grimley, David A.

Cultivation of undisturbed soils dramatically depletes organic carbon stocks at shallow depths, releasing a substantial quantity of stored carbon to the atmosphere. Restoration of native ecosystems can help degraded soils rebuild a portion of the depleted soil organic matter. However, the rate and magnitude of soil carbon accrual can be highly variable from site to site. Thus, a better understanding of the mechanisms controlling soil organic carbon stocks is necessary to improve predictions of soil carbon recovery. We measured soil organic carbon stocks and a suite of edaphic factors in the upper 10 cm of a series of restored tallgrassmore » prairies representing a range of drainage conditions. Our findings suggest that factors related to soil organic matter stabilization mechanisms (texture, polyvalent cations) were key predictors of soil organic carbon, along with variables that influence plant and microbial biomass (available phosphorus, pH) and soil moisture. Exchangeable soil calcium was the strongest single predictor, explaining 74% of the variation in soil organic carbon, followed by clay content,which explained 52% of the variation. Our results demonstrate that the cumulative effects of even relatively small differences in these edaphic properties can have a large impact on soil carbon stocks when integrated over several decades.« less

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

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

ERIC Educational Resources Information Center

Fayombo, Grace Adebisi

2011-01-01

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

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

ERIC Educational Resources Information Center

Rothwell, Gary R.; Baldwin, J. Norman

2007-01-01

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

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

ERIC Educational Resources Information Center

Solano-Flores, Guillermo

1993-01-01

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

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

ERIC Educational Resources Information Center

Choi, Seong Ji

2012-01-01

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

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

ERIC Educational Resources Information Center

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

1994-01-01

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

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

ERIC Educational Resources Information Center

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

2017-01-01

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

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

ERIC Educational Resources Information Center

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

2015-01-01

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

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

ERIC Educational Resources Information Center

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

2015-01-01

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

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

ERIC Educational Resources Information Center

Coleman, Priscilla K.

2006-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

Physicians’ accuracy and interrator reliability for the diagnosis of unstable meniscal tears in patients having osteoarthritis of the knee

PubMed Central

Dervin, Geoffrey F.; Stiell, Ian G.; Wells, George A.; Rody, Kelly; Grabowski, Jenny

2001-01-01

Objective To determine clinicians’ accuracy and reliability for the clinical diagnosis of unstable meniscus tears in patients with symptomatic osteoarthritis of the knee. Design A prospective cohort study. Setting A single tertiary care centre. Patients One hundred and fifty-two patients with symptomatic osteoarthritis of the knee refractory to conservative medical treatment were selected for prospective evaluation of arthroscopic débridement. Intervention Arthroscopic débridement of the knee, including meniscal tear and chondral flap resection, without abrasion arthroplasty. Outcome measures A standardized assessment protocol was administered to each patient by 2 independent observers. Arthroscopic determination of unstable meniscal tears was recorded by 1 observer who reviewed a video recording and was blinded to preoperative data. Those variables that had the highest interobserver agreement and the strongest association with meniscal tear by univariate methods were entered into logistic regression to model the best prediction of resectable tears. Results There were 92 meniscal tears (77 medial, 15 lateral). Interobserver agreement between clinical fellows and treating surgeons was poor to fair (κ < 0.4) for all clinical variables except radiographic measures, which were good. Fellows and surgeons predicted unstable meniscal tear preoperatively with equivalent accuracy of 60%. Logistic regression modelling revealed that a history of swelling and a ballottable effusion were negative predictors. A positive McMurray test was the only positive predictor of unstable meniscal tear. “Mechanical” symptoms were not reliable predictors in this prospective study. The model was 69% accurate for all patients and 76% for those with advanced medial compartment osteoarthritis defined by a joint space height of 2 mm or less. Conclusions This study underscored the difficulty in using clinical variables to predict unstable medial meniscal tears in patients with pre-existing osteoarthritis of the knee. The lack of interobserver agreement must be overcome to ensure that the findings can be generalized to other physician observers. PMID:11504260

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.

Predictors of length of stay after urogynecological surgery at a tertiary referral center.

PubMed

Gagnon, Louise-Helene; Tang, Selphee; Brennand, Erin

2017-02-01

The primary objective of this study was to determine significant predictors of length of stay (LOS) beyond the first postoperative day after urogynecological surgery. A single-center retrospective cohort study was conducted in 2015. Our study population included women who underwent inpatient pelvic reconstructive surgery. The primary outcome was LOS beyond the first postoperative day. A logistic regression analysis explored the relationship between 11 selected predictor variables [age, body mass index (BMI), American Society of Anesthesiologists (ASA) score, distance from home to hospital, length of surgery, anesthesia during surgery, route of surgical approach, trial of void recordings, choice of bladder protocol, presence of concomitant sling, surgeon], and LOS. Two hundred and sixty-three patients were included in this study. A logistic regression analysis identified route of surgery and trial of void recordings as the two statistically significant predictors of stay beyond the first postoperative day. The odds of LOS after laparoscopic or open surgery compared with vaginal surgery increased more than fivefold [laparoscopic vs. vaginal approach odds ratio (OR) 5.04, 95 % confidence interval (CI) 1.95-13.03; laparotomy vs. vaginal OR 15.56, 95 % CI 1.77-136.77] and more than threefold for a prolonged pass of the bladder protocol compared with an immediate pass (OR 3.25, 95 % CI 1.54-6.87). Our study identified route of surgery and trial of void recordings as the two predictors with the greatest impact on LOS beyond the first postoperative day. Our results warrant a larger follow-up study.

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.

Understanding Cancer Worry Among Patients in a Community Clinic-Based Colorectal Cancer Screening Intervention Study.

PubMed

Christy, Shannon M; Schmidt, Alyssa; Wang, Hsiao-Lan; Sutton, Steven K; Davis, Stacy N; Chavarria, Enmanuel; Abdulla, Rania; Quinn, Gwendolyn P; Vadaparampil, Susan T; Schultz, Ida; Roetzheim, Richard; Shibata, David; Meade, Cathy D; Gwede, Clement K

2018-06-04

To reduce colorectal cancer (CRC) screening disparities, it is important to understand correlates of different types of cancer worry among ethnically diverse individuals. The current study examined the prevalence of three types of cancer worry (i.e., general cancer worry, CRC-specific worry, and worry about CRC test results) as well as sociodemographic and health-related predictors for each type of cancer worry. Participants were aged 50-75, at average CRC risk, nonadherent to CRC screening guidelines, and enrolled in a randomized controlled trial to increase CRC screening. Participants completed a baseline questionnaire assessing sociodemographics, health beliefs, healthcare experiences, and three cancer worry measures. Associations between study variables were examined with separate univariate and multivariable logistic regression models. Responses from a total of 416 participants were used. Of these, 47% reported experiencing moderate-to-high levels of general cancer worry. Predictors of general cancer worry were salience and coherence (aOR = 1.1, 95% CI [1.0, 1.3]), perceived susceptibility (aOR = 1.2, 95% CI [1.1, 1.3), and social influence (aOR = 1.1, 95% CI [1.0, 0.1]). Fewer (23%) reported moderate-to-high levels of CRC-specific worry or CRC test worry (35%). Predictors of CRC worry were perceived susceptibility (aOR = 1.4, 95% CI [1.3, 1.6]) and social influence (aOR = 1.1, 95% CI [1.0, 1.2]); predictors of CRC test result worry were perceived susceptibility (aOR = 1.2, 95% CI [1.1, 1.3) and marital status (aOR = 2.0, 95% CI [1.1, 3.7] for married/partnered vs. single and aOR = 2.3, 95% CI [1.3, 4.1] for divorced/widowed vs. single). Perceived susceptibility consistently predicted the three types of cancer worry, whereas other predictors varied between cancer worry types and in magnitude of association. The three types of cancer worry were generally predicted by health beliefs, suggesting potential malleability. Future research should include multiple measures of cancer worry and clear definitions of how cancer worry is measured.

The relationship of document and quantitative literacy with learning styles and selected personal variables for aerospace technology students at Indiana State University

NASA Astrophysics Data System (ADS)

Martin, Royce Ann

The purpose of this study was to determine the extent that student scores on a researcher-constructed quantitative and document literacy test, the Aviation Documents Delineator (ADD), were associated with (a) learning styles (imaginative, analytic, common sense, dynamic, and undetermined), as identified by the Learning Type Measure, (b) program curriculum (aerospace administration, professional pilot, both aerospace administration and professional pilot, other, or undeclared), (c) overall cumulative grade point average at Indiana State University, and (d) year in school (freshman, sophomore, junior, or senior). The Aviation Documents Delineator (ADD) was a three-part, 35 question survey that required students to interpret graphs, tables, and maps. Tasks assessed in the ADD included (a) locating, interpreting, and describing specific data displayed in the document, (b) determining data for a specified point on the table through interpolation, (c) comparing data for a string of variables representing one aspect of aircraft performance to another string of variables representing a different aspect of aircraft performance, (d) interpreting the documents to make decisions regarding emergency situations, and (e) performing single and/or sequential mathematical operations on a specified set of data. The Learning Type Measure (LTM) was a 15 item self-report survey developed by Bernice McCarthy (1995) to profile an individual's processing and perception tendencies in order to reveal different individual approaches to learning. The sample used in this study included 143 students enrolled in Aerospace Technology Department courses at Indiana State University in the fall of 1996. The ADD and the LTM were administered to each subject. Data collected in this investigation were analyzed using a stepwise multiple regression analysis technique. Results of the study revealed that the variables, year in school and GPA, were significant predictors of the criterion variables, document, quantitative, and total literacy, when utilizing the ADD. The variables learning style and program of study were found not to be significant predictors of literacy scores on the ADD instrument.

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2012-11-20

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

High interpatient variability of raltegravir CSF concentrations in HIV-positive patients: a pharmacogenetic analysis.

PubMed

Calcagno, Andrea; Cusato, Jessica; Simiele, Marco; Motta, Ilaria; Audagnotto, Sabrina; Bracchi, Margherita; D'Avolio, Antonio; Di Perri, Giovanni; Bonora, Stefano

2014-01-01

To analyse the determinants of raltegravir CSF penetration, including the pharmacogenetics of drug transporters located at the blood-brain barrier or blood-CSF barrier. Plasma and CSF raltegravir concentrations were determined by a validated HPLC coupled with mass spectrometry method in adults on raltegravir-based combination antiretroviral therapy undergoing a lumbar puncture. Single nucleotide polymorphisms in the genes encoding drugs transporters (ABCB1 3435, SLCO1A2, ABCC2 and SLC22A6) and the gene encoding hepatocyte nuclear factor 4 α (HNF4α) were determined by real-time PCR. In 41 patients (73.2% male, 95.1% Caucasians), the median raltegravir plasma and CSF concentrations were 165 ng/mL (83-552) and 31 ng/mL (21-56), respectively. CSF-to-plasma ratios (CPRs) ranged from 0.005 to 1.33 (median 0.20, IQR 0.04-0.36). Raltegravir trough CSF concentrations (n = 35) correlated with raltegravir plasma levels (ρ = 0.395, P = 0.019); CPRs were higher in patients with blood-brain barrier damage (0.47 versus 0.18, P = 0.02). HNF4α 613 CG genotype carriers had lower trough CSF concentrations (20 versus 37 ng/mL, P = 0.03) and CPRs (0.12 versus 0.27, P = 0.02). Following multivariate linear regression analysis, the CSF-to-serum albumin ratio was the only independent predictor of raltegravir penetration into the CSF. Raltegravir penetration into the CSF shows a large interpatient variability, although CSF concentrations were above the wild-type IC50 in all patients (and above IC95 in 28.6%). In this cohort, blood-brain barrier permeability is the only independent predictor of raltegravir CPR. The impact of single nucleotide polymorphisms in selected genes on raltegravir penetration warrants further studies.

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

PubMed

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

2018-05-21

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

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.

Ecological forecasting in the presence of abrupt regime shifts

NASA Astrophysics Data System (ADS)

Dippner, Joachim W.; Kröncke, Ingrid

2015-10-01

Regime shifts may cause an intrinsic decrease in the potential predictability of marine ecosystems. In such cases, forecasts of biological variables fail. To improve prediction of long-term variability in environmental variables, we constructed a multivariate climate index and applied it to forecast ecological time series. The concept is demonstrated herein using climate and macrozoobenthos data from the southern North Sea. Special emphasis is given to the influence of selection of length of fitting period to the quality of forecast skill especially in the presence of regime shifts. Our results indicate that the performance of multivariate predictors in biological forecasts is much better than that of single large-scale climate indices, especially in the presence of regime shifts. The approach used to develop the index is generally applicable to all geographical regions in the world and to all areas of marine biology, from the species level up to biodiversity. Such forecasts are of vital interest for practical aspects of the sustainable management of marine ecosystems and the conservation of ecosystem goods and services.

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

PubMed Central

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

2012-01-01

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

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

PubMed

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

2012-01-01

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

Constrained Stochastic Extended Redundancy Analysis.

PubMed

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

2015-06-01

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

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

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

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

Assessing the determinants of evolutionary rates in the presence of noise.

PubMed

Plotkin, Joshua B; Fraser, Hunter B

2007-05-01

Although protein sequences are known to evolve at vastly different rates, little is known about what determines their rate of evolution. However, a recent study using principal component regression (PCR) has concluded that evolutionary rates in yeast are primarily governed by a single determinant related to translation frequency. Here, we demonstrate that noise in biological data can confound PCRs, leading to spurious conclusions. When equalizing noise levels across 7 predictor variables used in previous studies, we find no evidence that protein evolution is dominated by a single determinant. Our results indicate that a variety of factors--including expression level, gene dispensability, and protein-protein interactions--may independently affect evolutionary rates in yeast. More accurate measurements or more sophisticated statistical techniques will be required to determine which one, if any, of these factors dominates protein evolution.

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)

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

Scaling of muscle architecture and fiber types in the rat hindlimb.

PubMed

Eng, Carolyn M; Smallwood, Laura H; Rainiero, Maria Pia; Lahey, Michele; Ward, Samuel R; Lieber, Richard L

2008-07-01

The functional capacity of a muscle is determined by its architecture and metabolic properties. Although extensive analyses of muscle architecture and fiber type have been completed in a large number of muscles in numerous species, there have been few studies that have looked at the interrelationship of these functional parameters among muscles of a single species. Nor have the architectural properties of individual muscles been compared across species to understand scaling. This study examined muscle architecture and fiber type in the rat (Rattus norvegicus) hindlimb to examine each muscle's functional specialization. Discriminant analysis demonstrated that architectural properties are a greater predictor of muscle function (as defined by primary joint action and anti-gravity or non anti-gravity role) than fiber type. Architectural properties were not strictly aligned with fiber type, but when muscles were grouped according to anti-gravity versus non-anti-gravity function there was evidence of functional specialization. Specifically, anti-gravity muscles had a larger percentage of slow fiber type and increased muscle physiological cross-sectional area. Incongruities between a muscle's architecture and fiber type may reflect the variability of functional requirements on single muscles, especially those that cross multiple joints. Additionally, discriminant analysis and scaling of architectural variables in the hindlimb across several mammalian species was used to explore whether any functional patterns could be elucidated within single muscles or across muscle groups. Several muscles deviated from previously described muscle architecture scaling rules and there was large variability within functional groups in how muscles should be scaled with body size. This implies that functional demands placed on muscles across species should be examined on the single muscle level.

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.

Association of genetic and psychological factors with persistent pain after cosmetic thoracic surgery

PubMed Central

Dimova, Violeta; Lötsch, Jörn; Hühne, Kathrin; Winterpacht, Andreas; Heesen, Michael; Parthum, Andreas; Weber, Peter G; Carbon, Roman; Griessinger, Norbert; Sittl, Reinhard; Lautenbacher, Stefan

2015-01-01

The genetic control of pain has been repeatedly demonstrated in human association studies. In the present study, we assessed the relative contribution of 16 single nucleotide polymorphisms in pain-related genes, such as cathechol-O-methyl transferase gene (COMT), fatty acid amino hydrolase gene (FAAH), transient receptor potential cation channel, subfamily V, member 1 gene (TRPV1), and δ-opioid receptor gene (OPRD1), for postsurgical pain chronification. Ninety preoperatively pain-free male patients were assigned to good or poor outcome groups according to their intensity or disability score assessed at 1 week, 3 months, 6 months, and 1 year after funnel chest correction. The genetic effects were compared with those of two psychological predictors, the attentional bias toward positive words (dot-probe task) and the self-reported pain vigilance (Pain Vigilance and Awareness Questionnaire [PVAQ]), which were already shown to be the best predictors for pain intensity and disability at 6 months after surgery in the same sample, respectively. Cox regression analyses revealed no significant effects of any of the genetic predictors up to the end point of survival time at 1 year after surgery. Adding the genetics to the prediction by the attentional bias to positive words for pain intensity and the PVAQ for pain disability, again no significant additional explanation could be gained by the genetic predictors. In contrast, the preoperative PVAQ score was also, in the present enlarged sample, a meaningful predictor for lasting pain disability after surgery. Effect size measures suggested some genetic variables, for example, the polymorphism rs1800587G>A in the interleukin 1 alpha gene (IL1A) and the COMT haplotype rs4646312T>C/rs165722T>C/rs6269A>G/rs4633T>C/rs4818C>G/rs4680A>G, as possible relevant modulators of long-term postsurgical pain outcome. A comparison between pathophysiologically different predictor groups appears to be helpful in identifying clinically relevant predictors of chronic pain. PMID:26664154

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

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-07-01

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

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

ERIC Educational Resources Information Center

Karelitz, Tzur M.

2008-01-01

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

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

NASA Astrophysics Data System (ADS)

Jones, William I.

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

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

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

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

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

Finding structure in data using multivariate tree boosting

PubMed Central

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

2016-01-01

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

The role of lexical variables in the visual recognition of Chinese characters: A megastudy analysis.

PubMed

Sze, Wei Ping; Yap, Melvin J; Rickard Liow, Susan J

2015-01-01

Logographic Chinese orthography partially represents both phonology and semantics. By capturing the online processing of a large pool of Chinese characters, we were able to examine the relative salience of specific lexical variables when this nonalphabetic script is read. Using a sample of native mainland Chinese speakers (N = 35), lexical decision latencies for 1560 single characters were collated into a database, before the effects of a comprehensive range of variables were explored. Hierarchical regression analyses determined the unique item-level variance explained by orthographic (frequency, stroke count), semantic (age of learning, imageability, number of meanings), and phonological (consistency, phonological frequency) factors. Orthographic and semantic variables, respectively, accounted for more collective variance than the phonological variables. Significant main effects were further observed for the individual orthographic and semantic predictors. These results are consistent with the idea that skilled readers tend to rely on orthographic and semantic information when processing visually presented characters. This megastudy approach marks an important extension to existing work on Chinese character recognition, which hitherto has relied on factorial designs. Collectively, the findings reported here represent a useful set of empirical constraints for future computational models of character recognition.

Response variability in rapid automatized naming predicts reading comprehension

PubMed Central

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

2009-01-01

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

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

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

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

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

PubMed

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

2014-09-01

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

Housing Retention in Single-Site Housing First for Chronically Homeless Individuals With Severe Alcohol Problems

PubMed Central

Malone, Daniel K.; Clifasefi, Seema L.

2013-01-01

Objectives. We studied housing retention and its predictors in the single-site Housing First model. Methods. Participants (n = 111) were chronically homeless people with severe alcohol problems who lived in a single-site Housing First program and participated in a larger nonrandomized controlled trial (2005–2008) conducted in Seattle, Washington. At baseline, participants responded to self-report questionnaires assessing demographic, illness burden, alcohol and other drug use, and psychiatric variables. Housing status was recorded over 2 years. Results. Participants were interested in housing, although a sizable minority did not believe they would be able to maintain abstinence-based housing. Only 23% of participants returned to homelessness during the 2-year follow-up. Commonly cited risk factors—alcohol and other drug use, illness burden, psychiatric symptoms, and homelessness history—did not predict resumed homelessness. Active drinkers were more likely to stay in this housing project than nondrinkers. Conclusions. We found that single-site Housing First programming fills a gap in housing options for chronically homeless people with severe alcohol problems. PMID:24148063

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

USGS Publications Warehouse

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

2009-01-01

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

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.

Predictors of treatment attrition and treatment length in Parent-Child Interaction Therapy in Taiwanese families✩,✩✩

PubMed Central

Chen, Yi-Chuen; Fortson, Beverly L.

2015-01-01

Parent–Child Interaction Therapy (PCIT) has been used successfully in the United States and in other countries around the world, but its use in Asian countries has been more limited. The present study is the first of its kind to examine the predictors of treatment attrition and length in a sample of Taiwanese caregivers and their children. It is also the first to examine PCIT outcomes in Taiwanese families. Maladaptive personality characteristics of the caregiver were the best predictor of attrition, followed by single-parent, removal of the child from the home, and lower levels of caregiver education. Treatment length was predicted by child minority status and parent–child interactions (i.e., parent commands and negative parent talk). In terms of outcomes, statistically significant treatment changes were noted for all treatment outcome variables at post-treatment and at 3-month follow-up. These findings suggest that PCIT is a promising intervention for this population. The predictors of treatment attrition and length can be used when Taiwanese caregiver–child dyads present for services so that additional assistance can be provided prior to or during treatment to increase adherence to the recommended number of treatment sessions for maximal impact. Future studies may replicate the present study with a larger clinical sample to examine the long-term effects of PCIT and to include a no-treatment control condition to afford a more robust empirical evaluation. PMID:26705373

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

PubMed

Sibold, Jeremy; Zizzi, Samuel

2012-01-01

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

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

PubMed Central

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

2017-01-01

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

Assessing the accuracy and stability of variable selection ...

EPA Pesticide Factsheets

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

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

PubMed

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

2018-05-01

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

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

PubMed Central

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

2017-01-01

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

Modeling and Predicting Hearing Aid Outcome

PubMed Central

Humes, Larry E.

2003-01-01

Following a brief tutorial on the application of factor analysis to hearing aid outcome measures, three studies of hearing aid outcome measures in elderly adults are presented and analyzed. Two of the studies were completed at Indiana University (IU-1 and IU-2), and one was a collaborative multisite study by the Veterans Administration and the National Institute of Deafness and other Communication Disorders (NIDCD/VA). IU-1 measured hearing aid outcome in 173 elderly wearers of single-channel, linear, in-the-ear hearing aids with output-limiting compression, whereas IU-2 obtained the same extensive set of outcome measures from 53 elderly wearers of two-channel, wide-dynamic-range compression, in-the-canal hearing aids. In the NIDCD/VA study, 333 to 338 participants wore three single-channel circuits in succession, with each circuit housed within an in-the-ear shell. The three circuits included in that study and in this analysis were: (1) linear with peak clipping, (2) linear with output-limiting compression, and (3) single-channel, wide-dynamic-range compression. Evaluation of the many outcome measures completed in each study using principal components factor analysis revealed that from three (both IU studies) to five (NIDCD/VA study) principal components captured the individual differences in hearing aid outcome. This was independent of hearing aid type (in-the-ear or in-the-canal) and circuitry. Subsequent multiple regression analyses of individual differences in performance along each dimension of hearing aid outcome revealed that these individual differences could be accounted for reasonably well by various prefit variables for some dimensions of outcome, but not others. In general, measures of speech recognition performance were well accounted for by prefit measures, with the best predictors being hearing loss, cognitive performance, and age. Measures of hearing aid usage were less well accounted for by prefit measures, with the most accurate predictor of current hearing aid use being prior hearing aid use. The outcome dimension accounted for most poorly was that associated with hearing aid satisfaction, with subjective measures of aided sound quality being the best predictor of performance along this dimension of hearing aid outcome. Additional multicenter, large-scale studies are needed to develop more complete models of hearing aid outcome and to identify the variables that influence various aspects of hearing aid outcome. It is only through this additional research that it will be possible to optimize outcome for hearing aid wearers. PMID:15004647

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.

Cardiovascular outcomes after pharmacologic stress myocardial perfusion imaging.

PubMed

Lee, Douglas S; Husain, Mansoor; Wang, Xuesong; Austin, Peter C; Iwanochko, Robert M

2016-04-01

While pharmacologic stress single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is used for noninvasive evaluation of patients who are unable to perform treadmill exercise, its impact on net reclassification improvement (NRI) of prognosis is unknown. We evaluated the prognostic value of pharmacologic stress MPI for prediction of cardiovascular death or non-fatal myocardial infarction (MI) within 1 year at a single-center, university-based laboratory. We examined continuous and categorical NRI of pharmacologic SPECT-MPI for prediction of outcomes beyond clinical factors alone. Six thousand two hundred forty patients (median age 66 years [IQR 56-74], 3466 men) were studied and followed for 5963 person-years. SPECT-MPI variables associated with increased risk of cardiovascular death or non-fatal MI included summed stress score, stress ST-shift, and post-stress resting left ventricular ejection fraction ≤50%. Compared to a clinical model which included age, sex, cardiovascular disease, risk factors, and medications, model χ(2) (210.5 vs. 281.9, P < .001) and c-statistic (0.74 vs. 0.78, P < .001) were significantly increased by addition of SPECT-MPI predictors (summed stress score, stress ST-shift and stress resting left ventricular ejection fraction). SPECT-MPI predictors increased continuous NRI by 49.4% (P < .001), reclassifying 66.5% of patients as lower risk and 32.8% as higher risk of cardiovascular death or non-fatal MI. Addition of MPI predictors to clinical factors using risk categories, defined as <1%, 1% to 3%, and >3% annualized risk of cardiovascular death or non-fatal MI, yielded a 15.0% improvement in NRI (95% CI 7.6%-27.6%, P < .001). Pharmacologic stress MPI substantially improved net reclassification of cardiovascular death or MI risk beyond that afforded by clinical factors. Copyright © 2016 Elsevier Inc. All rights reserved.

Predictors of College Retention and Performance between Regular and Special Admissions

ERIC Educational Resources Information Center

Kim, Johyun

2015-01-01

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

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

ERIC Educational Resources Information Center

Masheb, Robin M.; Grilo, Carlos M.

2008-01-01

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

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

ERIC Educational Resources Information Center

Eshbaugh, Elaine M.

2006-01-01

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

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

ERIC Educational Resources Information Center

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

2005-01-01

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

Physical Activity and Perceived Self-Efficacy in Older Adults.

ERIC Educational Resources Information Center

Langan, Mary E.; Marotta, Sylvia A.

2000-01-01

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

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

PubMed

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

2014-10-01

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

Prediction of problematic wine fermentations using artificial neural networks.

PubMed

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

2011-11-01

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

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

PubMed

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

2010-07-01

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

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

PubMed

Dang, Michelle T

2014-03-01

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

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

PubMed

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

2015-05-01

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

Changes in patellofemoral pain resulting from repetitive impact landings are associated with the magnitude and rate of patellofemoral joint loading.

PubMed

Atkins, Lee T; James, C Roger; Yang, Hyung Suk; Sizer, Phillip S; Brismée, Jean-Michel; Sawyer, Steven F; Powers, Christopher M

2018-03-01

Although a relationship between elevated patellofemoral forces and pain has been proposed, it is unknown which joint loading variable (magnitude, rate) is best associated with pain changes. The purpose of this study was to examine associations among patellofemoral joint loading variables and changes in patellofemoral pain across repeated single limb landings. Thirty-one females (age: 23.5(2.8) year; height: 166.8(5.8) cm; mass: 59.6(8.1) kg) with PFP performed 5 landing trials from 0.25 m. The dependent variable was rate of change in pain obtained from self-reported pain scores following each trial. Independent variables included 5-trial averages of peak, time-integral, and average and maximum development rates of the patellofemoral joint reaction force obtained using a previously described model. Pearson correlation coefficients were calculated to evaluate individual associations between rate of change in pain and each independent variable (α = 0.05). Stepwise linear multiple regression (α enter  = 0.05; α exit  = 0.10) was used to identify the best predictor of rate of change in pain. Subjects reported an average increase of 0.38 pain points with each landing trial. Although, rate of change in pain was positively correlated with peak force (r = 0.44, p = 0.01), and average (r = 0.41, p = 0.02) and maximum force development rates (r = 0.39, p = 0.03), only the peak force entered the predictive model explaining 19% of variance in rate of change in pain (r 2  = 0.19, p = 0.01). Peak patellofemoral joint reaction force was the best predictor of the rate of change in pain following repetitive singe limb landings. The current study supports the theory that patellofemoral joint loading contributes to changes in patellofemoral pain. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Predictors for early introduction of solid food among Danish mothers and infants: an observational study.

PubMed

Kronborg, Hanne; Foverskov, Else; Væth, Michael

2014-10-01

Early introduction of complementary feeding may interfere with breastfeeding and the infant's self-controlled appetite resulting in increased growth. The aim of the present study was to investigate predictors for early introduction of solid food. In an observational study Danish mothers filled in a self-administered questionnaire approximately six months after birth. The questionnaire included questions about factors related to the infant, the mother, attachment and feeding known to influence time for introduction of solid food. The study population consisted of 4503 infants. Data were analysed using ordered logistic regression models. Outcome variable was time for introduction to solid food. Almost all of the included infants 4386 (97%) initiated breastfeeding. At weeks 16, 17-25, 25+, 330 infants (7%); 2923 (65%); and 1250 (28%), respectively had been introduced to solid food. Full breastfeeding at five weeks was the most influential predictor for later introduction of solid food (OR = 2.52 CI: 1.93-3.28). Among infant factors male gender, increased gestational age at birth, and higher birth weight were found to be statistically significant predictors. Among maternal factors, lower maternal age, higher BMI, and being primipara were significant predictors, and among attachment factors mother's reported perception of the infant as being temperamental, and not recognising early infant cues of hunger were significant predictors for earlier introduction of solid food. Supplementary analyses of interactions between the predictors showed that the association of maternal perceived infant temperament on early introduction was restricted to primiparae, that the mother's pre-pregnancy BMI had no impact if the infant was fully breastfed at week five, and that birth weight was only associated if the mother had reported early uncertainty in recognising infant's cues of hunger. Breastfeeding was the single most powerful indicator for preventing early introduction to solid food. Modifiable predictors pointed to the importance of supporting breastfeeding and educating primipara and mothers with low birth weight infants to be able to read and respond to their infants' cues to prevent early introduction to solid food.

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.

Sympatho-vagal balance, as quantified by ANSindex, predicts post spinal hypotension and vasopressor requirement in parturients undergoing lower segmental cesarean section: a single blinded prospective observational study.

PubMed

Prashanth, Anitha; Chakravarthy, Murali; George, Antony; Mayur, Rohini; Hosur, Rajathadri; Pargaonkar, Sumant

2017-08-01

Hypotension subsequent to spinal anesthesia occurs in a significant number of parturients undergoing lower segment caesarian section. Currently available methods to predict the incidence of hypotension, its severity and the outcome are sub-optimal. Many workers have used basal heart rate as one of the predictors. But using this method it is not possible to objectively analyze and predict the extent and severity of hypotension. We used an equipment measuring the level of sympatho-vagal balance, ANSiscope™, which derives these values from computed value of RR interval variability. We made a single measure of the value which was blinded to the patient and the anesthesiologist. We studied one hundred eight patients who underwent lower segment caesarian section under spinal anesthesia and found the variability of preoperative ANSindex (% activity displayed by the equipment) from 9 to 65 %. Higher ANSindex value was significantly associated with post spinal hypotension (p 0.017). A value of 24 % indicated the critical level above which hypotension appeared commonly. The ANSindex value might help anesthesiologist to anticipate and prepare for hypotension that is likely to ensue.

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

PubMed

Rosen, L N; Martin, L

1998-01-01

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

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

PubMed

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

2015-05-01

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

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

PubMed Central

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

2012-01-01

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

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

PubMed

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

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

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

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

PubMed

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

2017-10-30

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

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

PubMed

Cleophas, Ton J

2016-01-01

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

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.

Sociodemographic and home environment predictors of screen viewing among Spanish school children.

PubMed

Hoyos Cillero, Itziar; Jago, Russell

2011-09-01

Higher screen-viewing levels increase the risk of obesity. Understanding the correlates of screen viewing is an important first step in designing interventions but there is lack of information on the correlates among Spanish children. This study examined associations among environmental, sociocultural, age variables and screen viewing among Spanish children. Children completed a questionnaire about time spent in screen viewing. BMI was assessed and children were classified into obesity groups using International Obesity Task Force cut-off points. Parents completed a questionnaire about sociodemographic, environmental and sociocultural variables. Participants were 247 primary and 256 secondary school-aged children and their parents. Time spent in screen viewing increased with age. Males spent more time than females in screen viewing. Greater access to bedroom media sources was associated with higher screen viewing. Younger children from single-parent households and older children having a younger parent, siblings and a father who was not working were higher screen-viewers on weekends and weekdays, respectively. For older children parental TV viewing time appeared to be a significant correlate, while parental rules was a determinant predictor for younger children on weekdays. Environmental and sociocultural factors influence the time children spend in screen viewing. Parents play a central role in child's screen viewing; therefore, interventions that target environmental and family TV viewing practices are likely to be effective.

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

PubMed

Acelam, Philip A

2015-01-01

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

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

PubMed Central

Acelam, Philip A

2015-01-01

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

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

PubMed

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

2018-05-01

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

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

PubMed

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

1998-02-01

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

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

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

2002-01-01

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

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

ERIC Educational Resources Information Center

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

2016-01-01

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

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

ERIC Educational Resources Information Center

Klapproth, Florian; Schaltz, Paule

2015-01-01

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

The effect of charge mutations on the stability and aggregation of a human single chain Fv fragment.

PubMed

Austerberry, James I; Dajani, Rana; Panova, Stanislava; Roberts, Dorota; Golovanov, Alexander P; Pluen, Alain; van der Walle, Christopher F; Uddin, Shahid; Warwicker, Jim; Derrick, Jeremy P; Curtis, Robin

2017-06-01

The aggregation propensities for a series of single-chain variable fragment (scFv) mutant proteins containing supercharged sequences, salt bridges and lysine/arginine-enriched motifs were characterised as a function of pH and ionic strength to isolate the electrostatic contributions. Recent improvements in aggregation predictors rely on using knowledge of native-state protein-protein interactions. Consistent with previous findings, electrostatic contributions to native protein-protein interactions correlate with aggregate growth pathway and rates. However, strong reversible self-association observed for selected mutants under native conditions did not correlate with aggregate growth, indicating 'sticky' surfaces that are exposed in the native monomeric state are inaccessible when aggregates grow. We find that even though similar native-state protein-protein interactions occur for the arginine and lysine-enriched mutants, aggregation propensity is increased for the former and decreased for the latter, providing evidence that lysine suppresses interactions between partially folded states under these conditions. The supercharged mutants follow the behaviour observed for basic proteins under acidic conditions; where excess net charge decreases conformational stability and increases nucleation rates, but conversely reduces aggregate growth rates due to increased intermolecular electrostatic repulsion. The results highlight the limitations of using conformational stability and native-state protein-protein interactions as predictors for aggregation propensity and provide guidance on how to engineer stabilizing charged mutations. Copyright © 2017. Published by Elsevier B.V.

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 Impact of Reporting a Prior Penicillin Allergy on the Treatment of Methicillin-Sensitive Staphylococcus aureus Bacteremia

PubMed Central

Shenoy, Erica S.; Huang, Mingshu; Kuhlen, James L.; Ware, Winston A.; Parker, Robert A.; Walensky, Rochelle P.

2016-01-01

Background Methicillin-sensitive Staphylococcus aureus (MSSA) bacteremia is a morbid infection with mortality benefit from receipt of parenteral β-lactam therapy. A substantial portion of MSSA bacteremia patients report penicillin allergy, but infrequently have true allergy. Objective To determine the frequency and predictors of optimal and adequate therapy in patients with MSSA bacteremia. Design Retrospective cohort. Participants Adult inpatients with MSSA bacteremia, January 2009 through October 2013. Main Measures The primary measure was a trial of optimal therapy (OT), defined as ≥3 inpatient days or discharge on any first-line agents (nafcillin, oxacillin, cefazolin, or penicillin G, if susceptible). The secondary measure was completion of adequate therapy (AT), defined as ≥10 inpatient days or discharge on an agent appropriate for MSSA bacteremia. Data were electronically gathered with key variables manually validated through chart review. Log-binomial regression models were used to determine the frequency and predictors of outcomes. Key Results Of 456 patients, 346 (76%) received a trial of OT. Patients reporting penicillin allergy (13%) were less likely to receive OT trial than those without penicillin allergy (47% vs. 80%, p <0.001). Adjusting for other factors, penicillin allergy was the largest negative predictor of OT trial (RR 0.64 [0.49, 0.83]). Infectious Disease (ID) consultation was the largest positive predictor of OT trial across all patients (RR 1.34 [1.14, 1.57]). Allergy/Immunology consultation was the single most important predictor of OT trial among patients reporting penicillin allergy (RR 2.33 [1.44, 3.77]). Of 440 patients, 391 (89%) completed AT, with ID consultation the largest positive predictor of the outcome (RR 1.28 [1.15, 1.43]). Conclusions Nearly 25% of patients with MSSA bacteremia did not receive OT trial and about 10% did not receive AT completion. Reported penicillin allergy reduced, and ID consult increased, the likelihood of OT. Allergy evaluation, coupled with ID consultation, may improve outcomes in MSSA bacteremic patients. PMID:27438379

The Impact of Reporting a Prior Penicillin Allergy on the Treatment of Methicillin-Sensitive Staphylococcus aureus Bacteremia.

PubMed

Blumenthal, Kimberly G; Shenoy, Erica S; Huang, Mingshu; Kuhlen, James L; Ware, Winston A; Parker, Robert A; Walensky, Rochelle P

2016-01-01

Methicillin-sensitive Staphylococcus aureus (MSSA) bacteremia is a morbid infection with mortality benefit from receipt of parenteral β-lactam therapy. A substantial portion of MSSA bacteremia patients report penicillin allergy, but infrequently have true allergy. To determine the frequency and predictors of optimal and adequate therapy in patients with MSSA bacteremia. Retrospective cohort. Adult inpatients with MSSA bacteremia, January 2009 through October 2013. The primary measure was a trial of optimal therapy (OT), defined as ≥3 inpatient days or discharge on any first-line agents (nafcillin, oxacillin, cefazolin, or penicillin G, if susceptible). The secondary measure was completion of adequate therapy (AT), defined as ≥10 inpatient days or discharge on an agent appropriate for MSSA bacteremia. Data were electronically gathered with key variables manually validated through chart review. Log-binomial regression models were used to determine the frequency and predictors of outcomes. Of 456 patients, 346 (76%) received a trial of OT. Patients reporting penicillin allergy (13%) were less likely to receive OT trial than those without penicillin allergy (47% vs. 80%, p <0.001). Adjusting for other factors, penicillin allergy was the largest negative predictor of OT trial (RR 0.64 [0.49, 0.83]). Infectious Disease (ID) consultation was the largest positive predictor of OT trial across all patients (RR 1.34 [1.14, 1.57]). Allergy/Immunology consultation was the single most important predictor of OT trial among patients reporting penicillin allergy (RR 2.33 [1.44, 3.77]). Of 440 patients, 391 (89%) completed AT, with ID consultation the largest positive predictor of the outcome (RR 1.28 [1.15, 1.43]). Nearly 25% of patients with MSSA bacteremia did not receive OT trial and about 10% did not receive AT completion. Reported penicillin allergy reduced, and ID consult increased, the likelihood of OT. Allergy evaluation, coupled with ID consultation, may improve outcomes in MSSA bacteremic patients.

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.

Tumor Control Outcomes Following Hypofractionated and Single-Dose Stereotactic Image-Guided Intensity-Modulated Radiotherapy for Extracranial Metastases from Renal Cell Carcinoma

PubMed Central

Zelefsky, Michael J; Greco, Carlo; Motzer, Robert; Magsanoc, Juan Martin; Pei, Xin; Lovelock, Michael; Mechalakos, Jim; Zatcky, Joan; Fuks, Zvi; Yamada, Yoshiya

2014-01-01

Purpose To report tumor local progression-free outcomes following treatment with single-dose image-guided intensity-modulated radiotherapy (SD-IGRT) and hypofractionated regimens for extracranial metastases from renal cell primary tumors. Methods and Materials Between 2004 and 2010, a total of 105 lesions from renal cell carcinomas were treated with either SD-IGRT to prescription doses of 18–24 Gy (median, 24 Gy) or hypofractionation (3 or 5 fractions) with prescription doses ranging between 20 and 30 Gy. The median follow-up was 12 months (range, 1–48 months). Results The overall 3-year actuarial local progression-free survival (LPFS) for all lesions was 44%. The 3-year LPFS for those who received high single-dose (24 Gy; n = 45), low single-dose (< 24 Gy; n = 14), and hypofractionation regimens (n = 46) were 88%, 21%, and 17%, respectively (high single dose versus low single dose, p = 0.001; high single dose versus hypofractionation, p < 0.001). Multivariate analysis revealed the following variables as significant predictors of improved LPFS: dose of 24 Gy compared with lower dose (p = 0.009), and single dose versus hypofractionation (p = 0.008). Conclusion High-dose SD-IGRT is a non-invasive procedure resulting in high probability of local tumor control for metastatic renal cell cancers, generally considered radioresistant according to classical radiobiological ranking. PMID:21596489

Therapeutic profile of single-fraction radiosurgery of vestibular schwannoma: unrelated malignancy predicts tumor control

PubMed Central

Wowra, Berndt; Muacevic, Alexander; Fürweger, Christoph; Schichor, Christian; Tonn, Jörg-Christian

2012-01-01

Radiosurgery has become an accepted treatment option for vestibular schwannomas. Nevertheless, predictors of tumor control and treatment toxicity in current radiosurgery of vestibular schwannomas are not well understood. To generate new information on predictors of tumor control and cranial nerve toxicity of single-fraction radiosurgery of vestibular schwannomas, we conducted a single-institution long-term observational study of radiosurgery for sporadic vestibular schwannomas. Minimum follow-up was 3 years. Investigated as potential predictors of tumor control and cranial nerve toxicity were treatment technology; tumor resection preceding radiosurgery; tumor size; gender; patient age; history of cancer, vascular disease, or metabolic disease; tumor volume; radiosurgical prescription dose; and isodose line. Three hundred eighty-six patients met inclusion criteria. Treatment failure was observed in 27 patients. History of unrelated cancer (strongest predictor) and prescription dose significantly predicted tumor control. The cumulative incidence of treatment failure was 30% after 6.5 years in patients with unrelated malignancy and 10% after ≥15 years in patients without such cancer (P < .02). Tumor volume was the only predictor of trigeminal neuropathy (observed in 6 patients). No predictor of facial nerve toxicity was found. On the House and Brackmann scale, 1 patient had a permanent one-level drop and 7 a transient drop of 1 to 3 levels. Serviceable hearing was preserved in 75.1%. Tumor hearing before radiosurgery, recurrence, and prescription isodose predicted ototoxicity. Unrelated malignancy is a strong predictor of tumor control. Tumor recurrence predominantly predicts ototoxicity. These findings potentially will aid future clinical decision making in ambiguous cases. PMID:22561798

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.

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.

Characterizing response of total suspended solids and total phosphorus loading to weather and watershed characteristics for rainfall and snowmelt events in agricultural watersheds

USGS Publications Warehouse

Danz, Mari E.; Corsi, Steven; Brooks, Wesley R.; Bannerman, Roger T.

2013-01-01

Understanding the response of total suspended solids (TSS) and total phosphorus (TP) to influential weather and watershed variables is critical in the development of sediment and nutrient reduction plans. In this study, rainfall and snowmelt event loadings of TSS and TP were analyzed for eight agricultural watersheds in Wisconsin, with areas ranging from 14 to 110 km2 and having four to twelve years of data available. The data showed that a small number of rainfall and snowmelt runoff events accounted for the majority of total event loading. The largest 10% of the loading events for each watershed accounted for 73–97% of the total TSS load and 64–88% of the total TP load. More than half of the total annual TSS load was transported during a single event for each watershed at least one of the monitored years. Rainfall and snowmelt events were both influential contributors of TSS and TP loading. TSS loading contributions were greater from rainfall events at five watersheds, from snowmelt events at two watersheds, and nearly equal at one watershed. The TP loading contributions were greater from rainfall events at three watersheds, from snowmelt events at two watersheds and nearly equal at three watersheds. Stepwise multivariate regression models for TSS and TP event loadings were developed separately for rainfall and snowmelt runoff events for each individual watershed and for all watersheds combined by using a suite of precipitation, melt, temperature, seasonality, and watershed characteristics as predictors. All individual models and the combined model for rainfall events resulted in two common predictors as most influential for TSS and TP. These included rainfall depth and the antecedent baseflow. Using these two predictors alone resulted in an R2 greater than 0.7 in all but three individual models and 0.61 or greater for all individual models. The combined model yielded an R2 of 0.66 for TSS and 0.59 for TP. Neither the individual nor the combined models were substantially improved by using additional predictors. Snowmelt event models were statistically significant for individual and combined watershed models, but the model fits were not all as good as those for rainfall events (R2 between 0.19 and 0.87). Predictor selection varied from watershed to watershed, and the common variables that were selected were not always selected in the same order. Influential variables were commonly direct measures of moisture in the watershed such as snowmelt, rainfall + snowmelt, and antecedent baseflow, or measures of potential snowmelt volume in the watershed such as air temperature.

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.

Corporal punishment in rural Colombian families: prevalence, family structure and socio-demographic variables.

PubMed

González, Martha Rocío; Trujillo, Angela; Pereda, Noemí

2014-05-01

To reveal the prevalence of corporal punishment in a rural area of Colombia and its correlates to family structure and other socio-demographic variables. A survey about childrearing and childcare was developed for this study, including a specific question about corporal punishment that was developed based on the Conflict Tactics Scale (CTS). Family structure was categorized as follows, based on previous literature: 'nuclear family,' 'single parent' family, 'extended family,' 'simultaneous family' and 'composed family.' Forty-one percent of the parents surveyed admitted they had used corporal punishment of their children as a disciplinary strategy. The type of family structure, the number of children living at home, the age of the children, the gender of the parent who answered the survey, and the age and gender of the partner were significant predictors of corporal punishment. Family structure is an important variable in the understanding of corporal punishment, especially in regard to nuclear families that have a large number of children and parents who started their parental role early in life. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sexual behavior problems in preteen children: developmental, ecological, and behavioral correlates.

PubMed

Friedrich, W N; Davies, W Hobart; Feher, Eleonora; Wright, John

2003-06-01

A large sample of 2-12 year old children (N = 2311) was studied to determine the relationship between three sexually intrusive behavior items (SIBs) measured by the Child Sexual Behavior Inventory (CSBI) and a range of developmental, ecological, and behavioral correlates. The variables studied included age, gender, race, family income, single parent status, maternal education, family sexual behaviors, physical abuse, sexual abuse, domestic violence, social competence of the child, and three scales from the CBCL (Internalizing, Externalizing, and PTSD). Sexual abuse was not the primary predictor of SIB, but a model incorporating family adversity, modeling of coercive behavior, child behavior, and modeling of sexuality predicted a significant amount of variance.

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.

Pharmacokinetics, pharmacodynamics, and pharmacogenetics of hydroxyurea treatment for children with sickle cell anemia

PubMed Central

Despotovic, Jenny M.; Mortier, Nicole A.; Flanagan, Jonathan M.; He, Jin; Smeltzer, Matthew P.; Kimble, Amy C.; Aygun, Banu; Wu, Song; Howard, Thad; Sparreboom, Alex

2011-01-01

Hydroxyurea therapy has proven laboratory and clinical efficacies for children with sickle cell anemia (SCA). When administered at maximum tolerated dose (MTD), hydroxyurea increases fetal hemoglobin (HbF) to levels ranging from 10% to 40%. However, interpatient variability of percentage of HbF (%HbF) response is high, MTD itself is variable, and accurate predictors of hydroxyurea responses do not currently exist. HUSTLE (NCT00305175) was designed to provide first-dose pharmacokinetics (PK) data for children with SCA initiating hydroxyurea therapy, to investigate pharmacodynamics (PD) parameters, including HbF response and MTD after standardized dose escalation, and to evaluate pharmacogenetics influences on PK and PD parameters. For 87 children with first-dose PK studies, substantial interpatient variability was observed, plus a novel oral absorption phenotype (rapid or slow) that influenced serum hydroxyurea levels and total hydroxyurea exposure. PD responses in 174 subjects were robust and similar to previous cohorts; %HbF at MTD was best predicted by 5 variables, including baseline %HbF, whereas MTD was best predicted by 5 variables, including serum creatinine. Pharmacogenetics analysis showed single nucleotide polymorphisms influencing baseline %HbF, including 5 within BCL11A, but none influencing MTD %HbF or dose. Accurate prediction of hydroxyurea treatment responses for SCA remains a worthy but elusive goal. PMID:21876119

Simple uncertainty propagation for early design phase aircraft sizing

NASA Astrophysics Data System (ADS)

Lenz, Annelise

Many designers and systems analysts are aware of the uncertainty inherent in their aircraft sizing studies; however, few incorporate methods to address and quantify this uncertainty. Many aircraft design studies use semi-empirical predictors based on a historical database and contain uncertainty -- a portion of which can be measured and quantified. In cases where historical information is not available, surrogate models built from higher-fidelity analyses often provide predictors for design studies where the computational cost of directly using the high-fidelity analyses is prohibitive. These surrogate models contain uncertainty, some of which is quantifiable. However, rather than quantifying this uncertainty, many designers merely include a safety factor or design margin in the constraints to account for the variability between the predicted and actual results. This can become problematic if a designer does not estimate the amount of variability correctly, which then can result in either an "over-designed" or "under-designed" aircraft. "Under-designed" and some "over-designed" aircraft will likely require design changes late in the process and will ultimately require more time and money to create; other "over-designed" aircraft concepts may not require design changes, but could end up being more costly than necessary. Including and propagating uncertainty early in the design phase so designers can quantify some of the errors in the predictors could help mitigate the extent of this additional cost. The method proposed here seeks to provide a systematic approach for characterizing a portion of the uncertainties that designers are aware of and propagating it throughout the design process in a procedure that is easy to understand and implement. Using Monte Carlo simulations that sample from quantified distributions will allow a systems analyst to use a carpet plot-like approach to make statements like: "The aircraft is 'P'% likely to weigh 'X' lbs or less, given the uncertainties quantified" without requiring the systems analyst to have substantial knowledge of probabilistic methods. A semi-empirical sizing study of a small single-engine aircraft serves as an example of an initial version of this simple uncertainty propagation. The same approach is also applied to a variable-fidelity concept study using a NASA-developed transonic Hybrid Wing Body aircraft.

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.

Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level

PubMed Central

Peters, Marcell K.; Hemp, Andreas; Appelhans, Tim; Behler, Christina; Classen, Alice; Detsch, Florian; Ensslin, Andreas; Ferger, Stefan W.; Frederiksen, Sara B.; Gebert, Friederike; Haas, Michael; Helbig-Bonitz, Maria; Hemp, Claudia; Kindeketa, William J.; Mwangomo, Ephraim; Ngereza, Christine; Otte, Insa; Röder, Juliane; Rutten, Gemma; Schellenberger Costa, David; Tardanico, Joseph; Zancolli, Giulia; Deckert, Jürgen; Eardley, Connal D.; Peters, Ralph S.; Rödel, Mark-Oliver; Schleuning, Matthias; Ssymank, Axel; Kakengi, Victor; Zhang, Jie; Böhning-Gaese, Katrin; Brandl, Roland; Kalko, Elisabeth K.V.; Kleyer, Michael; Nauss, Thomas; Tschapka, Marco; Fischer, Markus; Steffan-Dewenter, Ingolf

2016-01-01

The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities. PMID:28004657

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).

Outcome of liver transplantation for hepatocellular carcinoma -- a single center experience.

PubMed

Iacob, R; Iacob, S; Gheorghe, L; Gheorghe, C; Hrehoreţ, D; Brașoveanu, V; Croitoru, A; Herlea, V; Popescu, I

2013-01-01

Liver transplantation (LT) is a promising treatment for patients with liver cirrhosis associated with hepatocellular carcinoma (HCC). The aim of our study was to evaluate our experience regarding the clinical and pathological staging of HCC in patients who underwent LT, as well as recurrence free and overall survival. From January 2006 to December 2011, 38 patients with diagnosis of HCC, underwent LT in our Center. Demographic, clinical, imaging and pathologic information were recorded. A Cox proportional hazards survival analysis was performed in order to identify significant predictors of tumor recurrence and patient's death after LT. Eighteen patients (47.4%) in our study group were within Milan criteria. The mean follow-up was 22 months and the recurrence rate of HCC after LT was 13.2%. The 1, 3- year recurrence free survival rates were 85%, 74.3% respectively. The 1 and 3-year overall survival rates were 83.5% and 63.6% respectively. No significant predictor for HCC recurrence was identified in our study group by survival analysis, taking into account 13 different variables. As independent predictors of patient'ss death after LT for HCC however, the presence of diabetes mellitus (p=0.001), presence of more than 3 HCC nodules (p=0.03) and tumor recurrence after LT (p=0.03) were identified by multivariate Cox proportional hazards survival analysis. In our cohort HCC recurrence rate after LT was 13.2%. Diabetes mellitus, presence of more than 3 HCC nodules and HCC recurrence were significant predictors of poor overall survival after LT. Celsius.

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.

A comparison of urinary tract pathology and morbidity in adult populations from endemic and non-endemic zones for urinary schistosomiasis on Unguja Island, Zanzibar

PubMed Central

2009-01-01

Background Renal tract involvement is implicated in both early and late schistosomiasis leading to increased disease burden. Despite there being good estimates of disease burden due to renal tract disease secondary to schistosomiasis at the global level, it is often difficult to translate these estimates into local communities. The aim of this study was to assess the burden of urinary tract pathology and morbidity due to schistosomiasis in Zanzibar and identify reliable clinical predictors of schistosomiasis associated renal disease. Methods A cross-sectional comparison of Ungujan men and women living within either high or low endemic areas for urinary schistosomiasis was conducted. Using urine analysis with reagent strips, parasitological egg counts, portable ultrasonography and a qualitative case-history questionnaire. Data analysis used single and multiple predictor variable logistic regression. Results One hundred and sixty people were examined in the high endemic area (63% women and 37% men), and 101 people in the low endemic area (61% women and 39% men). In the high endemic area, egg-patent schistosomiasis and urinary tract pathology were much more common (p = 1 × 10-3, 8 × 10-6, respectively) in comparison with the low endemic area. Self-reported frothy urine, self-reported haematuria, dysuria and urgency to urinate were associated with urinary tract pathology (p = 1.8 × 10-2, p = 1.1 × 10-4, p = 1.3 × 10-6, p = 1.1 × 10-7, respectively) as assessed by ultrasonography. In a multi-variable logistic regression model, self-reporting of schistosomiasis in the past year, self-reporting of urgency to urinate and having an egg-positive urine sample were all independently associated with detectable urinary tract abnormality, consistent with schistosomiasis-specific disease. Having two or more of these features was moderately sensitive (70%) as a predictor for urinary tract abnormality with high specificity (92%). Conclusion Having two out of urgency to urinate, self reporting of previous infections and detection of eggs in the urine were good proxy predictors of urinary tract abnormality as detected by ultrasound. PMID:19943968

A comparison of urinary tract pathology and morbidity in adult populations from endemic and non-endemic zones for urinary schistosomiasis on Unguja Island, Zanzibar.

PubMed

Lyons, Beatrice; Stothard, Russel; Rollinson, David; Khamis, Simba; Simai, Khamis A; Hunter, Paul R

2009-11-29

Renal tract involvement is implicated in both early and late schistosomiasis leading to increased disease burden. Despite there being good estimates of disease burden due to renal tract disease secondary to schistosomiasis at the global level, it is often difficult to translate these estimates into local communities. The aim of this study was to assess the burden of urinary tract pathology and morbidity due to schistosomiasis in Zanzibar and identify reliable clinical predictors of schistosomiasis associated renal disease. A cross-sectional comparison of Ungujan men and women living within either high or low endemic areas for urinary schistosomiasis was conducted. Using urine analysis with reagent strips, parasitological egg counts, portable ultrasonography and a qualitative case-history questionnaire. Data analysis used single and multiple predictor variable logistic regression. One hundred and sixty people were examined in the high endemic area (63% women and 37% men), and 101 people in the low endemic area (61% women and 39% men). In the high endemic area, egg-patent schistosomiasis and urinary tract pathology were much more common (p = 1 x 10-3, 8 x 10-6, respectively) in comparison with the low endemic area. Self-reported frothy urine, self-reported haematuria, dysuria and urgency to urinate were associated with urinary tract pathology (p = 1.8 x 10-2, p = 1.1 x 10-4, p = 1.3 x 10-6, p = 1.1 x 10-7, respectively) as assessed by ultrasonography. In a multi-variable logistic regression model, self-reporting of schistosomiasis in the past year, self-reporting of urgency to urinate and having an egg-positive urine sample were all independently associated with detectable urinary tract abnormality, consistent with schistosomiasis-specific disease. Having two or more of these features was moderately sensitive (70%) as a predictor for urinary tract abnormality with high specificity (92%). Having two out of urgency to urinate, self reporting of previous infections and detection of eggs in the urine were good proxy predictors of urinary tract abnormality as detected by ultrasound.

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.

Does Intravenous Midazolam Dose Influence the Duration of Recovery Room Stay Following Outpatient Third Molar Surgery?

PubMed

Ettinger, Kyle S; Jacob, Adam K; Viozzi, Christopher F; Van Ess, James M; Fillmore, W Jonathan; Arce, Kevin

2015-12-01

To evaluate the impact of intravenous midazolam dose on the duration of recovery room stay for patients undergoing outpatient third molar surgery. Using a retrospective cohort study design, a sample of patients undergoing outpatient third molar surgery under intravenous sedation at Mayo Clinic from 2010 to 2014 was identified. All patients underwent extraction of all 4 third molars during a single operative procedure and the age range was limited to 14 to 29 years. The primary predictor variable was the total dose of intravenous midazolam administered during sedation. The primary outcome variable was recovery room length of stay (LOS) after completion of surgery. Multiple covariates also abstracted included patient age, gender, American Society of Anesthesiologists (ASA) score, duration of surgical procedure, complexity of surgical procedure, types and dosages of all intravenous medications administered during sedation, and volume of crystalloid fluid administered perioperatively. Univariable and multivariable models were developed to evaluate associations between the primary predictor variable and covariates relative to the primary outcome variable. The study sample was composed of 2,610 patients. Mean age was 18.3 years (SD, 3.0 yr; range, 14 to 29 yr) and gender distribution was 52% female. Mean dosage of midazolam administered was 4.1 mg (SD, 1.1 mg; range, 0.5 to 10.0 mg). Variables predicting shorter LOS at multivariable analysis included older age (P < .001), male gender (P = .004), and administration of larger crystalloid fluid volumes (P < .001). Variables predicting longer LOS included higher ASA score (P < .001), administration of ketamine (P < .001), and administration of ketorolac (P < .001). The dose of midazolam administered during sedation was not found to be significantly associated with prolonged recovery room LOS in univariable or multivariable settings. Dosage of intravenous midazolam does not appear to significantly impact the duration of recovery room stay in the prototypical patients undergoing sedation for outpatient third molar surgery. Copyright © 2015 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Predictors of body appearance cognitive distraction during sexual activity in a sample of men with ED.

PubMed

Pascoal, P M; Raposo, C F; Oliveira, L B

2015-01-01

Our aim is to scrutinize the extent to which aspects of body dissatisfaction and relationship variables predict body appearance cognitive distraction during sexual activity (BACDSA) in a sample of men diagnosed with ED. A total of 65 heterosexual Portuguese participants with ED completed a survey that included questions on socio-demographic data as well as body-related and relationship measures. We used the Global Body Dissatisfaction (GBD) Subscale of the Body Attitudes Test; a version of the Contour Drawing Rating Scale; a single item on partner's opinion perceived about one's body appearance; the Global Measure of Relationship Satisfaction; and the Inclusion of Other in Self Scale. Open questions assessed focus on specific body parts during sexual activity and relationship length. Hierarchical multiple regression indicated that only GBD was a significant predictor of BACDSA, contrary to the relationship measures that showed no significant predictive effect (R(2) =0.47). Our results support the important role of individual factors on explanatory models of sexual dysfunctions, suggesting that interventions addressing individual factors that affect BACDSA may be of preference.

Site-scale disturbance and habitat development best predict an index of amphibian biotic integrity in Ohio shrub and forested wetlands

USGS Publications Warehouse

Micacchion, Mick; Stapanian, Martin A.; Adams, Jean V.

2015-01-01

We determined the best predictors of an index of amphibian biotic integrity calculated from 54 shrub and forested wetlands in Ohio, USA using a two-step sequential holdout validation procedure. We considered 13 variables as predictors: four metrics of wetland condition from the Ohio Rapid Assessment Method (ORAM), a wetland vegetation index of biotic integrity, and eight metrics from a landscape disturbance index. For all iterations, the best model included the single ORAM metric that assesses habitat alteration, substrate disturbance, and habitat development within a wetland. Our results align with results of similar studies that have associated high scores for wetland vegetation indices of biotic integrity with low habitat alteration and substrate disturbance within wetlands. Thus, implementing similar management practices (e.g., not removing downed woody debris, retaining natural morphological features, decreasing nutrient input from surrounding agricultural lands) could concurrently increase ecological integrity of both plant and amphibian communities in a wetland. Further, our results have the unexpected effect of making progress toward a more unifying theory of ecological indices.

Comparative recruitment dynamics of Alewife and Bloater in Lakes Michigan and Huron

USGS Publications Warehouse

Collingsworth, Paris D.; Bunnell, David B.; Madenjian, Charles P.; Riley, Stephen C.

2014-01-01

The predictive power of recruitment models often relies on the identification and quantification of external variables, in addition to stock size. In theory, the identification of climatic, biotic, or demographic influences on reproductive success assists fisheries management by identifying factors that have a direct and reproducible influence on the population dynamics of a target species. More often, models are constructed as one-time studies of a single population whose results are not revisited when further data become available. Here, we present results from stock recruitment models for Alewife Alosa pseudoharengus and Bloater Coregonus hoyi in Lakes Michigan and Huron. The factors that explain variation in Bloater recruitment were remarkably consistent across populations and with previous studies that found Bloater recruitment to be linked to population demographic patterns in Lake Michigan. Conversely, our models were poor predictors of Alewife recruitment in Lake Huron but did show some agreement with previously published models from Lake Michigan. Overall, our results suggest that external predictors of fish recruitment are difficult to discern using traditional fisheries models, and reproducing the results from previous studies may be difficult particularly at low population sizes.

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

PubMed

Jakes, S; Stephens, S D

1987-11-01

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2009-11-01

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

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

PubMed

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

2013-12-01

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

Tumor Control Outcomes After Hypofractionated and Single-Dose Stereotactic Image-Guided Intensity-Modulated Radiotherapy for Extracranial Metastases From Renal Cell Carcinoma

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

Zelefsky, Michael J., E-mail: zelefskm@mskcc.org; Greco, Carlo; Motzer, Robert

2012-04-01

Purpose: To report tumor local progression-free outcomes after treatment with single-dose, image-guided, intensity-modulated radiotherapy and hypofractionated regimens for extracranial metastases from renal cell primary tumors. Patients and Methods: Between 2004 and 2010, 105 lesions from renal cell carcinoma were treated with either single-dose, image-guided, intensity-modulated radiotherapy to a prescription dose of 18-24 Gy (median, 24) or hypofractionation (three or five fractions) with a prescription dose of 20-30 Gy. The median follow-up was 12 months (range, 1-48). Results: The overall 3-year actuarial local progression-free survival for all lesions was 44%. The 3-year local progression-free survival for those who received a highmore » single-dose (24 Gy; n = 45), a low single-dose (<24 Gy; n = 14), or hypofractionation regimens (n = 46) was 88%, 21%, and 17%, respectively (high single dose vs. low single dose, p = .001; high single dose vs. hypofractionation, p < .001). Multivariate analysis revealed the following variables were significant predictors of improved local progression-free survival: 24 Gy dose compared with a lower dose (p = .009) and a single dose vs. hypofractionation (p = .008). Conclusion: High single-dose, image-guided, intensity-modulated radiotherapy is a noninvasive procedure resulting in high probability of local tumor control for metastatic renal cell cancer generally considered radioresistant according to the classic radiobiologic ranking.« less

Modification effects of family economic status and school factors on depression risk of single-father family children in Mid-Taiwan area.

PubMed

Lin, Jin-Ding; Hsieh, Yu-Hsin; Lin, Fu-Gong

2013-05-01

The incidence of single-parent families has increased significantly in Taiwan in recent years. Children born in single-parent families are predisposed to suffering from emotional problems. We aimed to determine if the children of single-parent families are more depressive than children from both-parent families, and to examine the individual and joint effects of various factors on the depression risk. A cross-sectional study was performed to investigate the depression status of elementary school children in MiaoLi County, Taiwan. A total of 881 eligible subjects, including 144 children from single-parent families were recruited from 29 schools. Data for depression-related demographic characteristics, family and school variables were collected. The results show that 27.6% of children from single-father families with depressive symptoms, 15.1% children from single-mother families and 15.3% children from both-parent families with repressive symptoms. This study provides significant evidences that single-father family was one significant predictor for childhood depression and the enhanced effects of socioeconomic status and peer relationship on depression of children from single father families were found up to 4-fold (OR=4.0, 95% CI=1.8-8.5) and 5-fold (OR=5.5, 95% CI=2.3-13.2) risk respectively. The results provide hints to parents and teachers for improving the mental health of children in single-parent families by reducing the occurrence of depression. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

A randomized trial of cognitive behavior therapy and cognitive therapy for children with posttraumatic stress disorder following single-incident trauma: Predictors and outcome at 1-year follow-up.

PubMed

Nixon, Reginald D V; Sterk, Jisca; Pearce, Amanda; Weber, Nathan

2017-07-01

The 1-year outcome and moderators of adjustment for children and youth receiving treatment for posttraumatic stress disorder (PTSD) following single-incident trauma was examined. Children and youth who had experienced single-incident trauma (N = 33; 7-17 years old) were randomly assigned to receive 9 weeks of either trauma-focused cognitive behavior therapy (CBT) or trauma-focused cognitive therapy (without exposure; CT) that was administered to them and their parents individually. Intent-to-treat analyses demonstrated that both groups maintained posttreatment gains in PTSD, depression and general anxiety symptoms reductions at 1-year follow-up, with no children meeting criteria for PTSD. A large proportion of children showed good end-state functioning at follow-up (CBT: 65%; CT: 71%). Contrary to 6-month outcomes, maternal adjustment no longer moderated children's outcome, nor did any other tested variables. The findings confirm the positive longer-term outcomes of using trauma-focused cognitive-behavioral methods for PTSD secondary to single-incident trauma and that these outcomes are not dependent on the use of exposure. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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

NASA Astrophysics Data System (ADS)

Taie Semiromi, M.; Koch, M.

2017-12-01

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

Growth Asymmetry, Head Circumference, and Neurodevelopmental Outcomes in Infants with Single Ventricles.

PubMed

Miller, Thomas A; Zak, Victor; Shrader, Peter; Ravishankar, Chitra; Pemberton, Victoria L; Newburger, Jane W; Shillingford, Amanda J; Dagincourt, Nicholas; Cnota, James F; Lambert, Linda M; Sananes, Renee; Richmond, Marc E; Hsu, Daphne T; Miller, Stephen G; Zyblewski, Sinai C; Williams, Richard V

2016-01-01

To assess the variability in asymmetric growth and its association with neurodevelopment in infants with single ventricle (SV). We analyzed weight-for-age z-score minus head circumference-for-age z-score (HCAZ), relative head growth (cm/kg), along with individual growth variables in subjects prospectively enrolled in the Infant Single Ventricle Trial. Associations between growth indices and scores on the Psychomotor Developmental Index (PDI) and Mental Developmental Index (MDI) of the Bayley Scales of Infant Development-II (BSID-II) at 14 months were assessed. Of the 230 subjects enrolled in the Infant Single Ventricle trial, complete growth data and BSID-II scores were available in 168 (73%). Across the cohort, indices of asymmetric growth varied widely at enrollment and before superior cavopulmonary connection (SCPC) surgery. BSID-II scores were not associated with these asymmetry indices. In bivariate analyses, greater pre-SCPC HCAZ correlated with higher MDI (r = 0.21; P = .006) and PDI (r = 0.38; P < .001) and a greater HCAZ increase from enrollment to pre-SCPC with higher PDI (r = 0.15; P = .049). In multivariable modeling, pre-SCPC HCAZ was an independent predictor of PDI (P = .03), but not MDI. In infants with SV, growth asymmetry was not associated with neurodevelopment at 14 months, but pre-SCPC HCAZ was associated with PDI. Asymmetric growth, important in other high-risk infants, is not a brain-sparing adaptation in infants with SV. Clinicaltrials.gov: NCT00113087. Copyright © 2016 Elsevier Inc. All rights reserved.

The application of musculoskeletal modeling to investigate gender bias in non-contact ACL injury rate during single-leg landings.

PubMed

Ali, Nicholas; Andersen, Michael Skipper; Rasmussen, John; Robertson, D Gordon E; Rouhi, Gholamreza

2014-01-01

The central tenet of this study was to develop, validate and apply various individualised 3D musculoskeletal models of the human body for application to single-leg landings over increasing vertical heights and horizontal distances. While contributing to an understanding of whether gender differences explain the higher rate of non-contact anterior cruciate ligament (ACL) injuries among females, this study also correlated various musculoskeletal variables significantly impacted by gender, height and/or distance and their interactions with two ACL injury-risk predictor variables; peak vertical ground reaction force (VGRF) and peak proximal tibia anterior shear force (PTASF). Kinematic, kinetic and electromyography data of three male and three female subjects were measured. Results revealed no significant gender differences in the musculoskeletal variables tested except peak VGRF (p = 0.039) and hip axial compressive force (p = 0.032). The quadriceps and the gastrocnemius muscle forces had significant correlations with peak PTASF (r = 0.85, p < 0.05 and r = - 0.88, p < 0.05, respectively). Furthermore, hamstring muscle force was significantly correlated with peak VGRF (r = - 0.90, p < 0.05). The ankle flexion angle was significantly correlated with peak PTASF (r = - 0.82, p < 0.05). Our findings indicate that compared to males, females did not exhibit significantly different muscle forces, or ankle, knee and hip flexion angles during single-leg landings that would explain the gender bias in non-contact ACL injury rate. Our results also suggest that higher quadriceps muscle force increases the risk, while higher hamstring and gastrocnemius muscle forces as well as ankle flexion angle reduce the risk of non-contact ACL injury.

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

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

Home oximetry to screen for obstructive sleep apnoea in Down syndrome.

PubMed

Hill, Catherine M; Elphick, Heather E; Farquhar, Michael; Gringras, Paul; Pickering, Ruth M; Kingshott, Ruth N; Martin, Jane; Reynolds, Janine; Joyce, Anna; Gavlak, Johanna C; Evans, Hazel J

2018-05-14

Children with Down syndrome are at high risk of obstructive sleep apnoea (OSA) and screening is recommended. Diagnosis of OSA should be confirmed with multichannel sleep studies. We aimed to determine whether home pulse oximetry (HPO) discriminates children at high risk of OSA, who need further diagnostic multichannel sleep studies. Cross-sectional prospective study in a training sample recruited through three UK centres. Validation sample used single-centre retrospective analysis of clinical data. Children with Down syndrome aged 0.5-6 years. Diagnostic multichannel sleep study and HPO. Sensitivity and specificity of HPO to predict moderate-to-severe OSA. 161/202 children with Down syndrome met quality criteria for inclusion and 25 had OSA. In this training sample, the best HPO parameter predictors of OSA were the delta 12 s index >0.555 (sensitivity 92%, specificity 65%) and 3% oxyhaemoglobin (SpO 2 ) desaturation index (3% ODI)>6.15 dips/hour (sensitivity 92%, specificity 63%). Combining variables (delta 12 s index, 3% ODI, mean and minimum SpO 2 ) achieved sensitivity of 96% but reduced specificity to 52%. All predictors retained or improved sensitivity in a clinical validation sample of 50 children with variable loss of specificity, best overall was the delta 12 s index, a measure of baseline SpO 2 variability (sensitivity 92%; specificity 63%). HPO screening could halve the number of children with Down syndrome needing multichannel sleep studies and reduce the burden on children, families and health services alike. This approach offers a practical universal screening approach for OSA in Down syndrome that is accessible to the non-specialist paediatrician. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Dolichoectatic aneurysms of the vertebrobasilar system: clinical and radiographic factors that predict poor outcomes.

PubMed

Xu, David S; Levitt, Michael R; Kalani, M Yashar S; Rangel-Castilla, Leonardo; Mulholland, Celene B; Abecassis, Isaac J; Morton, Ryan P; Nerva, John D; Siddiqui, Adnan H; Levy, Elad I; Spetzler, Robert F; Albuquerque, Felipe C; McDougall, Cameron G

2018-02-01

OBJECTIVE Fusiform dolichoectatic vertebrobasilar aneurysms are rare, challenging lesions. The natural history of these lesions and medium- and long-term patient outcomes are poorly understood. The authors sought to evaluate patient prognosis after diagnosis of fusiform dolichoectatic vertebrobasilar aneurysms and to identify clinical and radiographic predictors of neurological deterioration. METHODS The authors reviewed multiple, prospectively maintained, single-provider databases at 3 large-volume cerebrovascular centers to obtain data on patients with unruptured, fusiform, basilar artery dolichoectatic aneurysms diagnosed between January 1, 2000, and January 1, 2015. RESULTS A total of 50 patients (33 men, 17 women) were identified; mean clinical follow-up was 50.1 months and mean radiographic follow-up was 32.4 months. At last follow-up, 42% (n = 21) of aneurysms had progressed and 44% (n = 22) of patients had deterioration of their modified Rankin Scale scores. When patients were dichotomized into 2 groups- those who worsened and those who did not-univariate analysis showed 5 variables to be statistically significantly different: sex (p = 0.007), radiographic brainstem compression (p = 0.03), clinical posterior fossa compression (p < 0.001), aneurysmal growth on subsequent imaging (p = 0.001), and surgical therapy (p = 0.006). A binary logistic regression was then created to evaluate these variables. The only variable found to be a statistically significant predictor of clinical worsening was clinical symptoms of posterior fossa compression at presentation (p = 0.01). CONCLUSIONS Fusiform dolichoectatic vertebrobasilar aneurysms carry a poor prognosis, with approximately one-half of the patients deteriorating or experiencing progression of their aneurysm within 5 years. Despite being high risk, intervention-when carefully timed (before neurological decline)-may be beneficial in select patients.

Predictors and moderators of response to cognitive behavioral therapy and medication for the treatment of binge eating disorder.

PubMed

Grilo, Carlos M; Masheb, Robin M; Crosby, Ross D

2012-10-01

To examine predictors and moderators of response to cognitive behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout, and posttreatment. Demographic factors, psychiatric and personality disorder comorbidity, eating disorder psychopathology, psychological features, and 2 subtyping methods (negative affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge eating and 4 secondary dimensional outcomes (binge-eating frequency, eating disorder psychopathology, depression, and body mass index). Mixed-effects models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication only (younger participants had greater binge-eating reductions), whereas several demographic and clinical variables (lower self-esteem, negative affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with vs. 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication only (10% vs. 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: Participants with overvaluation had significantly greater reductions in eating disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative affect. Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013

PubMed Central

Robles, Brenda; Kuo, Tony

2017-01-01

Background Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Methods Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). Results 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. Conclusions These findings have implications for public health practice and community planning in local health jurisdictions. PMID:28087545

[Cost analysis of radiotherapy provided in inpatient setting -  testing potential predictors for a new prospective payment system].

PubMed

Sedo, J; Bláha, M; Pavlík, T; Klika, P; Dušek, L; Büchler, T; Abrahámová, J; Srámek, V; Slampa, P; Komínek, L; Pospíšil, P; Sláma, O; Vyzula, R

2014-01-01

As a part of the development of a new prospective payment model for radiotherapy we analyzed data on costs of care provided by three comprehensive cancer centers in the Czech Republic. Our aim was to find a combination of variables (predictors) which could be used to sort hospitalization cases into groups according to their costs, with each group having the same reimbursement rate. We tested four variables as possible predictors -  number of fractions, stage of disease, radiotherapy technique and diagnostic group. We analyzed 7,440 hospitalization cases treated in three comprehensive cancer centers from 2007 to 2011. We acquired data from the I COP database developed by Institute of Biostatistics and Analyses of Masaryk University in cooperation with oncology centers that contains records from the National Oncological Registry along with data supplied by healthcare providers to insurance companies for the purpose of retrospective reimbursement. When comparing the four variables mentioned above we found that number of fractions and radiotherapy technique were much stronger predictors than the other two variables. Stage of disease did not prove to be a relevant indicator of cost distinction. There were significant differences in costs among diagnostic groups but these were mostly driven by the technique of radiotherapy and the number of fractions. Within the diagnostic groups, the distribution of costs was too heterogeneous for the purpose of the new payment model. The combination of number of fractions and radiotherapy technique appears to be the most appropriate cost predictors to be involved in the prospective payment model proposal. Further analysis is planned to test the predictive value of intention of radiotherapy in order to determine differences in costs between palliative and curative treatment.

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013.

PubMed

Robles, Brenda; Kuo, Tony

2017-01-13

Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. These findings have implications for public health practice and community planning in local health jurisdictions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Prediction of employer-employee relationships from sociodemographic variables and social values in Brunei public and private sector workers.

PubMed

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer-employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer-employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer-employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer-employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer-employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer-employee relationship problems. Other significant social value correlates, but not predictors of employer-employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer-employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions.

Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

PubMed Central

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer–employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions. PMID:28769597

Predictors and Moderators of Response to Cognitive Behavioral Therapy and Medication for the Treatment of Binge Eating Disorder

PubMed Central

Grilo, Carlos. M.; Masheb, Robin M.; Crosby, Ross D.

2012-01-01

Objective To examine predictors and moderators of response to cognitive-behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). Method 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout-, and post-treatment. Demographic factors, psychiatric and personality-disorder co-morbidity, eating-disorder psychopathology, psychological features, and two sub-typing methods (negative-affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge-eating and four secondary dimensional outcomes (binge-eating frequency, eating-disorder psychopathology, depression, and body mass index). Mixed-effects-models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Results Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication-only (younger participants had greater binge-eating reductions) whereas several demographic and clinical variables (lower self-esteem, negative-affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with versus 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication-only (10% versus 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: participants with overvaluation had significantly greater reductions in eating-disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative-affect. Conclusions Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. PMID:22289130

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Predictors of survival in mucosal melanoma of the head and neck.

PubMed

Jethanamest, Daniel; Vila, Peter M; Sikora, Andrew G; Morris, Luc G T

2011-10-01

The head and neck is the most common site of mucosal melanoma, a cancer with poor prognosis. In contrast to cutaneous melanoma, mucosal melanoma of the head and neck (MMHN) is uncommon, with limited data regarding outcomes and prognostic factors drawn from small, single-institution case series. In order to identify factors predictive of survival, we analyzed MMHN outcomes in a large US cohort. MMHN cases (n = 815) diagnosed in the USA between 1973 and 2007 were analyzed in the Surveillance, Epidemiology, and End Results registry, and cause of death was individually determined in 778 (95.5%) cases. Kaplan-Meier survival analysis and Cox proportional hazards regression were used to analyze prognostic variables. Disease-specific survival status was determined in 778 (95.5%) cases. The 5- and 10-year rates of overall survival (OS) were 25.2 and 12.2%; disease-specific survival (DSS), 32.4 and 19.3%. On multivariable analysis, anatomic primary site was an independent predictor of OS and DSS, with tumors in the nasal cavity and oral cavity associated with survival superior to tumors in the nasopharynx and paranasal sinuses. Age > 70 years, tumor size, nodal status, and distant metastasis status were additional independent predictors of poorer survival. In this large cohort of patients with MMHN, we have identified several novel factors robustly predictive of overall and melanoma-specific survival.

Predictors of free flap loss in the head and neck region: A four-year retrospective study with 451 microvascular transplants at a single centre.

PubMed

Mücke, Thomas; Ritschl, Lucas M; Roth, Maximilian; Güll, Florian D; Rau, Andrea; Grill, Sonja; Kesting, Marco R; Wolff, Klaus-Dietrich; Loeffelbein, Denys J

2016-09-01

Microvascular free flaps have become an essential part of reconstructive surgery following head and neck tumour ablation. The authors' aim was to investigate the influence of cardiovascular risk factors, preoperative irradiation, previous operations and metabolically active medication on free flap loss in order to predict patients at risk and to improve their therapy. All patients who underwent reconstructive surgery with microvascular free flaps in the head and neck region between 2009 and 2013 were retrospectively analysed. Uni- and multivariate logistic regressions were performed to determine the association between possible predictor variables for free flap loss. We included 451 patients in our analysis. The overall free flap failure rate was 4.0%. Multivariate regression analysis revealed significantly increased risks of free flap failure depending on prior attempts at microvascular transplants (p < 0.001, OR = 14.21) and length of hospitalisation (p = 0.007, OR = 1.05). With consistently low rates of flap failure, microvascular reconstruction of defects in the head and neck region has proven to be highly reliable, even in patients with comorbidities. The expertise of the operating team seems to remain the main factor affecting flap success. The only discerned independent predictor was previously failed attempts at microvascular reconstruction. Copyright © 2016 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Prevalence of Late Functional Tricuspid Regurgitation in Degenerative Mitral Regurgitation Surgery.

PubMed

Vaturi, Mordehay; Kotler, Tali; Shapira, Yaron; Weisenberg, Daniel; Monakier, Daniel; Sagie, Alexander

2016-03-01

Although significant late tricuspid regurgitation (TR) may develop after surgery for degenerative mitral regurgitation (MR), the use of routine tricuspid annuloplasty is debatable. The study aim was to determine the prevalence and predictors of significant late TR after surgery for degenerative MR. A total of 112 patients who had undergone surgery for degenerative MR without concomitant tricuspid valve repair (average follow up 7.7 ± 4.0 years) was studied retrospectively. The prevalence of post-surgical TR and predictors of progression were determined. The majority of patients (97%) had non-significant TR (less than moderate) prior to surgery, although an overall trend of progression towards significant TR (grades 2 or 3) was noted in 17 patients (p = 0.0006). Of the 18 patients (16%) with late postoperative significant TR, only nine (8%) had severe TR with only a single referral to surgery. New-onset post-surgical atrial fibrillation was more common in patients who developed late significant TR (p = 0.002). Multivariate analysis of the pre-surgery variables, age >65 years and left ventricular dysfunction were shown to be independent predictors of late functional TR. Significant progression in TR after surgery for degenerative MR was rare in this patient cohort. The impact of older age and left ventricular dysfunction at the time of surgery showed a strong association with post-surgical atrial fibrillation.

Bone mineral density in relation to body mass index among young women: a prospective cohort study.

PubMed

Elgán, Carina; Fridlund, Bengt

2006-08-01

To identify important predictors among lifestyle behaviours and physiological factors of bone mineral density (BMD) in relation to body mass index (BMI) among young women over a 2-year period. DESIGN, SAMPLE AND MEASUREMENTS: Data were collected in 1999 and 2001. Healthy young women (n=152) completed a questionnaire. BMD measurements were performed by DEXA in the calcaneus. The women were subdivided into three categories according to baseline BMI. Baseline bodyweight explained 25% of the variability in BMD at follow-up in the BMI<19 category, and high physical activity seemed to hinder BMD development. In the BMI>24 category, a difference in time spent outdoors during winter between baseline and follow-up was the single most important factor for BMD levels. Overweight women with periods of amenorrhoea had lower BMD than overweight women without such periods. Predictors and lifestyle behaviours associated with BMD are likely to be based on women of normal weight. BMI should be considered when advising on physical activity, since high physical activity seems to impair BMD development among underweight young women, possibly due to energy imbalance. Among overweight women, sleep satisfaction is the greatest predictor associated with BMD change and may indicate better bone formation conditions. Energy balance and sleep quality may be prerequisites of bone health and should be considered in prevention.

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

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.

Experiences of forced sex among female patrons of alcohol-serving venues in a South African township.

PubMed

Watt, Melissa H; Sikkema, Kathleen J; Abler, Laurie; Velloza, Jennifer; Eaton, Lisa A; Kalichman, Seth C; Skinner, Donald; Pieterse, Desiree

2015-05-01

South Africa has among the highest rates of forced sex worldwide, and alcohol use has consistently been associated with risk of forced sex in South Africa. However, methodological challenges affect the accuracy of forced sex measurements. This study explored the assessment of forced sex among South African women attending alcohol-serving venues and identified factors associated with reporting recent forced sex. Women (n = 785) were recruited from 12 alcohol-serving venues in a peri-urban township in Cape Town. Brief self-administered surveys included questions about lifetime and recent experiences of forced sex. Surveys included a single question about forced sex and detailed questions about sex by physical force, threats, verbal persuasion, trickery, and spiked drinks. We first compared the single question about forced sex to a composite variable of forced sex as unwanted sex by physical force, threats, or spiked drinks. We then examined potential predictors of recent forced sex (demographics, drinking behavior, relationship to the venue, abuse experiences). The single question about forced sex had low sensitivity (0.38); more than half of the respondents who reported on the detailed questions that they had experienced forced sex by physical force, threats, or spiked drinks reported on the single question item that they had not experienced forced sex. Using our composite variable, 18.6% of women reported lifetime and 10.8% reported recent experiences of forced sex. In our adjusted logistic regression model, recent forced sex using the composite variable was significantly associated with hazardous drinking (OR = 1.92), living farther from the venue (OR = 1.81), recent intimate partner violence (OR = 2.53), and a history of childhood sexual abuse (OR = 4.35). The findings support the need for additional work to refine the assessment of forced sex. Efforts to prevent forced sex should target alcohol-serving venues, where norms and behaviors may present particular risks for women who frequent these settings. © The Author(s) 2014.

Exploration of risk factors predicting outcomes for primary T1 high-grade bladder cancer and validation of the Spanish Urological Club for Oncological Treatment scoring model: Long-term follow-up experience at a single institute.

PubMed

Miyake, Makito; Gotoh, Daisuke; Shimada, Keiji; Tatsumi, Yoshihiro; Nakai, Yasushi; Anai, Satoshi; Torimoto, Kazumasa; Aoki, Katsuya; Tanaka, Nobumichi; Konishi, Noboru; Fujimoto, Kiyohide

2015-06-01

To determine the prognostic factors of primary T1 high-grade bladder cancer and to validate the Spanish Urological Club for Oncological Treatment model in Japanese patients with T1 high-grade bladder cancer treated at a single institution. Records of 106 patients with T1 high-grade bladder cancer treated from 1998 to 2013 were retrospectively reviewed. Variables included various clinicopathological parameters, including lymphovascular invasion and tumor growth pattern at the invasion front. Recurrence-free survival and progression-free survival were analyzed. Multivariate Cox proportional regression analysis was used to verify the prognostic significance of the variables. Scores for recurrence and progression were calculated using the Spanish Urological Club for Oncological Treatment model. Of 106 patients, 44 (42%) had recurrence and 16 (15%) developed progression after a median (interquartile range) follow-up period of 54 months (range 32-81 months). Non-papillary shape was the only independent predictor for recurrence, while broad-based tumor stalk and infiltrative tumor growth pattern at the invasion front were determined to be independent predictors for progression. Stratification of patients according to the number of progression risk factors yielded hazard ratios of 10.1 and 13.1 in patients having one and two risks, respectively, compared with those without any risks. The Spanish Urological Club for Oncological Treatment model successfully stratified our patients with a trend toward different probabilities of recurrence and progression. The results of the present study might be helpful for counseling certain patients towards intensive treatment, such as radical cystectomy and/or platinum-based systemic chemotherapy. In addition, the Spanish Urological Club for Oncological Treatment model might be applicable to Japanese patients with T1 high-grade bladder cancer. © 2015 The Japanese Urological Association.

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

PubMed

Hu, Zhihuang; Liang, Wenhua; Yang, Yunpeng; Keefe, Dorothy; Ma, Yuxiang; Zhao, Yuanyuan; Xue, Cong; Huang, Yan; Zhao, Hongyun; Chen, Likun; Chan, Alexandre; Zhang, Li

2016-01-01

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV.

Personality in remitted major depressive disorder with single and recurrent episodes assessed with the Temperament and Character Inventory.

PubMed

Teraishi, Toshiya; Hori, Hiroaki; Sasayama, Daimei; Matsuo, Junko; Ogawa, Shintaro; Ishida, Ikki; Nagashima, Anna; Kinoshita, Yukiko; Ota, Miho; Hattori, Kotaro; Higuchi, Teruhiko; Kunugi, Hiroshi

2015-01-01

Previous studies consistently reported increased harm avoidance (HA) assessed with the Temperament and Character Inventory (TCI) in patients with major depressive disorder (MDD). However, such findings may have been related with depression severity and number of depressive episodes. The aims of the present study were twofold: to examine TCI personality profile in remitted MDD (DSM-IV) patients and to compare TCI personality between MDD patients with single episode (SGL-MDD) and those with recurrent episodes (REC-MDD) in order to elucidate personality profile associated with recurrence. TCI was administered to 86 outpatients with remitted SGL-MDD (12 male and 17 female patients; mean age 43.2 ± 12.1 years) and REC-MDD (26 male and 31 female patients; 40.3 ± 11.6 years), and 529 healthy controls (225 men and 304 women; 43.4 ± 15.5 years), matched for age, sex and education years. Logistic regression analyses were performed in which single/recurrent episodes of depression were the dependent variable and age, sex, age of onset, family history of psychiatric disease and TCI scores were entered as possible predictors. The remitted MDD patients had significantly higher scores on HA (P < 0.001) and lower scores on self-directedness (P < 0.001), compared with the controls. HA (P = 0.03), its subscore, fatigability (P = 0.03), and family history of psychiatric disease were found to be positive predictors for recurrence. There are differences in personality profile between remitted MDD patients and controls, and between remitted REC-MDD and SGL-MDD patients, suggesting that they are trait markers. HA and fatigability might be useful to assess risk for recurrence of depression. © 2014 The Authors. Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology.

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Predictors and Moderators of Treatment Response in Childhood Anxiety Disorders: Results from the CAMS Trial

PubMed Central

Compton, Scott N.; Peris, Tara S.; Almirall, Daniel; Birmaher, Boris; Sherrill, Joel; Kendall, Phillip C.; March, John S.; Gosch, Elizabeth A.; Ginsburg, Golda S.; Rynn, Moira A.; Piacentini, John C.; McCracken, James T.; Keeton, Courtney P.; Suveg, Cynthia M.; Aschenbrand, Sasha G.; Sakolsky, Dara; Iyengar, Satish; Walkup, John T.; Albano, Anne Marie

2014-01-01

Objective To examine predictors and moderators of treatment outcomes among 488 youth ages 7-17 years (50% female; 74% ≤ 12 years) with DSM-IV diagnoses of separation anxiety disorder, social phobia, or generalized anxiety disorder who were randomly assigned to receive either cognitive behavior therapy (CBT), sertraline (SRT), their combination (COMB), or medication management with pill placebo (PBO) in the Child/Adolescent Anxiety Multimodal Study (CAMS). Method Six classes of predictor and moderator variables (22 variables) were identified from the literature and examined using continuous (Pediatric Anxiety Ratings Scale; PARS) and categorical (Clinical Global Impression Scale-Improvement; CGI-I) outcome measures. Results Three baseline variables predicted better outcomes (independent of treatment condition) on the PARS, including low anxiety severity (as measured by parents and independent evaluators) and caregiver strain. No baseline variables were found to predict week 12 responder status (CGI-I). Participant's principal diagnosis moderated treatment outcomes, but only on the PARS. No baseline variables were found to moderate treatment outcomes on week 12 responder status (CGI-I). Discussion Overall, anxious children responded favorably to CAMS treatments. However, having more severe and impairing anxiety, greater caregiver strain, and a principal diagnosis of social phobia were associated with less favorable outcomes. Clinical implications of these findings are discussed. PMID:24417601

Religiousness as a Predictor of Alcohol Use in High School Students.

ERIC Educational Resources Information Center

Park, Hae-Seong; Bauer, Scott; Oescher, Jeffrey

2001-01-01

Examines the relationship between religiousness and alcohol use of adolescents based on a sample of high school seniors. Results provide support for examining religiousness variables as predictors of alcohol use patterns of adolescents. (Contains 16 references and 4 tables.) (GCP)

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

Baseline Predictors for Success Following Strategy-Based Cognitive Remediation Group Training in Schizophrenia.

PubMed

Farreny, Aida; Aguado, Jaume; Corbera, Silvia; Ochoa, Susana; Huerta-Ramos, Elena; Usall, Judith

2016-08-01

Our aim was to examine predictive variables associated with the improvement in cognitive, clinical, and functional outcomes after outpatient participation in REPYFLEC strategy-based Cognitive Remediation (CR) group training. In addition, we investigated which factors might be associated with some long-lasting effects at 6 months' follow-up. Predictors of improvement after CR were studied in a sample of 29 outpatients with schizophrenia. Partial correlations were computed between targeted variables and outcomes of response to explore significant associations. Subsequently, we built linear regression models for each outcome variable and predictors of improvement. The improvement in negative symptoms at posttreatment was linked to faster performance in the Trail Making Test B. Disorganization and cognitive symptoms were related to changes in executive function at follow-up. Lower levels of positive symptoms were related to durable improvements in life skills. Levels of symptoms and cognition were associated with improvements following CR, but the pattern of resulting associations was nonspecific.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Anthropometry as a predictor of high speed performance.

PubMed

Caruso, J F; Ramey, E; Hastings, L P; Monda, J K; Coday, M A; McLagan, J; Drummond, J

2009-07-01

To assess anthropometry as a predictor of high-speed performance, subjects performed four seated knee- and hip-extension workouts with their left leg on an inertial exercise trainer (Impulse Technologies, Newnan GA). Workouts, done exclusively in either the tonic or phasic contractile mode, entailed two one-minute sets separated by a 90-second rest period and yielded three performance variables: peak force, average force and work. Subjects provided the following anthropometric data: height, weight, body mass index, as well as total, upper and lower left leg lengths. Via multiple regression, anthropometry attempted to predict the variance per performance variable. Anthropometry explained a modest (R2=0.27-0.43) yet significant degree of variance from inertial exercise trainer workouts. Anthropometry was a better predictor of peak force variance from phasic workouts, while it accounted for a significant degree of average force and work variance solely from tonic workouts. Future research should identify variables that account for the unexplained variance from high-speed exercise performance.

Predictors of Nursing Students' Performance in a One-Semester Organic and Biochemistry Course

NASA Astrophysics Data System (ADS)

van Lanen, Robert J.; Lockie, Nancy M.; McGannon, Thomas

2000-06-01

In an effort to empower nursing students to successfully persist in chemistry, predictors of success for undergraduate nursing students enrolled in a one-semester organic and biochemistry course were identified. The sample consisted of 308 undergraduate nursing students enrolled in Chemistry 108 (Principles of Organic and Biochemistry) during a period of seven semesters. In this study, Supplemental Instruction (SI) is a nonremedial academic support program offered for Chemistry 108 students. Placement tests in Mathematics, Reading, and English are required of all entering students. The English Placement Test assesses proficiency in analytical reading and writing; the Nelson Denny Reading Test (Form E) assesses the student's understanding of written vocabulary and the mastery of reading comprehension, and the Mathematics Placement Test measures the student's mastery of arithmetic and algebraic calculations. Both demographic and academic variables were examined. For the entire sample, five predictor variables were identified: Mathematics Placement Test score, Chemistry 107 grade (a prerequisite), total number of SI sessions attended, Nelson Denny Reading Test (Form E) score, and age. Predictors for various subpopulations of the sample were also identified. Predictors for students of traditional age were Mathematics Placement Test score, total number of SI sessions attended, and Chemistry 107 grade. The best predictors for continuing education students were Chemistry 107 grade and Nelson Denny Test score.

Predictors of posttreatment drinking outcomes in patients with alcohol dependence.

PubMed

Flórez, Gerardo; Saiz, Pilar A; García-Portilla, Paz; De Cos, Francisco J; Dapía, Sonia; Alvarez, Sandra; Nogueiras, Luis; Bobes, Julio

2015-01-01

This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors influence outcomes up to 1 year after treatment. Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors.

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

Calderon-Garciduenas, L.; Roy-Ocotla, G.

Southwest metropolitan Mexico City (SWMMC) inhabitants have been exposed several hours per day for the last 6 years to photochemical smog, ozone being the most important oxidant pollutant. Subjects exposed to the SWMMC atmosphere develop several histopathological changes in their nasal mucosa: dysplasia is the most significant, affecting 78.72% of adult individuals within 60 or more days of residence in SWMMC. This study was originally designed to explore whether chemical intervention could modify nasal dysplasia, as determined by nasal cytology, in a defined adult population. In a placebo-controlled, randomized, double-blind trial, 177 healthy male subjects were divided into 5 groupsmore » to whom 5000 IU of vitamin A, 100 IU of vitamin E, a combination of vitamins A and E (5000 IU + 100 IU), 16 mg of beta-carotene, or placebo were administered daily for 4 months. Sixteen clinical and cytological variables were monitored. No effect on dysplasia was seen at the end of the 4-month trial; however, an apparent reversibility as well as progression of the dysplastic nasal lesions and high correlation coefficients between dysplasia and nasal cytology of polymorphonuclear leukocytes (PMNs; 0.85), squamous metaplasia (SM; 0.50), and nasal mucosa atrophy (NMA; 0.41) were found. A mathematical theoretical nasal dysplasia (tD) predictor equation for SWMMC adult male inhabitants is proposed (tD = 0.85 delta PMNs + 0.50 delta SM + 0.41 delta NMA + 0.98), in which PMNs are the best single dysplasia predictor, and all variables are independent.« less

Data mining: Potential applications in research on nutrition and health.

PubMed

Batterham, Marijka; Neale, Elizabeth; Martin, Allison; Tapsell, Linda

2017-02-01

Data mining enables further insights from nutrition-related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition-related intervention. Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables' importance. CART found that body fat, quality of life using the SF-12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF-12PCS, glucose levels and level of education to be the most significant predictors (P ≤ 0.01). Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research. © 2017 Dietitians Association of Australia.

Post-operative diffusion weighted imaging as a predictor of posterior fossa syndrome permanence in paediatric medulloblastoma.

PubMed

Chua, Felicia H Z; Thien, Ady; Ng, Lee Ping; Seow, Wan Tew; Low, David C Y; Chang, Kenneth T E; Lian, Derrick W Q; Loh, Eva; Low, Sharon Y Y

2017-03-01

Posterior fossa syndrome (PFS) is a serious complication faced by neurosurgeons and their patients, especially in paediatric medulloblastoma patients. The uncertain aetiology of PFS, myriad of cited risk factors and therapeutic challenges make this phenomenon an elusive entity. The primary objective of this study was to identify associative factors related to the development of PFS in medulloblastoma patient post-tumour resection. This is a retrospective study based at a single institution. Patient data and all related information were collected from the hospital records, in accordance to a list of possible risk factors associated with PFS. These included pre-operative tumour volume, hydrocephalus, age, gender, extent of resection, metastasis, ventriculoperitoneal shunt insertion, post-operative meningitis and radiological changes in MRI. Additional variables included molecular and histological subtypes of each patient's medulloblastoma tumour. Statistical analysis was employed to determine evidence of each variable's significance in PFS permanence. A total of 19 patients with appropriately complete data was identified. Initial univariate analysis did not show any statistical significance. However, multivariate analysis for MRI-specific changes reported bilateral DWI restricted diffusion changes involving both right and left sides of the surgical cavity was of statistical significance for PFS permanence. The authors performed a clinical study that evaluated possible risk factors for permanent PFS in paediatric medulloblastoma patients. Analysis of collated results found that post-operative DWI restriction in bilateral regions within the surgical cavity demonstrated statistical significance as a predictor of PFS permanence-a novel finding in the current literature.

P-wave characteristics on routine preoperative electrocardiogram improve prediction of new-onset postoperative atrial fibrillation in cardiac surgery.

PubMed

Wong, Jim K; Lobato, Robert L; Pinesett, Andre; Maxwell, Bryan G; Mora-Mangano, Christina T; Perez, Marco V

2014-12-01

To test the hypothesis that including preoperative electrocardiogram (ECG) characteristics with clinical variables significantly improves the new-onset postoperative atrial fibrillation prediction model. Retrospective analysis. Single-center university hospital. Five hundred twenty-six patients, ≥ 18 years of age, who underwent coronary artery bypass grafting, aortic valve replacement, mitral valve replacement/repair, or a combination of valve surgery and coronary artery bypass grafting requiring cardiopulmonary bypass. Retrospective review of medical records. Baseline characteristics and cardiopulmonary bypass times were collected. Digitally-measured timing and voltages from preoperative electrocardiograms were extracted. Postoperative atrial fibrillation was defined as atrial fibrillation requiring therapeutic intervention. Two hundred eight (39.5%) patients developed postoperative atrial fibrillation. Clinical predictors were age, ejection fraction<55%, history of atrial fibrillation, history of cerebral vascular event, and valvular surgery. Three ECG parameters associated with postoperative atrial fibrillation were observed: Premature atrial contraction, p-wave index, and p-frontal axis. Adding electrocardiogram variables to the prediction model with only clinical predictors significantly improved the area under the receiver operating characteristic curve, from 0.71 to 0.78 (p<0.01). Overall net reclassification improvement was 0.059 (p = 0.09). Among those who developed postoperative atrial fibrillation, the net reclassification improvement was 0.063 (p = 0.03). Several p-wave characteristics are independently associated with postoperative atrial fibrillation. Addition of these parameters improves the postoperative atrial fibrillation prediction model. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Usefulness of the UCSD Performance-based Skills Assessment (UPSA) for Predicting Residential Independence in Patients with Chronic Schizophrenia

PubMed Central

Mausbach, Brent T.; Bowie, Christopher R.; Harvey, Philip D.; Twamley, Elizabeth W.; Goldman, Sherrill R.; Jeste, Dilip V.; Patterson, Thomas L.

2009-01-01

The objective of this study was to examine the sensitivity and specificity of a performance-based measure of functional capacity, the UCSD Performance-Based Skills Assessment (UPSA) for the prediction of independent living status in patients with chronic schizophrenia-related conditions. A sample of 434 adults with schizophrenia or schizoaffective disorder was administered the UPSA and assessed for independent living status. Participants were classified as “independent” if they were living alone in an apartment, house, or single-resident occupancy (e.g., hotel room) and non-independent if they resided in a care facility (e.g., Board-and-Care home, Skilled Nursing Facility). Receiver Operator Characteristic (ROC) curves were calculated with the UPSA and Mattis’ Dementia Rating Scale (DRS) scores as predictor variables and residential independence as the state variable. Of the 434 participants, 99 (23%) were living independently at the time of assessment. The discriminant validity of the UPSA was adequate (ROC area under the curve = 0.74; 95% CI: 0.68–0.79), with greatest dichotomization for the UPSA at a cutoff score of 75 (68% accuracy, 69% sensitivity, 66% specificity), or 80 (68% accuracy, 59% sensitivity, 76% specificity). The UPSA was also a significantly better predictor of living status than was the DRS, based on ROC (z = 2.43, p = .015). The UPSA is a brief measure of functional capacity that predicts the ability of patients with schizophrenia to reside independently in the community. PMID:17303168

Influence of economic and demographic factors on quality of life in renal transplant recipients.

PubMed

Chisholm, Marie A; Spivey, Christina A; Nus, Audrey Van

2007-01-01

The purpose of this study was to determine the influence of annual income, Medicare status, and demographic variables on the health-related quality of life (HQoL) of renal transplant recipients. A cross-sectional survey was mailed to 146 Georgia renal transplant recipients who had functional grafts. Data were collected using the SF-12 Health Survey (version 2), a demographics survey, and 2003 tax documents. One-way ANOVAs and Pearson's R correlations were used to examine relationships between annual income, Medicare status, demographic variables and SF-12 scores. Significant variables were included in stepwise multiple regression analyses. Data from 130 participants (89% response rate) were collected. Recipients with no Medicare coverage had significantly higher scores on the Physical Functioning and Role Physical SF-12 scales (p = 0.005) compared to recipients with Medicare. Annual income was positively correlated with General Health (p < 0.05). Age and race were significant predictors of Vitality (p = 0.004) and Physical Component Summary (p < 0.001) scores. Age, race, and Medicare status were significant predictors of Physical Functioning and Role Physical scores (p < 0.001). Age, annual income, race, and years post-transplant were significant predictors of General Health score (p < 0.001). Age was the sole predictor of Bodily Pain score (p = 0.002), and marital status was the sole predictor of Social Functioning score (p = 0.005). Interventions designed to offset financial barriers may be needed to bolster renal transplant recipients' HQoL.

Moderation analysis with missing data in the predictors.

PubMed

Zhang, Qian; Wang, Lijuan

2017-12-01

The most widely used statistical model for conducting moderation analysis is the moderated multiple regression (MMR) model. In MMR modeling, missing data could pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a nonlinear function of the involved variables. In this study, we consider a simple MMR model, where the effect of the focal predictor X on the outcome Y is moderated by a moderator U. The primary interest is to find ways of estimating and testing the moderation effect with the existence of missing data in X. We mainly focus on cases when X is missing completely at random (MCAR) and missing at random (MAR). Three methods are compared: (a) Normal-distribution-based maximum likelihood estimation (NML); (b) Normal-distribution-based multiple imputation (NMI); and (c) Bayesian estimation (BE). Via simulations, we found that NML and NMI could lead to biased estimates of moderation effects under MAR missingness mechanism. The BE method outperformed NMI and NML for MMR modeling with missing data in the focal predictor, missingness depending on the moderator and/or auxiliary variables, and correctly specified distributions for the focal predictor. In addition, more robust BE methods are needed in terms of the distribution mis-specification problem of the focal predictor. An empirical example was used to illustrate the applications of the methods with a simple sensitivity analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predictors of health-related and global quality of life among young adults with difficult-to-treat epilepsy and mild intellectual disability.

PubMed

Endermann, Michael

2013-02-01

This study evaluated predictors of health-related quality of life (HRQOL) and global quality of life (QOL) among young adults with difficult-to-treat epilepsy and mild intellectual disability. One hundred and forty-two persons with epilepsy and cognitive problems were routinely screened on HRQOL, global QOL, and psychological distress four weeks after admission to a time-limited residential rehabilitation unit. The PESOS scales (PE = PErformance, SO = SOciodemographic aspects, S = Subjective evaluation/estimation) on epilepsy-specific problems were administered as measures of HRQOL; a questionnaire on life satisfaction and an item on overall QOL were used as measures of global QOL. Psychological distress was captured with the Symptom Checklist 90-R. Further data were gained from medical files. Quality-of- life predictors were identified using univariate methods and stepwise regression analyses. Psychological distress was the only predictor of all HRQOL and global QOL parameters. Seizure frequency was a predictor of most HRQOL variables. Other epilepsy variables affected only some HRQOL variables but were not associated with global QOL. Health-related quality of life did not seem to be strongly impaired. Only low correlations were found between HRQOL and global QOL. The notion of psychological distress as the most influential predictor of all QOL measures is in line with most findings on QOL in epilepsy. Former observations of weak associations between HRQOL and global QOL among patients with epilepsy and mild intellectual disability are supported. Thus, interventions to reduce psychological distress, besides epilepsy treatment, seem to be of great importance to improve QOL. Copyright © 2012 Elsevier Inc. All rights reserved.

Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis.

PubMed

Cho, Seoae; Kim, Haseong; Oh, Sohee; Kim, Kyunga; Park, Taesung

2009-12-15

The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.

Correlates of alcohol consumption on heavy drinking occasions of young risky drinkers: event versus personal characteristics.

PubMed

Dietze, Paul; Agius, Paul A; Livingston, Michael; Callinan, Sarah; Jenkinson, Rebecca; Lim, Megan S C; Wright, Cassandra J C; Room, Robin

2017-08-01

Risky single-occasion drinking (RSOD) by young people is a serious public health issue, yet little is known about the specific circumstances of risky drinking occasions. This study examined the independent effects of event- and individual-specific variables on RSOD. Longitudinal cohort study measuring self-reported RSOD and event- and individual-specific variables across two drinking occasions approximately 1 year apart. Metropolitan Melbourne, Australia. A sample of 710 young risky drinkers aged between 18 and 25 years and defined as engaging in risky drinking practices (males: consumed alcohol in excess of 10 Australian Standard Drinks (ASD: 10 g ethanol) in a single occasion in the previous year; females: consumed alcohol in excess of seven ASD for females in a single occasion in the previous year). Random digit-dial telephone landline survey of the most recent heavy drinking occasion and socio-demographic variables. The primary outcome was the log of the total drinks consumed in the most recent heavy drinking occasion. Event-specific (e.g. number of drinking locations) and time-varying (e.g. weekly income) and time-invariant (e.g. sex) individual-specific variables were examined as correlates of total drinks consumed. Changes in event-specific characteristics including the length of the drinking occasion (Likelihood Ratio χ 2 (2) = 24.4, P < 0.001), the number of drinking locations (Wald χ 2 (1)  = 7.6, P = 0.006) and the number of different drink types (Wald χ 2 (1)  = 13.6, P < 0.001) were associated with increases in total drinks consumed, after adjustment for time-invariant and time-variant individual-specific variables such as gender, income level and weekly consumption. Few other effects were noted. Event-specific characteristics are important predictors of the number of drinks consumed during risky single occasion drinking (RSOD) and illustrate the importance of event contexts when considering interventions targeting RSOD. The total number of drinks consumed in a RSOD session appears to rise independently with the duration of the drinking event, the number of drinking locations and the number of different types of beverage consumed. © 2017 Society for the Study of Addiction.

Continuation Power Flow with Variable-Step Variable-Order Nonlinear Predictor

NASA Astrophysics Data System (ADS)

Kojima, Takayuki; Mori, Hiroyuki

This paper proposes a new continuation power flow calculation method for drawing a P-V curve in power systems. The continuation power flow calculation successively evaluates power flow solutions through changing a specified value of the power flow calculation. In recent years, power system operators are quite concerned with voltage instability due to the appearance of deregulated and competitive power markets. The continuation power flow calculation plays an important role to understand the load characteristics in a sense of static voltage instability. In this paper, a new continuation power flow with a variable-step variable-order (VSVO) nonlinear predictor is proposed. The proposed method evaluates optimal predicted points confirming with the feature of P-V curves. The proposed method is successfully applied to IEEE 118-bus and IEEE 300-bus systems.

Pursuit of STEM: Factors shaping degree completion for African American females in STEM

NASA Astrophysics Data System (ADS)

Wilkins, Ashlee N.

The primary purpose of the study was to examine secondary data from the Cooperative Institutional Research Program (CIRP) Freshman and College Senior Surveys to investigate factors shaping degree aspirations for African American female undergraduates partaking in science, technology, engineering, and mathematics (STEM) majors. Hierarchical multiple regression was used to analyze the data and identify relationships between independent variables in relation to the dependent variable. The findings of the study reveal four key variables that were predictive of degree completion for African American females in STEM. Father's education, SAT composite, highest degree planned, and self-perception were positive predictors for females; while independent variable overall sense of community among students remained a negative predictor. Lastly implications for education and recommendations for future research were discussed.

Treatment processes and demographic variables as predictors of dropout from trauma-focused cognitive behavioral therapy (TF-CBT) for youth.

PubMed

Yasinski, Carly; Hayes, Adele M; Alpert, Elizabeth; McCauley, Thomas; Ready, C Beth; Webb, Charles; Deblinger, Esther

2018-05-22

Premature dropout is a significant concern in trauma-focused psychotherapy for youth. Previous studies have primarily examined pre-treatment demographic and symptom-related predictors of dropout, but few consistent findings have been reported. The current study examined demographic, symptom, and in-session process variables as predictors of dropout from Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) for youth. Participants were a diverse sample of Medicaid-eligible youth (ages 7-17; n = 108) and their nonoffending caregivers (n = 86), who received TF-CBT through an effectiveness study in a community setting. In-session process variables were coded from audio-recorded sessions, and these and pre-treatment demographic variables and symptom levels were examined as predictors of dropout prior to receiving an adequate dose of TF-CBT (<7 sessions). Twenty-nine children were classified as dropouts and 79 as completers. Binary logistic regression analyses revealed that higher levels of child and caregiver avoidance expressed during early sessions, as well as greater relationship difficulties between the child and therapist, predicted dropout. Those children who were in foster care during treatment were less likely to drop out than children living with parents or relatives. No other demographic or symptom-related factors predicted dropout. These findings highlight the importance of addressing avoidance and therapeutic relationship difficulties in early sessions of TF-CBT to help reduce dropout, and they have implications for improving efforts to disseminate evidence-based trauma-focused treatments. Copyright © 2018 Elsevier Ltd. All rights reserved.

Impact of marriage on HIV/AIDS risk behaviors among impoverished, at-risk couples: a multilevel latent variable approach.

PubMed

Stein, Judith A; Nyamathi, Adeline; Ullman, Jodie B; Bentler, Peter M

2007-01-01

Studies among normative samples generally demonstrate a positive impact of marriage on health behaviors and other related attitudes. In this study, we examine the impact of marriage on HIV/AIDS risk behaviors and attitudes among impoverished, highly stressed, homeless couples, many with severe substance abuse problems. A multilevel analysis of 368 high-risk sexually intimate married and unmarried heterosexual couples assessed individual and couple-level effects on social support, substance use problems, HIV/AIDS knowledge, perceived HIV/AIDS risk, needle-sharing, condom use, multiple sex partners, and HIV/AIDS testing. More variance was explained in the protective and risk variables by couple-level latent variable predictors than by individual latent variable predictors, although some gender effects were found (e.g., more alcohol problems among men). The couple-level variable of marriage predicted lower perceived risk, less deviant social support, and fewer sex partners but predicted more needle-sharing.

Crisis Management Research Summaries

ERIC Educational Resources Information Center

Brock, Stephen E., Ed.; Zhe, Elizabeth; Torem, Chris; Comeaux, Natashia; Dempsey, Allison

2010-01-01

This article presents a summary of recent crisis management publications. The first research report summarized, "Predictors of PTSD," was a study of predictor variables for responses to the World Trade Center attack. The second paper, "Effective Mental Health Response to Catastrophic Events," looked at effective responses following Hurricane…

Predictors of Immigrant Children's School Achievement: A Comparative Study

ERIC Educational Resources Information Center

Moon, Sung Seek; Kang, Suk-Young; An, Soonok

2009-01-01

This paper examines the predictors and indicators of immigrant children's school achievement, using the two of the most predominant groups of American immigrants (103 Koreans and 100 Mexicans). Regression analyses were conducted to determine which independent variables (acculturation, parenting school involvement, parenting style, parent…

The Use of Case History Studies to Differentiate Potentially Infected from Potentially Noninfected Herds with Reactors to Brucella abortus Antigens

PubMed Central

Martin, S. W.; Gerrow, A. F.

1978-01-01

Data on farm characteristics and the results of the first herd test for brucellosis were collected for 74 reactor and 74 negative herds in Wellington County, Ontario. Each reactor herd was classified as either probably infected or probably not infected using the occurrence of abortions prior to the first herd test or during the testing period, the total number of cattle removed and/or the spread of reactors within the herd as criteria of infection. Statistical techniques were used to select variables which were good “discriminators” between probably infected and noninfected herds. In general, reactor herds were primarily dairy herds and were somewhat larger than negative herds. The presence of only single suspicious reactors on the first test appeared to be a good predictor of lack of infection with Brucella abortus. Among the 37 farms in this category the single reactor was removed from only eight farms and no evidence o fthe spread of infection was observed. The presence of one or more positive reactors on the first herd test appeared to be a good predictor of the presence of infection. Of the 24 farms in this category, evidence of the spread of infection was present in ten farms and seven of these ten farms were eventually depopulated. The brucella milk ring test appeared to be the most effective means of identifying infected herds under the conditions present in Wellington County. PMID:417777

Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices

NASA Astrophysics Data System (ADS)

Finn, Conor; Lizier, Joseph

2018-04-01

What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.

Partisan Politics or Public-Health Need? An empirical analysis of state choice during initial implementation of the Affordable Care Act.

PubMed

Mayer, Martin; Kenter, Robert; Morris, John C

2015-01-01

States' policy decisions regarding the Affordable Care Act (ACA) of 2010 have often been explained as predominantly, if not solely, partisan. Might rival explanations also apply? Using a cross-sectional 50-state regression model, we studied standard political variables coupled with public-health indicators. This work differs from existing research by employing a dependent variable of five additive measures of ACA support, examining the impact of both political and socioeconomic indicators on state policy decisions. Expanding on recent empirical studies with our more nuanced additive index of support measures, we found that same-party control of a state's executive and legislative branches was indeed by far the single best predictor of policy decisions. Public-health indicators, overwhelmed by partisan effect, did not sufficiently explain state policy choice. This result does not allay the concerns that health policy has become synonymous with health politics and that health politics now has little to do with health itself.

The Simulation of Daily Temperature Time Series from GCM Output. Part II: Sensitivity Analysis of an Empirical Transfer Function Methodology.

NASA Astrophysics Data System (ADS)

Winkler, Julie A.; Palutikof, Jean P.; Andresen, Jeffrey A.; Goodess, Clare M.

1997-10-01

Empirical transfer functions have been proposed as a means for `downscaling' simulations from general circulation models (GCMs) to the local scale. However, subjective decisions made during the development of these functions may influence the ensuing climate scenarios. This research evaluated the sensitivity of a selected empirical transfer function methodology to 1) the definition of the seasons for which separate specification equations are derived, 2) adjustments for known departures of the GCM simulations of the predictor variables from observations, 3) the length of the calibration period, 4) the choice of function form, and 5) the choice of predictor variables. A modified version of the Climatological Projection by Model Statistics method was employed to generate control (1 × CO2) and perturbed (2 × CO2) scenarios of daily maximum and minimum temperature for two locations with diverse climates (Alcantarilla, Spain, and Eau Claire, Michigan). The GCM simulations used in the scenario development were from the Canadian Climate Centre second-generation model (CCC GCMII).Variations in the downscaling methodology were found to have a statistically significant impact on the 2 × CO2 climate scenarios, even though the 1 × CO2 scenarios for the different transfer function approaches were often similar. The daily temperature scenarios for Alcantarilla and Eau Claire were most sensitive to the decision to adjust for deficiencies in the GCM simulations, the choice of predictor variables, and the seasonal definitions used to derive the functions (i.e., fixed seasons, floating seasons, or no seasons). The scenarios were less sensitive to the choice of function form (i.e., linear versus nonlinear) and to an increase in the length of the calibration period.The results of Part I, which identified significant departures of the CCC GCMII simulations of two candidate predictor variables from observations, together with those presented here in Part II, 1) illustrate the importance of detailed comparisons of observed and GCM 1 × CO2 series of candidate predictor variables as an initial step in impact analysis, 2) demonstrate that decisions made when developing the transfer functions can have a substantial influence on the 2 × CO2 scenarios and their interpretation, 3) highlight the uncertainty in the appropriate criteria for evaluating transfer function approaches, and 4) suggest that automation of empirical transfer function methodologies is inappropriate because of differences in the performance of transfer functions between sites and because of spatial differences in the GCM's ability to adequately simulate the predictor variables used in the functions.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method

USGS Publications Warehouse

Eng, K.; Milly, P.C.D.; Tasker, Gary D.

2007-01-01

To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis. ?? 2007 ASCE.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss.

PubMed

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. © 2016 S. Karger GmbH, Freiburg.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715

SCD-HeFT: Use of RR Interval Statistics for Long-term Risk Stratification for Arrhythmic Sudden Cardiac Death

PubMed Central

Au-yeung, Wan-tai M.; Reinhall, Per; Poole, Jeanne E.; Anderson, Jill; Johnson, George; Fletcher, Ross D.; Moore, Hans J.; Mark, Daniel B.; Lee, Kerry L.; Bardy, Gust H.

2015-01-01

Background In the SCD-HeFT a significant fraction of the congestive heart failure (CHF) patients ultimately did not die suddenly from arrhythmic causes. CHF patients will benefit from better tools to identify if ICD therapy is needed. Objective To identify predictor variables from baseline SCD-HeFT patients’ RR intervals that correlate to arrhythmic sudden cardiac death (SCD) and mortality and to design an ICD therapy screening test. Methods Ten predictor variables were extracted from pre-randomization Holter data from 475 patients enrolled in the SCD-HeFT ICD arm using novel and traditional heart rate variability methods. All variables were correlated to SCD using Mann Whitney-Wilcoxon test and receiver operating characteristic analysis. ICD therapy screening tests were designed by minimizing the cost of false classifications. Survival analysis, including log-rank test and Cox models, was also performed. Results α1 and α2 from detrended fluctuation analysis, the ratio of low to high frequency power, the number of PVCs per hour and heart rate turbulence slope are all statistically significant for predicting the occurrences of SCD (p<0.001) and survival (log-rank p<0.01). The most powerful multivariate predictor tool using the Cox Proportional Hazards was α2 with a hazard ratio of 0.0465 (95% CI: 0.00528 – 0.409, p<0.01). Conclusion Predictor variables from RR intervals correlate to the occurrences of SCD and distinguish survival among SCD-HeFT ICD patients. We believe SCD prediction models should incorporate Holter based RR interval analysis to refine ICD patient selection especially in removing patients who are unlikely to benefit from ICD therapy. PMID:26096609

Short-term dynamics of indoor and outdoor endotoxin exposure: Case of Santiago, Chile, 2012.

PubMed

Barraza, Francisco; Jorquera, Héctor; Heyer, Johanna; Palma, Wilfredo; Edwards, Ana María; Muñoz, Marcelo; Valdivia, Gonzalo; Montoya, Lupita D

2016-01-01

Indoor and outdoor endotoxin in PM2.5 was measured for the very first time in Santiago, Chile, in spring 2012. Average endotoxin concentrations were 0.099 and 0.094 [EU/m(3)] for indoor (N=44) and outdoor (N=41) samples, respectively; the indoor-outdoor correlation (log-transformed concentrations) was low: R=-0.06, 95% CI: (-0.35 to 0.24), likely owing to outdoor spatial variability. A linear regression model explained 68% of variability in outdoor endotoxins, using as predictors elemental carbon (a proxy of traffic emissions), chlorine (a tracer of marine air masses reaching the city) and relative humidity (a modulator of surface emissions of dust, vegetation and garbage debris). In this study, for the first time a potential source contribution function (PSCF) was applied to outdoor endotoxin measurements. Wind trajectory analysis identified upwind agricultural sources as contributors to the short-term, outdoor endotoxin variability. Our results confirm an association between combustion particles from traffic and outdoor endotoxin concentrations. For indoor endotoxins, a predictive model was developed but it only explained 44% of endotoxin variability; the significant predictors were tracers of indoor PM2.5 dust (Si, Ca), number of external windows and number of hours with internal doors open. Results suggest that short-term indoor endotoxin variability may be driven by household dust/garbage production and handling. This would explain the modest predictive performance of published models that use answers to household surveys as predictors. One feasible alternative is to increase the sampling period so that household features would arise as significant predictors of long-term airborne endotoxin levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

PubMed

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

Predictors of stroke in patients with impaired glucose tolerance: results from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research trial.

PubMed

Preiss, David; Giles, Thomas D; Thomas, Laine E; Sun, Jie-Lena; Haffner, Steven M; Holman, Rury R; Standl, Eberhard; Mazzone, Theodore; Rutten, Guy E; Tognoni, Gianni; Chiang, Fu-Tien; McMurray, John J V; Califf, Robert M

2013-09-01

Risk factors for stroke are well-established in general populations but sparsely studied in individuals with impaired glucose tolerance. We identified predictors of stroke among participants with impaired glucose tolerance in the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial. Cox proportional-hazard regression models were constructed using baseline variables, including the 2 medications studied, valsartan and nateglinide. Among 9306 participants, 237 experienced a stroke over 6.4 years. Predictors of stroke included classical risk factors such as existing cerebrovascular and coronary heart disease, higher pulse pressure, higher low-density lipoprotein cholesterol, older age, and atrial fibrillation. Other factors, including previous venous thromboembolism, higher waist circumference, lower estimated glomerular filtration rate, lower heart rate, and lower body mass index, provided additional important predictive information, yielding a C-index of 0.72. Glycemic measures were not predictive of stroke. Variables associated with stroke were similar in participants with no prior history of cerebrovascular disease at baseline. The most powerful predictors of stroke in patients with impaired glucose tolerance included a combination of established risk factors and novel variables, such as previous venous thromboembolism and elevated waist circumference, allowing moderately effective identification of high-risk individuals.

Predictors of intelligence at the age of 5: family, pregnancy and birth characteristics, postnatal influences, and postnatal growth.

PubMed

Eriksen, Hanne-Lise Falgreen; Kesmodel, Ulrik Schiøler; Underbjerg, Mette; Kilburn, Tina Røndrup; Bertrand, Jacquelyn; Mortensen, Erik Lykke

2013-01-01

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003-2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence - Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child's sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child's head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude.

Preinjury somatization symptoms contribute to clinical recovery after sport-related concussion.

PubMed

Nelson, Lindsay D; Tarima, Sergey; LaRoche, Ashley A; Hammeke, Thomas A; Barr, William B; Guskiewicz, Kevin; Randolph, Christopher; McCrea, Michael A

2016-05-17

To determine the degree to which preinjury and acute postinjury psychosocial and injury-related variables predict symptom duration following sport-related concussion. A total of 2,055 high school and collegiate athletes completed preseason evaluations. Concussed athletes (n = 127) repeated assessments serially (<24 hours and days 8, 15, and 45) postinjury. Cox proportional hazard modeling was used to predict concussive symptom duration (in days). Predictors considered included demographic and history variables; baseline psychological, neurocognitive, and balance functioning; acute injury characteristics; and postinjury clinical measures. Preinjury somatic symptom score (Brief Symptom Inventory-18 somatization scale) was the strongest premorbid predictor of symptom duration. Acute (24-hour) postconcussive symptom burden (Sport Concussion Assessment Tool-3 symptom severity) was the best injury-related predictor of recovery. These 2 predictors were moderately correlated (r = 0.51). Path analyses indicated that the relationship between preinjury somatization symptoms and symptom recovery was mediated by postinjury concussive symptoms. Preinjury somatization symptoms contribute to reported postconcussive symptom recovery via their influence on acute postconcussive symptoms. The findings highlight the relevance of premorbid psychological factors in postconcussive recovery, even in a healthy athlete sample relatively free of psychopathology or medical comorbidities. Future research should elucidate the neurobiopsychosocial mechanisms that explain the role of this individual difference variable in outcome following concussive injury. © 2016 American Academy of Neurology.

An assessment of environmental literacy and analysis of predictors of responsible environmental behavior held by secondary teachers in Hualien County of Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Shih-Jang

The major purpose of this study was to determine the relative contribution of nine variables in predicting teachers' responsible environmental behavior (REB). The theoretic framework of this study was based on the Hines model, the Hungerford and Volk model, and the environmental literacy framework proposed by Environmental Literacy Assessment Consortium. A nine-page instrument was administered by mailed questionnaire to 300 randomly selected secondary teachers in Hualien County of Taiwan with a 78.7% response rate. Correlation and stepwise multiple regression analyses were conducted. The following conclusions were drawn: (1) For all the respondents, all the nine environmental literacy variables were significant correlates of REB. These correlates included: perceived knowledge of environmental action strategies (KNOW; r =.46), intention to act (IA; r =.46), perceived skill in using environmental action strategies (SKILL; r =.45), perceived knowledge of environmental problems and issues (KISSU; r =.34), environmental sensitivity (r =.28), environmental responsibility (r =.27), perceived knowledge of ecology and environmental science (r =.27), locus of control (r =.27), and environmental attitudes (r =.21). (2) When only the nine environmental literacy variables were considered, the most parsimonious set of predictors of REB for all the teachers included: (a) KNOW, (Rsp2 =.2116); (b) IA, (Rsp2 =.0916); and (c) SKILL, (Rsp2 =.0205). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.2559); (b) SKILL (Rsp2.0926); and (c) environmental responsibility (Rsp2 =.0219). For the rural teachers, the most parsimonious set of predictors included: (a) KNOW (Rsp2 =.1872); (b) IA (Rsp2 =.0816); and (c) KISSU (Rsp2 =.0318). (3) When the environmental literacy variables as well as demographic and experience variables were considered, the most parsimonious set of predictors for all the teachers included: (a) KNOW, (Rsp2 =.2834); (b) IA, (Rsp2 =.0696); (c) area of residence, (Rsp2 =.0174); and (d) SKILL, (Rsp2 =.0163). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.3199); (b) SKILL (Rsp2 =.0840); (c) major sources of environmental information (Rsp2 =.0432); and (d) membership in environmental organizations, (Rsp2 =.0240). Implications for environmental education program development and instructional practice were presented. Recommendations for further research were also provided.

Impression management and achievement motivation: Investigating substantive links.

PubMed

Elliot, Andrew J; Aldhobaiban, Nawal; Murayama, Kou; Kobeisy, Ahmed; Gocłowska, Małgorzata A; Khyat, Aber

2018-02-01

In this research, we investigate impression management (IM) as a substantive personality variable by linking it to differentiated achievement motivation constructs, namely achievement motives (workmastery, competitiveness, fear of failure) and achievement goals (mastery-approach, mastery-avoidance, performance-approach, performance-avoidance). Study 1 revealed that IM was a positive predictor of workmastery and a negative predictor of competitiveness (with and without self-deceptive enhancement (SDE) controlled). Studies 2a and 2b revealed that IM was a positive predictor of mastery-approach goals and mastery-avoidance goals (without and, in Study 2b, with SDE controlled). These findings highlight the value of conceptualising and utilising IM as a personality variable in its own right and shed light on the nature of the achievement motive and achievement goal constructs. © 2016 International Union of Psychological Science.

Predictors of Outcomes in Autism Early Intervention: Why Don’t We Know More?

PubMed Central

Vivanti, Giacomo; Prior, Margot; Williams, Katrina; Dissanayake, Cheryl

2014-01-01

Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on “what works for whom and why” in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies. PMID:24999470

Prostate specific antigen density to predict prostate cancer upgrading in a contemporary radical prostatectomy series: a single center experience.

PubMed

Magheli, Ahmed; Hinz, Stefan; Hege, Claudia; Stephan, Carsten; Jung, Klaus; Miller, Kurt; Lein, Michael

2010-01-01

We investigated the value of pretreatment prostate specific antigen density to predict Gleason score upgrading in light of significant changes in grading routine in the last 2 decades. Of 1,061 consecutive men who underwent radical prostatectomy between 1999 and 2004, 843 were eligible for study. Prostate specific antigen density was calculated and a cutoff for highest accuracy to predict Gleason upgrading was determined using ROC curve analysis. The predictive accuracy of prostate specific antigen and prostate specific antigen density to predict Gleason upgrading was evaluated using ROC curve analysis based on predicted probabilities from logistic regression models. Prostate specific antigen and prostate specific antigen density predicted Gleason upgrading on univariate analysis (as continuous variables OR 1.07 and 7.21, each p <0.001) and on multivariate analysis (as continuous variables with prostate specific antigen density adjusted for prostate specific antigen OR 1.07, p <0.001 and OR 4.89, p = 0.037, respectively). When prostate specific antigen density was added to the model including prostate specific antigen and other Gleason upgrading predictors, prostate specific antigen lost its predictive value (OR 1.02, p = 0.423), while prostate specific antigen density remained an independent predictor (OR 4.89, p = 0.037). Prostate specific antigen density was more accurate than prostate specific antigen to predict Gleason upgrading (AUC 0.61 vs 0.57, p = 0.030). Prostate specific antigen density is a significant independent predictor of Gleason upgrading even when accounting for prostate specific antigen. This could be especially important in patients with low risk prostate cancer who seek less invasive therapy such as active surveillance since potentially life threatening disease may be underestimated. Further studies are warranted to help evaluate the role of prostate specific antigen density in Gleason upgrading and its significance for biochemical outcome.

Predictive factors for rebleeding and death in alcoholic cirrhotic patients with acute variceal bleeding: a multivariate analysis.

PubMed

Krige, Jake E J; Kotze, Urda K; Distiller, Greg; Shaw, John M; Bornman, Philippus C

2009-10-01

Bleeding from esophageal varices is a leading cause of death in alcoholic cirrhotic patients. The aim of the present single-center study was to identify risk factors predictive of variceal rebleeding and death within 6 weeks of initial treatment. Univariate and multivariate analyses were performed on 310 prospectively documented alcoholic cirrhotic patients with acute variceal hemorrhage (AVH) who underwent 786 endoscopic variceal injection treatments between January 1984 and December 2006. All injections were administered during the first 6 weeks after the patients were treated for their first variceal bleed. Seventy-five (24.2%) patients experienced a rebleed, 38 within 5 days of the initial treatment and 37 within 6 weeks of their initial treatment. Of the 15 variables studied and included in a multivariate analysis using a logistic regression model, a bilirubin level >51 mmol/l and transfusion of >6 units of blood during the initial hospital admission were predictors of variceal rebleeding within the first 6 weeks. Seventy-seven (24.8%) patients died, 29 (9.3%) within 5 days and 48 (15.4%) between 6 and 42 days after the initial treatment. Stepwise multivariate logistic regression analysis showed that six variables were predictors of death within the first 6 weeks: encephalopathy, ascites, bilirubin level >51 mmol/l, international normalized ratio (INR) >2.3, albumin <25 g/l, and the need for balloon tube tamponade. Survival was influenced by the severity of liver failure, with most deaths occurring in Child-Pugh grade C patients. Patients with AVH and encephalopathy, ascites, bilirubin levels >51 mmol/l, INR >2.3, albumin <25 g/l and who require balloon tube tamponade are at increased risk of dying within the first 6 weeks. Bilirubin levels >51 mmol/l and transfusion of >6 units of blood were predictors of variceal rebleeding.

Spectrum of outcomes following traumatic brain injury-relationship between functional impairment and health-related quality of life.

PubMed

Tsyben, Anastasia; Guilfoyle, Mathew; Timofeev, Ivan; Anwar, Fahim; Allanson, Judith; Outtrim, Joanne; Menon, David; Hutchinson, Peter; Helmy, Adel

2018-01-01

The outcome following traumatic brain injury (TBI) is heterogeneous and poorly defined and physical disability scales like the extended Glasgow Outcome Score (GOSE) while providing valuation information in terms of broad categorisation of outcome are unlikely to capture the full spectrum of deficits. Quality of life questionnaires such as SF-36 are emerging as potential tools to help characterise factors important to patients' recovery. This study assessed the association between physical disability and subjective health rating. The relationship is of value as it may help evaluate the impact of TBI on patients' lives and facilitate the delivery of appropriate neuro-rehabilitation services. A single-centre retrospective study was undertaken to assess the relationship between physical outcome as measured by GOSE and quality of life captured by the SF-36 questionnaire. Cronbach's alpha was calculated for each of the eight SF-36 domains to measure internal consistency of the test. Multivariate analysis of variance was conducted to look at the association between GOSE and the physical (PCS) and mental (MCS) component scores on the SF-36. Finally, we performed a generalised linear mixed model (GLMM) to assess the relative contribution of GOSE score, age at the time of trauma, sex and TBI duration towards MCS and PCS rating. There is a statistically significant difference in the MCS and PCS scores based on patients' GOSE scores. The mean scores of the eight SF-36 domains showed significant association with GOSE. GLMM demonstrated that GOSE was the strongest predictor of PCS and MCS. Age was an important variable in the PCS score while time following trauma was a significant predictor of MCS rating. This study highlights that patients' physical outcome following TBI is a strong predictor of the subjective mental and physical health. Nevertheless, there remains tremendous variability in individual SF-36 scores for each GOSE category, highlighting that additional factors play a role in determining quality of life.

123I-IPPA SPECT for the prediction of enhanced left ventricular function after coronary bypass graft surgery. Multicenter IPPA Viability Trial Investigators. 123I-iodophenylpentadecanoic acid.

PubMed

Verani, M S; Taillefer, R; Iskandrian, A E; Mahmarian, J J; He, Z X; Orlandi, C

2000-08-01

Fatty acids are the prime metabolic substrate for myocardial energy production. Hence, fatty acid imaging may be useful in the assessment of myocardial hibernation. The goal of this prospective, multicenter trial was to assess the use of a fatty acid, 123I-iodophenylpentadecanoic acid (IPPA), to identify viable, hibernating myocardium. Patients (n = 119) with abnormal left ventricular wall motion and a left ventricular ejection fraction (LVEF) < 40% who were already scheduled to undergo coronary artery bypass grafting (CABG) underwent IPPA tomography (rest and 30-min redistribution) and blood-pool radionuclide angiography within 3 d of the scheduled operation. Radionuclide angiography was repeated 6-8 wk after CABG. The study endpoint was a > or =10% increase in LVEF after CABG. The number of IPPA-viable abnormally contracting segments necessary to predict a positive LVEF outcome was determined by receiver operating characteristic (ROC) curves and was included in a logistic regression analysis, together with selected clinical variables. Before CABG, abnormal IPPA tomography findings were seen in 113 of 119 patients (95%), of whom 71 (60%) had redistribution in the 30-min images. The LVEF increased modestly after CABG (from 32% +/- 12% to 36% +/- 8%, P< 0.001).A > or =10% increase in LVEF after CABG occurred in 27 of 119 patients (23%). By ROC curves, the best predictor of a > or =10% increase in LVEF was the presence of > or =7 IPPA-viable segments (accuracy, 72%; confidence interval, 64%-80%). Among clinical and scintigraphic variables, the single most important predictor also was the number of IPPA-viable segments (P = 0.008). The number of IPPA-viable segments added significant incremental value to the best clinical predictor model. Asubstantial increase in LVEF occurs after CABG in only a minority of patients (23%) with depressed preoperative function. The number of IPPA-viable segments is useful in predicting a clinically meaningful increase in LVEF.

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

Risk factors for psychological maladjustment of parents of children with cancer.

PubMed

Hoekstra-Weebers, J E; Jaspers, J P; Kamps, W A; Klip, E C

1999-12-01

To examine risk variables for future, more immediate, and persistent psychological distress of parents of pediatric cancer patients. Parents (n = 128) completed questionnaires at the time of diagnosis (T1) and 12 months later (T2). Multiple regression analyses were performed using the following as predictors: demographics, illness-related variables, other life events, personality, coping styles, and social support. Trait anxiety was the strongest predictor of both fathers' and mothers' future distress. Changes in trait anxiety during the year also accompanied changes in both parents' levels of distress. Additional prospective predictors for fathers were the coping style "social support-seeking" and dissatisfaction with support. Dissatisfaction with support also had short-term effects for fathers. An additional prospective predictor for mothers was the number of pleasant events they had experienced prior to diagnosis, while a short-term effect was found for performance in assertiveness. No predictors for the persistence of distress were found. These results underscore the importance of personality anxiety in predicting parents' risk for adjustment difficulties associated with the experience of cancer in one's child. An additional risk factor for fathers was social support. For mothers, previously experienced life events and the frequency of assertive behavior were additional risk factors.

Predictors of Early Termination in a University Counseling Training Clinic

ERIC Educational Resources Information Center

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

Verbal and Nonverbal Predictors of Spelling Performance

ERIC Educational Resources Information Center

Sadoski, Mark; Willson, Victor L.; Holcomb, Angelia; Boulware-Gooden, Regina

2005-01-01

Verbal and nonverbal predictors of spelling performance in Grades 1-12 were investigated using the national norming data from a standardized spelling test. Verbal variables included number of letters, phonemes, syllables, digraphs, blends, silent markers, r-controlled vowels, and the proportion of grapheme-phoneme correspondence. The nonverbal…

Predictors of Secondary Traumatic Stress among Children's Advocacy Center Forensic Interviewers

ERIC Educational Resources Information Center

Bonach, Kathryn; Heckert, Alex

2012-01-01

This study examined various predictor variables that were hypothesized to impact secondary traumatic stress in forensic interviewers (n = 257) from children's advocacy centers across the United States. Data were examined to investigate the relationship between organizational satisfaction, organizational buffers, and job support with secondary…

Pharmacogenetic Predictors of Methylphenidate Dose-Response in Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Froehlich, Tanya E.; Epstein, Jeffery N.; Nick, Todd G.; Melguizo Castro, Maria S.; Stein, Mark A.; Brinkman, William B.; Graham, Amanda J.; Langberg, Joshua M.; Kahn, Robert S.

2011-01-01

Objective: Because of significant individual variability in attention-deficit/hyperactivity disorder (ADHD) medication response, there is increasing interest in identifying genetic predictors of treatment effects. This study examined the role of four catecholamine-related candidate genes in moderating methylphenidate (MPH) dose-response. Method:…

Relationship between Graphical Device Comprehension and Overall Text Comprehension for Third-Grade Children

ERIC Educational Resources Information Center

Roberts, Kathryn L.; Norman, Rebecca R.; Cocco, Jaime

2015-01-01

This study examined relationships between reading comprehension, known predictors of reading comprehension (i.e., cognitive flexibility, fluency, reading motivation and attitude, vocabulary), and graphical device comprehension. One-hundred fifty-six third graders completed assessments of known predictor variables and an assessment tapping…

A Study of Predictors of Environmental Behaviour using U.S. Samples.

ERIC Educational Resources Information Center

Sia, Archibald P.; And Others

1986-01-01

Reports on a study done with the intentions of determining the relative contribution of eight variables in predicting environmental behavior. Concluded that the major predictors were skill in using environmental action strategies, level of environmental sensitivity, and percieved knowledge of environmental action strategies. (TW)

Predictors of Life Satisfaction in Individuals with Intellectual Disabilities

ERIC Educational Resources Information Center

Miller, S. M.; Chan, F.

2008-01-01

Background: The purpose of this study was to examine factors that predict life satisfaction in individuals with intellectual disabilities (ID). Two groups of variables were studied: life skills (interpersonal, instrumental and leisure) and higher-order predictors (social support, self-determination and productivity). Method: Fifty-six participants…

Multidisciplinary treatment for traumatized refugees in a naturalistic setting: symptom courses and predictors

PubMed Central

Stammel, Nadine; Knaevelsrud, Christine; Schock, Katrin; Walther, Lena C. S.; Wenk-Ansohn, Mechthild; Böttche, Maria

2017-01-01

ABSTRACT Background: Multidisciplinary treatment approaches are commonly used in specialized psychosocial centres for the treatment of traumatized refugees, but empirical evidence for their efficacy is inconsistent. Objective: In order to obtain more evidence on the development of mental health and well-being of traumatized refugees who receive multidisciplinary treatment, symptom courses of posttraumatic stress disorder (PTSD), anxiety, depression and somatoform symptoms as well as in the subjective quality of life were investigated in the course of a multidisciplinary treatment. In addition, it was analysed if sociodemographic variables were predictors for possible changes in symptomatology and quality of life. Method: N = 76 patients of the outpatient clinic of a psychosocial centre for traumatized refugees receiving regular multidisciplinary treatment were surveyed using standardized questionnaires at three measurement points (at the beginning of treatment, and after an average of 7 and 14 months of treatment) in a single-group design. Results: Multilevel analysis showed significant improvements of symptoms of PTSD (p < .001), depression (p < .001), anxiety (p < .001), and somatoform symptoms (p = .002) as well as of the subjective quality of life (p < .001) over time. Among the tested predictors (gender, age, country of origin), age was a significant predictor for the course of somatoform symptoms (p < .05). Younger patients showed greater improvements in symptomatology over time than older ones. Conclusions: The results suggest that the received multidisciplinary treatment had a positive effect on trauma-related symptoms as well as on quality of life of traumatized refugees. There was no indication that sociodemographic characteristics predicted the symptom courses of the patients, except for somatoform symptoms. Younger patients benefitted more from multidisciplinary treatment than older ones. PMID:29163866

Time to Death after Terminal Withdrawal of Mechanical Ventilation: Specific Respiratory and Physiologic Parameters May Inform Physician Predictions.

PubMed

Long, Ann C; Muni, Sarah; Treece, Patsy D; Engelberg, Ruth A; Nielsen, Elizabeth L; Fitzpatrick, Annette L; Curtis, J Randall

2015-12-01

Discussions about withdrawal of life-sustaining therapies often include family members of critically ill patients. These conversations should address essential components of the dying process, including expected time to death after withdrawal. The study objective was to aid physician communication about the dying process by identifying predictors of time to death after terminal withdrawal of mechanical ventilation. We conducted an observational analysis from a single-center, before-after evaluation of an intervention to improve palliative care. We studied 330 patients who died after terminal withdrawal of mechanical ventilation. Predictors included patient demographics, laboratory, respiratory, and physiologic variables, and medication use. The median time to death for the entire cohort was 0.58 hours (interquartile range (IQR) 0.22-2.25 hours) after withdrawal of mechanical ventilation. Using Cox regression, independent predictors of shorter time to death included higher positive end-expiratory pressure (per 1 cm H2O hazard ratio [HR], 1.07; 95% CI 1.04-1.11); higher static pressure (per 1 cm H2O HR, 1.03; 95% CI 1.01-1.04); extubation prior to death (HR, 1.41; 95% CI 1.06-1.86); and presence of diabetes (HR, 1.75; 95% CI 1.25-2.44). Higher noninvasive mean arterial pressure predicted longer time to death (per 1 mmHg HR, 0.98; 95% CI 0.97-0.99). Comorbid illness and key respiratory and physiologic parameters may inform physician predictions of time to death after withdrawal of mechanical ventilation. An understanding of the predictors of time to death may facilitate discussions with family members of dying patients and improve communication about end-of-life care.

Time to Death after Terminal Withdrawal of Mechanical Ventilation: Specific Respiratory and Physiologic Parameters May Inform Physician Predictions

PubMed Central

Muni, Sarah; Treece, Patsy D.; Engelberg, Ruth A.; Nielsen, Elizabeth L.; Fitzpatrick, Annette L.; Curtis, J. Randall

2015-01-01

Abstract Background: Discussions about withdrawal of life-sustaining therapies often include family members of critically ill patients. These conversations should address essential components of the dying process, including expected time to death after withdrawal. Objectives: The study objective was to aid physician communication about the dying process by identifying predictors of time to death after terminal withdrawal of mechanical ventilation. Methods: We conducted an observational analysis from a single-center, before–after evaluation of an intervention to improve palliative care. We studied 330 patients who died after terminal withdrawal of mechanical ventilation. Predictors included patient demographics, laboratory, respiratory, and physiologic variables, and medication use. Results: The median time to death for the entire cohort was 0.58 hours (interquartile range (IQR) 0.22–2.25 hours) after withdrawal of mechanical ventilation. Using Cox regression, independent predictors of shorter time to death included higher positive end-expiratory pressure (per 1 cm H2O hazard ratio [HR], 1.07; 95% CI 1.04–1.11); higher static pressure (per 1 cm H2O HR, 1.03; 95% CI 1.01–1.04); extubation prior to death (HR, 1.41; 95% CI 1.06–1.86); and presence of diabetes (HR, 1.75; 95% CI 1.25–2.44). Higher noninvasive mean arterial pressure predicted longer time to death (per 1 mmHg HR, 0.98; 95% CI 0.97–0.99). Conclusions: Comorbid illness and key respiratory and physiologic parameters may inform physician predictions of time to death after withdrawal of mechanical ventilation. An understanding of the predictors of time to death may facilitate discussions with family members of dying patients and improve communication about end-of-life care. PMID:26555010

Prenatal Sonographic Predictors of Neonatal Coarctation of the Aorta.

PubMed

Anuwutnavin, Sanitra; Satou, Gary; Chang, Ruey-Kang; DeVore, Greggory R; Abuel, Ashley; Sklansky, Mark

2016-11-01

To identify practical prenatal sonographic markers for the postnatal diagnosis of coarctation of the aorta. We reviewed the fetal echocardiograms and postnatal outcomes of fetal cases of suspected coarctation of the aorta seen at a single institution between 2010 and 2014. True- and false-positive cases were compared. Logistic regression analysis was used to determine echocardiographic predictors of coarctation of the aorta. Optimal cutoffs for these markers and a multivariable threshold scoring system were derived to discriminate fetuses with coarctation of the aorta from those without coarctation of the aorta. Among 35 patients with prenatal suspicion of coarctation of the aorta, the diagnosis was confirmed postnatally in 9 neonates (25.7% true-positive rate). Significant predictors identified from multivariate analysis were as follows: Z score for the ascending aorta diameter of -2 or less (P = < .001), Z score for the mitral valve annulus of -2 or less (P= .033), Zscore for the transverse aortic arch diameter of -2 or less (P= .028), and abnormal aortic valve morphologic features (P= .026). Among all variables studied, the ascending aortic Z score had the highest sensitivity (78%) and specificity (92%) for detection of coarctation of the aorta. A multivariable threshold scoring system identified fetuses with coarctation of the aorta with still greater sensitivity (89%) and only mildly decreased specificity (88%). The finding of a diminutive ascending aorta represents a powerful and practical prenatal predictor of neonatal coarctation of the aorta. A multivariable scoring system, including dimensions of the ascending and transverse aortas, mitral valve annulus, and morphologic features of the aortic valve, provides excellent sensitivity and specificity. The use of these practical sonographic markers may improve prenatal detection of coarctation of the aorta. © 2016 by the American Institute of Ultrasound in Medicine.

First- versus second-generation electronic cigarettes: predictors of choice and effects on urge to smoke and withdrawal symptoms.

PubMed

Dawkins, Lynne; Kimber, Catherine; Puwanesarasa, Yasothani; Soar, Kirstie

2015-04-01

To (1) estimate predictors of first- versus second-generation electronic cigarette (e-cigarette) choice; and (2) determine whether a second-generation device was (i) superior for reducing urge to smoke and withdrawal symptoms (WS) and (ii) associated with enhanced positive subjective effects. Mixed-effects experimental design. Phase 1: reason for e-cigarette choice was assessed via questionnaire. Phase 2: participants were allocated randomly to first- or second-generation e-cigarette condition. Urge to smoke and WS were measured before and 10 minutes after taking 10 e-cigarette puffs. University of East London, UK. A total of 97 smokers (mean age 26; standard deviation 8.7; 54% female). Single-item urge to smoke scale to assess craving and the Mood and Physical Symptoms Scale (MPSS) to assess WS. Subjective effects included: satisfaction, hit, 'felt like smoking' and 'would use to stop smoking' (yes versus no response). Equal numbers chose each device, but none of the predictor variables (gender, age, tobacco dependence, previous e-cigarette use) accounted for choice. Only baseline urge to smoke/WS predicted urge to smoke/WS 10 minutes after use (B =0.38; P <0.001 and B =0.53; P <0.001). E-cigarette device was not a significant predictor. Those using the second-generation device were more likely to report satisfaction and use in a quit attempt (χ(2)  = 12.10, P =0.001 and χ(2)  = 5.53, P =0.02). First- and second-generation electronic cigarettes appear to be similarly effective in reducing urges to smoke during abstinence, but second-generation devices appear to be more satisfying to users. © 2014 Society for the Study of Addiction.

Proposal of a clinical response score and predictors of clinical response to 2 years of GH replacement therapy in adult GH deficiency.

PubMed

Schneider, Harald J; Buchfelder, Michael; Wallaschofski, Henri; Luger, Anton; Johannsson, Gudmundur; Kann, Peter H; Mattsson, Anders

2015-12-01

There is no single clinical marker to reliably assess the clinical response to growth hormone replacement therapy (GHRT) in adults with growth hormone deficiency (GHD). The objective of this study was to propose a clinical response score to GHRT in adult GHD and to establish clinical factors that predict clinical response. This was a prospective observational cohort study from the international KIMS database (Pfizer International Metabolic Database). We included 3612 adult patients with GHD for proposing the response score and 844 patients for assessing predictors of response. We propose a clinical response score based on changes in total cholesterol, waist circumference and QoL-AGHDA quality of life measurements after 2 years of GHRT. A score point was added for each quintile of change in each variable, resulting in a sum score ranging from 3 to 15. For clinical response at 2 years, we analysed predictors at baseline and after 6 months using logistic regression analyses. In a baseline prediction model, IGF1, QoL-AGHDA, total cholesterol and waist circumference predicted response, with worse baseline parameters being associated with a favourable response (AUC 0.736). In a combined baseline and 6-month prediction model, baseline QoL-AGHDA, total cholesterol and waist circumference, and 6-month change in waist circumference were significant predictors of response (AUC 0.815). A simple clinical response score might be helpful in evaluating the success of GHRT. The baseline prediction model may aid in the decision to initiate GHRT and the combined prediction model may be helpful in the decision to continue GHRT. © 2015 European Society of Endocrinology.

Prognostic implications of atrial fibrillation in patients undergoing myocardial perfusion single-photon emission computed tomography.

PubMed

Abidov, Aiden; Hachamovitch, Rory; Rozanski, Alan; Hayes, Sean W; Santos, Marcia M; Sciammarella, Maria G; Cohen, Ishac; Gerlach, James; Friedman, John D; Germano, Guido; Berman, Daniel S

2004-09-01

The aim of this research was to determine whether presence of atrial fibrillation (AF) provides incremental prognostic information relative to myocardial perfusion single-photon emission computed tomography (MPS) with respect to risk of cardiac death (CD). The prognostic significance of AF in patients undergoing MPS is not known. A total of 16,048 consecutive patients undergoing MPS were followed-up for a mean of 2.21 +/- 1.15 years for the development of CD. Of those, 384 patients (2.4%) had AF. Cox proportional hazards method was used to compare clinical and perfusion data for the prediction of CD in patients with and without AF. Atrial fibrillation was a significant predictor of CD in patients with normal (1.6% per year vs. 0.4% per year in non-AF patients), mildly abnormal (6.3% per year vs. 1.2% per year), and severely abnormal MPS (6.4% per year vs. 3.7% per year) (p < 0.001 for all). By multivariable analysis, AF patients had worse survival (p = 0.001) even after adjustment for the variables most predictive of CD: age, diabetes, shortness of breath, use of vasodilator stress, rest heart rate, and the nuclear variables. In the 4,239 patients with left ventricular ejection fraction evaluated by gated MPS, AF demonstrated incremental prognostic value not only over clinical and nuclear variables, but also over left ventricular ejection in predicting CD (p = 0.014). The presence of AF independently increases the risk of cardiac events over perfusion and function variables in patients undergoing MPS. Patients with AF have a high risk of CD, even when MPS is only mildly abnormal. Whether patients with AF and mildly abnormal MPS constitute a group more deserving of early referral to cardiac catheterization is a question warranting further study.

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115). Conclusions Our proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules. PMID:23802742

Recovery of Work-Related Stress: Complaint Reduction and Work-Resumption are Relatively Independent Processes.

PubMed

de Vente, Wieke; Kamphuis, Jan Henk; Blonk, Roland W B; Emmelkamp, Paul M G

2015-09-01

The process of recovery from work-related stress, consisting of complaint reduction and work-resumption, is not yet fully understood. The aim of this study was to investigate predictors of complaint reduction and work-resumption, as well as testing complaint reduction as a mediator in the association between predictors and work-resumption. Seventy-one patients on sickness-leave because of work-related stress complaints were followed over a period of 13 months. Predictors comprised personal (demographics, coping, cognitions), work-related (job-characteristics, social support), and illness-related (complaint duration, absence duration) variables. Dependent variables were distress complaints, burnout complaints, and work-resumption. Complaints reduced considerably over time to borderline clinical levels and work-resumption increased to 68% at 13 months. Predictors of stronger reduction of distress complaints were male gender, less working hours, less decision authority, more co-worker support, and shorter absence duration. Predictors of stronger reduction of burnout complaints were male gender, lower age, high education, less avoidant coping, less decision authority, more job security, and more co-worker support. Predictors of work-resumption were lower age and stronger reduction of burnout complaints. No indication for a mediating role of burnout complaints between the predictor age and work-resumption was found. Complaint reduction and work-resumption are relatively independent processes. Symptom reduction is influenced by individual and work-related characteristics, which holds promise for a multidisciplinary treatment approach for work-related stress.

Species-environment relationships and potential for distribution modelling in coastal waters

NASA Astrophysics Data System (ADS)

Snickars, M.; Gullström, M.; Sundblad, G.; Bergström, U.; Downie, A.-L.; Lindegarth, M.; Mattila, J.

2014-01-01

Due to increasing pressure on the marine environment there is a growing need to understand species-environment relationships. To provide background for prioritising among variables (predictors) for use in distribution models, the relevance of predictors for benthic species was reviewed using the coastal Baltic Sea as a case-study area. Significant relationships for three response groups (fish, macroinvertebrates, macrovegetation) and six predictor categories (bottom topography, biotic features, hydrography, wave exposure, substrate and spatiotemporal variability) were extracted from 145 queried peer-reviewed field-studies covering three decades and six subregions. In addition, the occurrence of interaction among predictors was analysed. Hydrography was most often found in significant relationships, had low level of interaction with other predictors, but also had the most non-significant relationships. Depth and wave exposure were important in all subregions and are readily available, increasing their applicability for cross-regional modelling efforts. Otherwise, effort to model species distributions may prove challenging at larger scale as the relevance of predictors differed among both response groups and regions. Fish and hard bottom macrovegetation have the largest modelling potential, as they are structured by a set of predictors that at the same time are accurately mapped. A general importance of biotic features implies that these need to be accounted for in distribution modelling, but the mapping of most biotic features is challenging, which currently lowers the applicability. The presence of interactions suggests that predictive methods allowing for interactive effects are preferable. Detailing these complexities is important for future distribution modelling.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

PubMed

Heddam, Salim; Kisi, Ozgur

2017-07-01

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction

NASA Technical Reports Server (NTRS)

Huikuri, H. V.; Makikallio, T. H.; Peng, C. K.; Goldberger, A. L.; Hintze, U.; Moller, M.

2000-01-01

BACKGROUND: Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. METHODS AND RESULTS: Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Predictors of post-traumatic stress symptoms in Oklahoma City: exposure, social support, peri-traumatic responses.

PubMed

Tucker, P; Pfefferbaum, B; Nixon, S J; Dickson, W

2000-11-01

Eighty-five adults seeking mental health assistance six months after the Oklahoma City bombing were assessed to determine which of three groups of variables (exposure, peri-traumatic responses, and social support) predicted development of post-traumatic stress disorder (PTSD) symptoms. Variables most highly associated with subsequent PTSD symptoms included having been injured (among exposure variables), feeling nervous or afraid (peri-traumatic responses), and responding that counseling helped (support variables). Combining primary predictors in the three areas, PTSD symptoms were more likely to occur in those reporting counseling to help and those feeling nervous or afraid at the time of the bombing. Implications of these findings are discussed for behavioral health administrators and clinicians planning service delivery to groups of victims seeking mental health intervention after terrorist attacks and other disasters.

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

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

Bullying by Definition: An Examination of Definitional Components of Bullying

ERIC Educational Resources Information Center

Goldsmid, Susan; Howie, Pauline

2014-01-01

Lack of definitional consensus remains an important unresolved issue within bullying research. This study examined the ability of definitional variables to predict overall level of victimisation (distress, power inequity, and provocation as predictors) and bullying (intention to harm, power inequity, and provocation as predictors) in 246…

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

ERIC Educational Resources Information Center

Alverson, Charlotte Y.; Yamamoto, Scott H.

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school,…

Oral Reading Fluency in Second Language Reading

ERIC Educational Resources Information Center

Jeon, Eun Hee

2012-01-01

This study investigated the role of oral reading fluency in second language reading. Two hundred and fifty-five high school students in South Korea were assessed on three oral reading fluency (ORF) variables and six other reading predictors. The relationship between ORF and other reading predictors was examined through an exploratory factor…

Predictors of Employment Outcomes for State-Federal Vocational Rehabilitation Consumers with HIV/AIDS

ERIC Educational Resources Information Center

Jung, Youngoh; Schaller, James; Bellini, James

2010-01-01

In this study, the authors investigated the effects of demographic, medical, and vocational rehabilitation service variables on employment outcomes of persons living with HIV/AIDS. Binary logistic regression analyses were conducted to determine predictors of employment outcomes using two groups drawn from Rehabilitation Services Administration…

Predictors of Care-Giver Stress in Families of Preschool-Aged Children with Developmental Disabilities

ERIC Educational Resources Information Center

Plant, K. M.; Sanders, M. R.

2007-01-01

Background: This study examined the predictors, mediators and moderators of parent stress in families of preschool-aged children with developmental disability. Method: One hundred and five mothers of preschool-aged children with developmental disability completed assessment measures addressing the key variables. Results: Analyses demonstrated that…

Using Situational Factors to Predict Types of Prison Violence.

ERIC Educational Resources Information Center

Steinke, Pamela

1991-01-01

Tested situational factors as predictors of types of individual aggressive incidents in male prison population. Categorized incidents of violence by whether occurrence of infraction involved aggressive behavior directed at staff, another inmate, self, or property. Found that situational variables did serve as predictors of these categories of…

Predictors of Confidence and Competence among Early Childhood Interventionists

ERIC Educational Resources Information Center

Bruder, Mary Beth; Dunst, Carl J.; Wilson, Cristina; Stayton, Vicki

2013-01-01

The preservice and in-service predictors of 1,668 Part C early intervention and Part B(619) preschool special practitioners' perceived self-efficacy beliefs are reported. The preservice variables were type of degree (discipline), years of formal postsecondary education, licensure, and participants' judgment of how well their preservice training…

Beyond Health and Wealth: Predictors of Women's Retirement Satisfaction

ERIC Educational Resources Information Center

Price, Christine A.; Balaswamy, Shantha

2009-01-01

Despite empirical support for the positive effects of health and wealth on retirement satisfaction, alternative variables also play a key role in helping to shape women's assessment of retirement. In the present study, we explore personal and psychosocial predictors of women's retirement satisfaction while controlling for financial security and…

What Good Predictors of Marijuana Use Are Good For: A Synthesis of Research.

ERIC Educational Resources Information Center

Derzon, James H.; Lipsey, Mark W.

1999-01-01

Analyzes correlates of marijuana use based on 3,690 effect sizes coded from 86 prospective longitudinal studies. Summarizes findings on strength of relationships for categorizing predictor variables, and implications of these relationships. Findings are relevant for intervention programmers and policymakers since they identify characteristics of…

Predictors of Recidivism to a Juvenile Assessment Center: An Expanded Analysis.

ERIC Educational Resources Information Center

Dembo, Richard; And Others

1996-01-01

Over 5,200 youths processed through a Juvenile Assessment Center during a 20-month period were involved in this study of recidivism predictors. Significant relationships were found between the youths' demographics, dependency referral factors, delinquency referral history variables, and recidivism. Direct implications for service delivery and…

Examining Postsecondary Education Predictors and Participation for Students with Learning Disabilities

ERIC Educational Resources Information Center

Joshi, Gauri S.; Bouck, Emily C.

2017-01-01

Given the history of poor postschool outcomes for students with disabilities, researchers repeatedly sought to demonstrate the links between predictor variables and postschool outcomes for students with disabilities. This secondary data analysis used the National Longitudinal Transition Study-2 to examine the relationship between postsecondary…

A Longitudinal Investigation of Employment among Low-Income Youth: Patterns, Predictors, and Correlates

ERIC Educational Resources Information Center

Purtell, Kelly M.; McLoyd, Vonnie C.

2013-01-01

Drawing on previous research linking patterns of adolescent employment--defined in terms of duration and intensity--to educational and occupational outcomes later in life (Staff & Mortimer, 2008), the present study (a) examined positive social behavior and academic variables as longitudinal predictors of patterns of adolescent employment…

Noncognitive Predictors of Student Athletes' Academic Performance.

ERIC Educational Resources Information Center

Simons, Herbert D.; Van Rheenen, Derek

2000-01-01

Examines the role of four noncognitive variables in predicting academic performance in 200 Division I athletes. Studies the noncognitive variables of athletic-academic commitment, feelings of being exploited, academic self-worth, self-handicapping excuses as well as several background and academic preparation variables. Finds all four noncognitive…

Fear of childbirth and obstetrical events as predictors of postnatal symptoms of depression and post-traumatic stress disorder.

PubMed

Fairbrother, Nichole; Woody, Sheila R

2007-12-01

This prospective study examined psychological and obstetrical predictors of enduring postpartum symptoms of depression and post-traumatic stress disorder. Contrary to prediction, prenatal fear of childbirth did not significantly predict symptoms of depression or post-traumatic stress disorder at one month postpartum, but anxiety sensitivity was an unexpected predictor that merits further investigation. Several obstetrical and neonatal variables significantly predicted symptoms of post-traumatic disorder, but not depression.

Personality variables as predictors of Facebook usage.

PubMed

Caci, Barbara; Cardaci, Maurizio; Tabacchi, Marco E; Scrima, Fabrizio

2014-04-01

This study investigates the role of personality factors as predictors of Facebook usage. Data concerning Facebook usage and personality factors from 654 Facebook users were gathered using a web survey. Using path analysis, the results showed Openness was a predictor of Facebook early adoption, Conscientiousness with sparing use, Extraversion with long sessions and abundant friendships, and Neuroticism with high frequency of sessions. The possible role of Agreeableness in predicting low session frequency and friendships needs further validation.

Predictors of outcomes of psychological treatments for disordered gambling: A systematic review.

PubMed

Merkouris, S S; Thomas, S A; Browning, C J; Dowling, N A

2016-08-01

This systematic review aimed to synthesise the evidence relating to pre-treatment predictors of gambling outcomes following psychological treatment for disordered gambling across multiple time-points (i.e., post-treatment, short-term, medium-term, and long-term). A systematic search from 1990 to 2016 identified 50 articles, from which 11 socio-demographic, 16 gambling-related, 21 psychological/psychosocial, 12 treatment, and no therapist-related variables, were identified. Male gender and low depression levels were the most consistent predictors of successful treatment outcomes across multiple time-points. Likely predictors of successful treatment outcomes also included older age, lower gambling symptom severity, lower levels of gambling behaviours and alcohol use, and higher treatment session attendance. Significant associations, at a minimum of one time-point, were identified between successful treatment outcomes and being employed, ethnicity, no gambling debt, personality traits and being in the action stage of change. Mixed results were identified for treatment goal, while education, income, preferred gambling activity, problem gambling duration, anxiety, any psychiatric comorbidity, psychological distress, substance use, prior gambling treatment and medication use were not significantly associated with treatment outcomes at any time-point. Further research involving consistent treatment outcome frameworks, examination of treatment and therapist predictor variables, and evaluation of predictors across long-term follow-ups is warranted to advance this developing field of research. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Contribution of Vegetation and Landscape Configuration for Predicting Environmental Change Impacts on Iberian Birds

PubMed Central

Triviño, Maria; Thuiller, Wilfried; Cabeza, Mar; Hickler, Thomas; Araújo, Miguel B.

2011-01-01

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26–40% of the cases with BRT, and in 1–18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species. PMID:22216263

Genetic and Environmental Influences on Retinopathy of Prematurity

PubMed Central

Ortega-Molina, J. M.; Anaya-Alaminos, R.; Uberos-Fernández, J.; Solans-Pérez de Larraya, A.; Chaves-Samaniego, M. J.; Salgado-Miranda, A.; Piñar-Molina, R.; Jerez-Calero, A.; García-Serrano, J. L.

2015-01-01

Objective. The goals were to isolate and study the genetic susceptibility to retinopathy of prematurity (ROP), as well as the gene-environment interaction established in this disease. Methods. A retrospective study (2000–2014) was performed about the heritability of retinopathy of prematurity in 257 infants who were born at a gestational age of ≤32 weeks. The ROP was studied and treated by a single pediatric ophthalmologist. A binary logistic regression analysis was completed between the presence or absence of ROP and the predictor variables. Results. Data obtained from 38 monozygotic twins, 66 dizygotic twins, and 153 of simple birth were analyzed. The clinical features of the cohorts of monozygotic and dizygotic twins were not significantly different. Genetic factors represented 72.8% of the variability in the stage of ROP, environmental factors 23.08%, and random factors 4.12%. The environmental variables representing the highest risk of ROP were the number of days of tracheal intubation (p < 0.001), postnatal weight gain (p = 0.001), and development of sepsis (p = 0.0014). Conclusion. The heritability of ROP was found to be 0.73. The environmental factors regulate and modify the expression of the genetic code. PMID:26089603

Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes

Treesearch

Jakob Zscheischler; Simone Fatichi; Sebastian Wolf; Peter D. Blanken; Gil Bohrer; Ken Clark; Ankur R. Desai; David Hollinger; Trevor Keenan; Kimberly A. Novick; Sonia I. Seneviratne

2016-01-01

Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their...

Predictors of Performance in Introductory Finance: Variables within and beyond the Student's Control

ERIC Educational Resources Information Center

Englander, Fred; Wang, Zhaobo; Betz, Kenneth

2015-01-01

This study examined variables that are within and beyond the control of students in explaining variations in performance in an introductory finance course. Regression models were utilized to consider whether the variables within the student's control have a greater impact on course performance relative to the variables beyond the student's…

Patient Characteristics and Patient Behavior as Predictors of Outcome in Cognitive Therapy and Exposure Therapy for Hypochondriasis.

PubMed

Richtberg, Samantha; Jakob, Marion; Höfling, Volkmar; Weck, Florian

2017-06-01

Psychotherapy for hypochondriasis has greatly improved over the last decades and cognitive-behavioral treatments are most promising. However, research on predictors of treatment outcome for hypochondriasis is rare. Possible predictors of treatment outcome in cognitive therapy (CT) and exposure therapy (ET) for hypochondriasis were investigated. Characteristics and behaviors of 75 patients were considered as possible predictors: sociodemographic variables (sex, age, and cohabitation); psychopathology (pretreatment hypochondriacal symptoms, comorbid mental disorders, and levels of depression, anxiety, and somatic symptoms); and patient in-session interpersonal behavior. Severity of pretreatment hypochondriacal symptoms, comorbid mental disorders, and patient in-session interpersonal behavior were significant predictors in multiple hierarchical regression analyses. Interactions between the predictors and the treatment (CT or ET) were not found. In-session interpersonal behavior is an important predictor of outcome. Furthermore, there are no specific contraindications to treating hypochondriasis with CT or ET. © 2016 Wiley Periodicals, Inc.

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel

2010-01-01

Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734

Predicting national suicide numbers with social media data.

PubMed

Won, Hong-Hee; Myung, Woojae; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J; Kim, Doh Kwan

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

Predicting National Suicide Numbers with Social Media Data

PubMed Central

Won, Hong-Hee; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J.

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention. PMID:23630615

Demographic and socioenvironmental predictors of premorbid marijuana use among patients with first-episode psychosis.

PubMed

Pauselli, Luca; Birnbaum, Michael L; Vázquez Jaime, Beatriz Paulina; Paolini, Enrico; Kelley, Mary E; Broussard, Beth; Compton, Michael T

2018-01-31

We identified, in subjects with first-episode psychosis, demographic and socioenvironmental predictors of three variables pertaining to premorbid marijuana use: age at initiation of marijuana use, trajectories of marijuana use in the five years prior to onset of psychosis, and the cumulative "dose" of marijuana intake in that same premorbid period. We enrolled 247 first-episode psychosis patients and collected data on lifetime marijuana/alcohol/tobacco use, age at onset of psychosis, diverse socioenvironmental variables, premorbid adjustment, past traumatic experiences, perceived neighborhood-level social disorder, and cannabis use experiences. Bivariate tests were used to examine associations between the three premorbid marijuana use variables and hypothesized predictors. Regression models determined which variables remained independently significantly associated. Age at initiation of cigarette smoking was linked to earlier initiation, faster escalation, and higher cumulative dose of premorbid marijuana use. During childhood, poorer academic performance was predictive of an earlier age at initiation of marijuana use, while poorer sociability was related to more rapid escalation to daily use and a higher cumulative dose. As expected, experiencing euphoric effects was positively correlated with trajectories and cumulative dose, but having negative experiences was unrelated. Traumatic childhood/adolescent experiences were correlated with rapid escalation and amount of marijuana used, but not with age at initiation of marijuana use. These data expand the very limited literature on predictors of premorbid marijuana use in first-episode psychosis. Given its association with earlier age at onset of psychosis, and poorer outcomes among first-episode patients, prevention and treatment efforts should be further developed. Copyright © 2018 Elsevier B.V. All rights reserved.

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

DOE PAGES

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

2016-04-07

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

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

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

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

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

Psychological Predictors of Visual and Auditory P300 Brain-Computer Interface Performance

PubMed Central

Hammer, Eva M.; Halder, Sebastian; Kleih, Sonja C.; Kübler, Andrea

2018-01-01

Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor “emotional stability” was negatively correlated (Spearman's rho = −0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. “Emotional stability” was also negatively related to auditory P300-BCI performance (Spearman's rho = −0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. “Emotional stability” was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors. PMID:29867319

Predictors of suicidal ideation in chronic pain patients: an exploratory study.

PubMed

Racine, Mélanie; Choinière, Manon; Nielson, Warren R

2014-05-01

To explore whether chronic pain (CP) patients who report suicidal ideation (SI) present a distinctive profile with regard to their sociodemographic characteristics, physical health, psychological well-being, cognitions, and use of antidepressants, illicit drugs, and alcohol for pain relief. Eighty-eight CP patients completed self-administered questionnaires during their intake assessment at 3 pain clinics located in the province of Québec (Canada). Patients reporting active or passive SI on the Beck Depression Inventory were compared with patients reporting no SI. Between-group univariate analyses were performed using profile variables to compare patients with and without SI. Significant variables were then entered into multiple logistic regression analyses to identify significant independent predictors of SI. Twenty-four percent of patients reported having had SI. Unemployed/disabled patients were 6 times more likely to report SI. Poor sleep quality was the only predictor of SI among the physical variables. For psychological well-being, depressive symptoms did not significantly predict SI. However, the poorer the patients perceived their mental health to be the more likely they were to report SI. Pain-related helplessness was the only predictor for SI among the cognitive variables. Patients who had used illicit drugs as a form of pain relief at any time since pain onset were 5 times more likely to report SI. Similar results were obtained for those who were on antidepressants. Some factors associated with SI seem pain specific, whereas others are more generally associated with SI. Better identification and understanding of these factors is essential for the development of targeted suicide prevention programs for at-risk CP patients.

Peripheral neuropathy predicts nuclear gene defect in patients with mitochondrial ophthalmoplegia.

PubMed

Horga, Alejandro; Pitceathly, Robert D S; Blake, Julian C; Woodward, Catherine E; Zapater, Pedro; Fratter, Carl; Mudanohwo, Ese E; Plant, Gordon T; Houlden, Henry; Sweeney, Mary G; Hanna, Michael G; Reilly, Mary M

2014-12-01

Progressive external ophthalmoplegia is a common clinical feature in mitochondrial disease caused by nuclear DNA defects and single, large-scale mitochondrial DNA deletions and is less frequently associated with point mutations of mitochondrial DNA. Peripheral neuropathy is also a frequent manifestation of mitochondrial disease, although its prevalence and characteristics varies considerably among the different syndromes and genetic aetiologies. Based on clinical observations, we systematically investigated whether the presence of peripheral neuropathy could predict the underlying genetic defect in patients with progressive external ophthalmoplegia. We analysed detailed demographic, clinical and neurophysiological data from 116 patients with genetically-defined mitochondrial disease and progressive external ophthalmoplegia. Seventy-eight patients (67%) had a single mitochondrial DNA deletion, 12 (10%) had a point mutation of mitochondrial DNA and 26 (22%) had mutations in either POLG, C10orf2 or RRM2B, or had multiple mitochondrial DNA deletions in muscle without an identified nuclear gene defect. Seventy-seven patients had neurophysiological studies; of these, 16 patients (21%) had a large-fibre peripheral neuropathy. The prevalence of peripheral neuropathy was significantly lower in patients with a single mitochondrial DNA deletion (2%) as compared to those with a point mutation of mitochondrial DNA or with a nuclear DNA defect (44% and 52%, respectively; P<0.001). Univariate analyses revealed significant differences in the distribution of other clinical features between genotypes, including age at disease onset, gender, family history, progressive external ophthalmoplegia at clinical presentation, hearing loss, pigmentary retinopathy and extrapyramidal features. However, binomial logistic regression analysis identified peripheral neuropathy as the only independent predictor associated with a nuclear DNA defect (P=0.002; odds ratio 8.43, 95% confidence interval 2.24-31.76). Multinomial logistic regression analysis identified peripheral neuropathy, family history and hearing loss as significant predictors of the genotype, and the same three variables showed the highest performance in genotype classification in a decision tree analysis. Of these variables, peripheral neuropathy had the highest specificity (91%), negative predictive value (83%) and positive likelihood ratio (5.87) for the diagnosis of a nuclear DNA defect. These results indicate that peripheral neuropathy is a rare finding in patients with single mitochondrial DNA deletions but that it is highly predictive of an underlying nuclear DNA defect. This observation may facilitate the development of diagnostic algorithms. We suggest that nuclear gene testing may enable a more rapid diagnosis and avoid muscle biopsy in patients with progressive external ophthalmoplegia and peripheral neuropathy. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.

Peripheral neuropathy predicts nuclear gene defect in patients with mitochondrial ophthalmoplegia

PubMed Central

Pitceathly, Robert D. S.; Blake, Julian C.; Woodward, Catherine E.; Zapater, Pedro; Fratter, Carl; Mudanohwo, Ese E.; Plant, Gordon T.; Houlden, Henry; Sweeney, Mary G.; Hanna, Michael G.; Reilly, Mary M.

2014-01-01

Progressive external ophthalmoplegia is a common clinical feature in mitochondrial disease caused by nuclear DNA defects and single, large-scale mitochondrial DNA deletions and is less frequently associated with point mutations of mitochondrial DNA. Peripheral neuropathy is also a frequent manifestation of mitochondrial disease, although its prevalence and characteristics varies considerably among the different syndromes and genetic aetiologies. Based on clinical observations, we systematically investigated whether the presence of peripheral neuropathy could predict the underlying genetic defect in patients with progressive external ophthalmoplegia. We analysed detailed demographic, clinical and neurophysiological data from 116 patients with genetically-defined mitochondrial disease and progressive external ophthalmoplegia. Seventy-eight patients (67%) had a single mitochondrial DNA deletion, 12 (10%) had a point mutation of mitochondrial DNA and 26 (22%) had mutations in either POLG, C10orf2 or RRM2B, or had multiple mitochondrial DNA deletions in muscle without an identified nuclear gene defect. Seventy-seven patients had neurophysiological studies; of these, 16 patients (21%) had a large-fibre peripheral neuropathy. The prevalence of peripheral neuropathy was significantly lower in patients with a single mitochondrial DNA deletion (2%) as compared to those with a point mutation of mitochondrial DNA or with a nuclear DNA defect (44% and 52%, respectively; P < 0.001). Univariate analyses revealed significant differences in the distribution of other clinical features between genotypes, including age at disease onset, gender, family history, progressive external ophthalmoplegia at clinical presentation, hearing loss, pigmentary retinopathy and extrapyramidal features. However, binomial logistic regression analysis identified peripheral neuropathy as the only independent predictor associated with a nuclear DNA defect (P = 0.002; odds ratio 8.43, 95% confidence interval 2.24–31.76). Multinomial logistic regression analysis identified peripheral neuropathy, family history and hearing loss as significant predictors of the genotype, and the same three variables showed the highest performance in genotype classification in a decision tree analysis. Of these variables, peripheral neuropathy had the highest specificity (91%), negative predictive value (83%) and positive likelihood ratio (5.87) for the diagnosis of a nuclear DNA defect. These results indicate that peripheral neuropathy is a rare finding in patients with single mitochondrial DNA deletions but that it is highly predictive of an underlying nuclear DNA defect. This observation may facilitate the development of diagnostic algorithms. We suggest that nuclear gene testing may enable a more rapid diagnosis and avoid muscle biopsy in patients with progressive external ophthalmoplegia and peripheral neuropathy. PMID:25281868

Prevalence and Religious Predictors of Healing Prayer Use in the USA: Findings from the Baylor Religion Survey.

PubMed

Levin, Jeff

2016-08-01

Using data from the 2010 Baylor Religion Survey (N = 1714), this study investigates the prevalence and religious predictors of healing prayer use among US adults. Indicators include prayed for self (lifetime prevalence = 78.8 %), prayed for others (87.4 %), asked for prayer (54.1 %), laying-on-of-hands (26.1 %), and participated in a prayer group (53.0 %). Each was regressed onto eight religious measures, and then again controlling for sociodemographic variables and health. While all religious measures had net effects on at least one healing prayer indicator, the one consistent predictor was a four-item scale assessing a loving relationship with God. Higher scores were associated with more frequent healing prayer use according to every measure, after controlling for all other religious variables and covariates.