Journal articles on the topic 'Relevant predictor variables'
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Ruzicka, Mary F. "Predictor Variables of Clergy Pedophiles." Psychological Reports 80, no. 2 (April 1997): 589–90. http://dx.doi.org/10.2466/pr0.1997.80.2.589.
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File data on familial traits, past sexual experience as a victim, and other traits identified in the literature as leading toward pedophilia, were summarized for 10 convicted clergy pedophiles to construct a set of variables possibly useful for screening. Further research is underway to identify trauma in early life and those personality-related variables current studies indicate as relevant.
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Wilson, Claire A., Sarah E. Babcock, and Donald H. Saklofske. "Sinking or Swimming in an Academic Pool: A Study of Resiliency and Student Success in First-Year Undergraduates." Canadian Journal of Higher Education 49, no. 1 (April 21, 2019): 60–84. http://dx.doi.org/10.47678/cjhe.v49i1.188220.
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The transition from high school to post-secondary education presents challenges for students. Many variables have been identified as significant predictors of student achievement. Resiliency, defined as the ability to overcome challenges and adversity, may be particularly relevant during the adjustment to post-secondary education. This study assesses whether resiliency incrementally predicts student success after controlling for additional predictors. Participants were 277 undergraduate students who completed self-reports of academic skills, resiliency, personality variables, emotional intelligence (EI), and perfectionism. Students’ year-end GPA was collected from the university registrar. Hierarchical regression analysis revealed that resiliency, measured by sense of mastery, negatively predicted GPA after controlling for other predictors. The sense of mastery facet of self-efficacy positively predicted GPA; however, the adaptability facet was a significant negative predictor of GPA. Findings suggest that self-efficacy is a salient predictor of academic success, and that strong academic skills may serve as a protective factor for poor adaptability.
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Irwin, Julie R., and Gary H. McClelland. "Negative Consequences of Dichotomizing Continuous Predictor Variables." Journal of Marketing Research 40, no. 3 (August 2003): 366–71. http://dx.doi.org/10.1509/jmkr.40.3.366.19237.
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Marketing researchers frequently split (dichotomize) continuous predictor variables into two groups, as with a median split, before performing data analysis. The practice is prevalent, but its effects are not well understood. In this article, the authors present historical results on the effects of dichotomization of normal predictor variables rederived in a regression context that may be more relevant to marketing researchers. The authors then present new results on the effect of dichotomizing continuous predictor variables with various nonnormal distributions and examine the effects of dichotomization on model specification and fit in multiple regression. The authors conclude that dichotomization has only negative consequences and should be avoided.
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Lopez, Alberto D., Kanisha Patel, Vincent P. Diego, Marcio A. Almeida, John Blangero, Jerry S. Powell, and Tom E. Howard. "On the Role of Hemostasis Variables in Cardiometabolic Outcomes." Blood 138, Supplement 1 (November 5, 2021): 4266. http://dx.doi.org/10.1182/blood-2021-154441.
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Abstract Background: Hemostasis variables represent well-known pathogenic determinants of venous-thromboembolism and are hypothesized to influence risk for cardiometabolic outcomes (CMOs) by effecting susceptibility to vascular inflammation and/or endothelial dysfunction. Methods: We investigated in Mexican American (MA)-participants of the San Antonio Family Study the effects of 20 measured hemostasis variables on CMOs including type-2 diabetes (T2D), impaired-fasting glucose (IFG), insulin resistance (IR), hypertension, obesity based on sex-specific waist circumference (OBWC), low high-density lipoprotein (Low-HDL)-cholesterol, hypertriglyceridemia, and a heart distress (HD) variable comprised of history of heart attack and/or heart surgery. The hemostasis variables included PT, aPTT, TFPI, aPC-ratio, fibrinogen, von Willebrand Factor (VWF), ADAMST-13, D-dimer, fPS, tPS, plasminogen, and factor (F)II, FV, FVII, FVIII, FX, FXI, and FXII of the coagulation system. We used a backward stepwise-regression selection approach to identify the preliminary sets of hemostasis predictors. Results: Our final regression models consisted of the final set of hemostasis variables while accounting for age and sex as covariates. For the T2D, IFG, and HD outcomes, VWF was the only significant hemostasis predictor (p<0.05). In each case, VWF was positively associated with the outcome. The hemostasis variable fibrinogen was a significant positive predictor for the IR, OBWC, and Low-HDL-cholesterol outcomes (p<0.05). For OBWC, FV was an additional significant positive predictor (p<0.05). Finally, no hemostasis variables significantly predicted hypertension or hypertriglyceridemia. Conclusions: Our results demonstrate that, among the MA-population, all measured CMOs-except hypertension and hypertriglyceridemia-are significantly influenced by individual-variability in several hemostasis parameters with most important predictors being VWF and fibrinogen. Keywords: Hemostasis; Venous-thromboembolism; Cardiovascular diseases/disorders; Metabolic syndrome; Inflammation; Endothelial dysfunction; Mexican American; Coagulation factors; von Willebrand Factor; and Fibrinogen Disclosures No relevant conflicts of interest to declare.
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Antoniou, Maria-Christina, Leah Gilbert, Justine Gross, Jean-Benoît Rossel, Céline Julie Fischer Fumeaux, Yvan Vial, and Jardena Jacqueline Puder. "Main Fetal Predictors of Adverse Neonatal Outcomes in Pregnancies with Gestational Diabetes Mellitus." Journal of Clinical Medicine 9, no. 8 (July 28, 2020): 2409. http://dx.doi.org/10.3390/jcm9082409.
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The objectives of this study were to (a) assess the utility of fetal anthropometric variables to predict the most relevant adverse neonatal outcomes in a treated population with gestational diabetes mellitus (GDM) beyond the known impact of maternal anthropometric and metabolic parameters and (b) to identify the most important fetal predictors. A total of 189 patients with GDM were included. The fetal predictors included sonographically assessed fetal weight centile (FWC), FWC > 90% and <10%, and fetal abdominal circumference centile (FACC), FACC > 90% and < 10%, at 29 0/7 to 35 6/7 weeks. Neonatal outcomes comprising neonatal weight centile (NWC), large and small for gestational age (LGA, SGA), hypoglycemia, prematurity, hospitalization for neonatal complication, and (emergency) cesarean section were evaluated. Regression analyses were conducted. Fetal variables predicted anthropometric neonatal outcomes, prematurity, cesarean section and emergency cesarean section. These associations were independent of maternal anthropometric and metabolic predictors, with the exception of cesarean section. FWC was the most significant predictor for NWC, LGA and SGA, while FACC was the most significant predictor for prematurity and FACC > 90% for emergency cesarean section. In women with GDM, third-trimester fetal anthropometric parameters have an important role in predicting adverse neonatal outcomes beyond the impact of maternal predictors.
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Messner, Jakob W., Georg J. Mayr, and Achim Zeileis. "Nonhomogeneous Boosting for Predictor Selection in Ensemble Postprocessing." Monthly Weather Review 145, no. 1 (December 16, 2016): 137–47. http://dx.doi.org/10.1175/mwr-d-16-0088.1.
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Abstract Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.
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Xie, Yanxi, Yuewen Li, Victor Shi, and Quan Lu. "An Orthogonal Matching Pursuit Variable Screening Algorithm for High-Dimensional Linear Regression Models." Scientific Programming 2022 (August 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/6446903.
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Variable selection plays an important role in data mining. It is crucial to filter useful variables and extract useful information in a high-dimensional setup when the number of predictor variables d tends to be much larger than the sample size n . Statistical inferences can be more precise after irrelevant variables are moved out by the screening method. This article proposes an orthogonal matching pursuit algorithm for variable screening under the high-dimensional setup. The proposed orthogonal matching pursuit method demonstrates good performance in variable screening. In particular, if the dimension of the true model is finite, OMP might discover all relevant predictors within a finite number of steps. Throughout theoretical analysis and simulations, it is confirmed that the orthogonal matching pursuit algorithm can identify relevant predictors to ensure screening consistency in variable selection. Given the sure screening property, the BIC criterion can be used to practically select the best candidate from the models generated by the OMP algorithm. Compared with the traditional orthogonal matching pursuit method, the resulting model can improve prediction accuracy and reduce computational cost by screening out the relevant variables.
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Lookingbill, Todd R., and Dean L. Urban. "Gradient analysis, the next generation: towards more plant-relevant explanatory variables." Canadian Journal of Forest Research 35, no. 7 (July 1, 2005): 1744–53. http://dx.doi.org/10.1139/x05-109.
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The long history of gradient analysis is anchored in the observation that species turnover can be described along elevation gradients. This model is unsatisfying in that elevation is not directly relevant to plants and the ubiquitous "elevation gradient" is composed of multiple intertwined environmental factors. We offer an approach to landscape-scale vegetation analysis that disentangles the elevation gradient into its constituent parts through focused field sampling and statistical analysis. We illustrate the approach for an old-growth watershed in the Oregon Western Cascades. Our initial model of this system supports the common observation that forest community types are highly associated with specific elevation bands. By replacing elevation and other crude environmental proxy variables with estimates of more direct and resource gradients (radiation, temperature, and soil moisture), we create a vegetative model with stronger explanatory power than the proxy model in both cross-validation analysis and validation using an independent data set. The resulting model is also more biologically interpretable, which provides more meaningful insight into potential forest response to environmental change (e.g., global climate change scenarios). Acquiring a better mechanistic understanding of the relationship between plant communities and environmental predictor variables presents the next great challenge to community ecologists conducting gradient studies at landscape scales.
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González, Mari Feli, David Facal, Onésimo Juncos-Rabadán, and Javier Yanguas. "Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants." International Psychogeriatrics 29, no. 10 (June 29, 2017): 1669–80. http://dx.doi.org/10.1017/s1041610217001144.
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ABSTRACTBackground: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.Methods: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.Results: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.Conclusions: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.
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Parasuraman, N. R., P. Janaki Ramudu, and Nusrathuunisa . "Does Lintner model of dividend payout hold good? An Empirical evidence from BSE SENSEX firms." SDMIMD Journal of Management 3, no. 2 (September 1, 2012): 63. http://dx.doi.org/10.18311/sdmimd/2012/2743.
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This study primarily investigates into as to what influenced the dividends payment of BSE constituent companies for the years 2002 through as latest as 2011. The primary model used is that of Lintner (1956) with addition of relevant factors. The study tests three models including Lintner's basic model. While dividends paid is criterion variable in all the models, basic earnings and lagged dividends are predictor variables in the first model (Lintner model, 1956), cash earnings and lagged dividends in the second model and growth opportunities (depreciation and capital expenditure) in the third model are the predictor variables. The study tests the hypotheses if the dividends paid (criterion variable) depended on basic earnings, lagged dividends, cash earnings and capital expenditure. The multiple regression analysis has been performed using SPSS 15.0 version through ENTER method for every year and for all the years on an aggregate basis across the sample companies. Significance 'F' revealed that in all the three models dividends paid depended significantly (at 5% significance level) on all predictors variables. The value of multiple 'R' indicated that the models were very strong. Coefficient of determination (R<sup>2</sup>) also revealed that the explained portion of the relationship between criterion and predictor variables has been very high and significant enough to accept the model fit. However, standardized beta co-efficients (â) and 't' statistic revealed that basic earnings, cash earnings and lagged dividends exercised highest impact on dividends paid in most of the years during the study period. On the other hand, other predictor variables, depreciation and capital expenditure, did not have any significant impact on the dividends paid. The Durbin Watson coefficient indicated that multi co-linearity among predictor variables was strong enough to accept the validity of the model almost during the entire period of the study. Thus, the results and findings of the study support the prevalence and relevance of Lintner model of dividend policy. This means that the finance manager can't afford to ignore the variables like earnings capacity and lagged dividends while framing a dividend policy.
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Eddy, Yosi Lizar, Engku Muhammad Nazri, and Nor Idayu Mahat. "Identifying Relevant Predictor Variables for a Credit Scoring Model using Compromised-Analytic Hierarchy Process (Compromised-AHP)." Journal of Advanced Research in Business and Management Studies 20, no. 1 (September 30, 2020): 1–13. http://dx.doi.org/10.37934/arbms.20.1.113.
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Atmani, Baghdad, and Bouziane Beldjilali. "Neuro-IG: A Hybrid System for Selection and Elimination of Predictor Variables and non Relevant Individuals." Informatica 18, no. 2 (January 1, 2007): 163–86. http://dx.doi.org/10.15388/informatica.2007.170.
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Gzyl, H. "Best predictors in logarithmic distance between positive random variables." Journal of Applied Mathematics, Statistics and Informatics 15, no. 2 (December 1, 2019): 15–28. http://dx.doi.org/10.2478/jamsi-2019-0006.
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Abstract The metric properties of the set in which random variables take their values lead to relevant probabilistic concepts. For example, the mean of a random variable is a best predictor in that it minimizes the L2 distance between a point and a random variable. Similarly, the median is the same concept but when the distance is measured by the L1 norm. Also, a geodesic distance can be defined on the cone of strictly positive vectors in ℝn in such a way that, the minimizer of the distance between a point and a collection of points is their geometric mean. That geodesic distance induces a distance on the class of strictly positive random variables, which in turn leads to an interesting notions of conditional expectation (or best predictors) and their estimators. It also leads to different versions of the Law of Large Numbers and the Central Limit Theorem. For example, the lognormal variables appear as the analogue of the Gaussian variables for version of the Central Limit Theorem in the logarithmic distance.
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Soria, Juan J., Orlando Poma, David A. Sumire, Joel Hugo Fernandez Rojas, and Sulamita Marinela Ramos Chipa. "Multiple Linear Regression Model of Environmental Variables, Predictors of Global Solar Radiation in the Area of East Lima, Peru." IOP Conference Series: Earth and Environmental Science 1006, no. 1 (April 1, 2022): 012009. http://dx.doi.org/10.1088/1755-1315/1006/1/012009.
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Abstract Multiple regression models are very relevant to predict values using predictor variables. The objective of this study was to predict the global solar radiation in the year 2019 in the area of East Lima, Peru. Three continuous quantitative predictor variables were analyzed: temperature, humidity, wind speed and the response variable was global solar radiation, resulting in a model with excellent significance p<0.001 that shows the prediction is effective. The multiple linear regression method was used, finding an average global radiation of 175 W/m2 and predictor variables with average temperature of 19.2 °C, humidity 23.9% and wind speed 1.77 m/s, with the highest temperature in summer recorded at 24.6°C, the highest humidity of 51.2% in autumn, the highest wind speed in summer at 2.63 m/s and the highest maximum global solar radiation in spring with 183 W/m2.
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Gerendas, Bianca S., Sonja Prager, Gabor Deak, Christian Simader, Jan Lammer, Sebastian M. Waldstein, Tadhg Guerin, Michael Kundi, and Ursula Margarethe Schmidt-Erfurth. "Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema." British Journal of Ophthalmology 102, no. 2 (July 19, 2017): 195–203. http://dx.doi.org/10.1136/bjophthalmol-2017-310483.
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Background/aimsThe objective is to identify imaging biomarkers in optical coherence tomography predicting functional/anatomical outcomes in diabetic macular oedema (DMO).MethodsThe presented study is a post hoc analysis of the RESTORE/RESTORE-extension studies. Best-corrected visual acuity (BCVA) was analysed using general estimating equation models using treatment group/morphological features as predictor variables. In addition, linear multiple regression models analysed BCVA gain up to 12 and 36 months with BCVA/morphological baseline characteristics as independent predictor variables. The correlations between central retinal thickness (CRT)/BCVA were calculated as Spearman’s/Pearson’s correlation coefficients.ResultsA weak negative linear correlation between CRT/BCVA was observed in all study arms at baseline (r=−0.34, p<0.001) and at month 36 (r=−0.26, p<0.001). Patients with baseline height of intraretinal cystoid fluid (IRC) ≤380 µm had better baseline BCVA compared with patients with IRC height >380 µm (64.84±10.63 vs 61.66±9.92 letters; p=0.0071, respectively), which was maintained until the end of month 12 (70.5±12.33 vs 67.0±14.09 letters; p=0.0252, respectively). With laser, there was a trend for patients with subretinal fluid (SRF) at baseline to lose BCVA letters at month 12 (−5.38±16.54 vs 2.49±9.72 letters; p=0.1038), whereas ranibizumab patients trended towards higher BCVA gains (10.28±7.14 vs 6.76±7.67; p=0.0563), compared with those without SRF. With combined therapy, all patients had similar BCVA gains regardless of SRF (p=0.3768).ConclusionWith ranibizumab treatment, the height of IRC spaces at baseline was a better predictor of functional/anatomical improvement than CRT alone. There was also a trend for SRF to show a positive impact on ranibizumab therapy response and a negative impact on laser therapy response.
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Lorenzo, J. Buesa, A. García-Blanco, M. Vento, A. Moreno-Giménez, L. Campos Berga, V. Diago, D. Hervás, C. Cháfer-Pericás, and P. Sáenz González. "Can stress predict delivery date?: Role of chronic and acute stress to the threatened preterm labor as predictors of delivery date." European Psychiatry 64, S1 (April 2021): S607. http://dx.doi.org/10.1192/j.eurpsy.2021.1619.
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IntroductionThreatened preterm labor (TPL) is a traumatic event during pregnancy that involves a threat to the physical integrity of the upcoming baby. Despite biomarkers would be the strongest delivery date predictors, an assessment of chronic and acute stress response to TPL diagnosis may improve this prediction.ObjectivesThe objective is to predict delivery date in women with TPL based on their response to this diagnosis and chronic stressors, along with relevant obstetric variables.MethodsA prospective cohort study was conducted with a sample was formed by 157 pregnant women with TPL diagnosis between 24 and 31 weeks. Determination of salivary cortisol, α-amylase levels, along with anxiety and depression symptoms were measured to estimate stress response to TPL. Cumulative life stressors as traumas, social and familiar functioning were also registered. To examine the effect of the possible predictor variables of delivery date, linear regression models were used.ResultsA correlation was found between the variables of response to chronic stress and between the variables of psychological response to stress. The main predictors of preterm delivery were low family adaptation, higher BMI, higher cortisol levels, and the week of diagnosis of TPL (<29 weeks of gestation).ConclusionsThe best predictor of delivery date was the combination of the stress response to the diagnosis of TPL measured by cortisol in saliva, cumulative life stressors (mainly family adaptation) and obstetric factors (week TPL and BMI). Through psychosocial therapeutic intervention programs, it is possible to influence this modifiable predictive factors of preterm birth in symptomatic women.
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Alabdullah, Tariq Tawfeeq Yousif, and Essia Ries Ahmed. "Audit Committee Impact on Corporate Profitability in Oman Companies: an Auditing and Management Accounting Perspective." Riset Akuntansi dan Keuangan Indonesia 5, no. 2 (October 26, 2020): 121–28. http://dx.doi.org/10.23917/reaksi.v5i2.11836.
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This work investigates the impact of audit committees’ features as predictor variables of corporate profitability with a sample of firms belong to non-financial sector in Muscat Securities Market (MSM). This work analyzed cross sectional data for 60 non-financial firms. It used annual reports for the year of 2019 to analyze the impact of audit committees’ features on corporate profitability. The present work tested its hypotheses and utilized its variables via utilizing the Smart-PLS for data analysis. The findings revealed that a positive association between all the predictors and dependent variables are exist among the whole variables; audit committee, audit Independence, meeting of audit committee, and corporate profitability measured by management accounting’s indicators represented by ROA and ROE. This work is a new in its kind to be applied in Oman context via examining the relation between its predictors of audit committees’ features towards their impact on corporate profitability. The current study presents a theoretical and practical implications as a contribution relevant to practitioners working and academics in areas related to corporate profitability. In that, it furnishes empirical evidence for the policymakers, researchers and other interested parties.
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AROGUNDADE, O. T., A. O. ADEJIMI, A. M. MUSTAPHA, A. M. IKOTUN, and A. AKINWALE. "INVESTIGATION OF FACTORS AFFECTING CLOUD COMPUTING ADOPTION IN NIGERIA." Journal of Natural Sciences Engineering and Technology 15, no. 2 (November 22, 2017): 73–94. http://dx.doi.org/10.51406/jnset.v15i2.1687.
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Cloud computing is a viable alternative for meeting the technological needs of many enterprises with the benefits of instantaneous computing resource fulfillment, technology expenditures at lower costs, common technology platforms that can facilitate standardization and decreased need for internal technology support personnel. This paper examined the behavioral intention to adopt cloud computing services in large and small organization using an Enhanced Technology Acceptance Model (ETAM). The aim is to investigate the factors affecting cloud computing adoption in Nigeria. The model includes variables that other research has found related to adoption of new computing services and technologies. Regression Analysis was then deployed to test the research hypotheses. The result of regression analysis revealed that attitude and adopters ability to use cloud computing (self-efficacy) were better predictor of intention; perceived usefulness and perceived ease of use of cloud computing were better predictor of attitude; perceived ease of use and the relevant of cloud computing to adopters’ work (job relevance) were the predictor of perceived usefulness.
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Fernandes, Katia, Angel G. Muñoz, Julian Ramirez-Villegas, Diego Agudelo, Lizeth Llanos-Herrera, Alejandra Esquivel, Jeferson Rodriguez-Espinoza, and Steven D. Prager. "Improving Seasonal Precipitation Forecasts for Agriculture in the Orinoquía Region of Colombia." Weather and Forecasting 35, no. 2 (February 25, 2020): 437–49. http://dx.doi.org/10.1175/waf-d-19-0122.1.
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Abstract Canonical correlation analysis (CCA) is used to improve the skill of seasonal forecasts in the Orinoquía region, where over 40% of Colombian rice is produced. Seasonal precipitation and frequency of wet days are predicted, as rice yields simulated by a calibrated crop model are better correlated with wet-day frequency than with precipitation amounts in June–August (JJA). Prediction of the frequency of wet days, using as predictors variables from the NCEP Climate Forecast System, version 2 (CFSv2), results in a forecast with higher skill than models predicting seasonal precipitation amounts. Using wet-day frequency as an alternative climate variable reveals that the distribution of daily rainfall is both more relevant for rice yield variability and more skillfully predicted than seasonal precipitation amounts. Forecast skill can also be improved by using the Climate Hazards Infrared Precipitation with Stations (CHIRPS) merged satellite–station JJA precipitation as the predictand in a CCA model, especially if the predictor is CFSv2 vertically integrated meridional moisture flux (VQ). The probabilistic hindcast derived from the CCA model using CHIRPS as the predictand can successfully discriminate above-normal, normal, and below-normal terciles of over 80% of the stations in the region. This is particularly relevant for stations that, due to discontinuity in their time series, are not included in station-only CCA models but are still in need of probabilistic seasonal forecasts. Finally, CFSv2 VQ performs better than precipitation as the predictor in CCA, which we attribute to CFSv2 being more internally consistent in regards to sea surface temperature (SST)-forced VQ variability than to SST-forced precipitation variability in the Orinoquía region.
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Barragan, Rodolfo C., Nigini Oliveira, Koosha Khalvati, Rechele Brooks, Katharina Reinecke, Rajesh P. N. Rao, and Andrew N. Meltzoff. "Identifying with all humanity predicts cooperative health behaviors and helpful responding during COVID-19." PLOS ONE 16, no. 3 (March 10, 2021): e0248234. http://dx.doi.org/10.1371/journal.pone.0248234.
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In the ongoing COVID-19 pandemic, public health experts have produced guidelines to limit the spread of the coronavirus, but individuals do not always comply with experts’ recommendations. Here, we tested whether a specific psychological belief—identification with all humanity—predicts cooperation with public health guidelines as well as helpful behavior during the COVID-19 pandemic. We hypothesized that peoples’ endorsement of this belief—their relative perception of a connection and moral commitment to other humans—would predict their tendencies to adopt World Health Organization (WHO) guidelines and to help others. To assess this, we conducted a global online study (N = 2537 participants) of four WHO-recommended health behaviors and four pandemic-related moral dilemmas that we constructed to be relevant to helping others at a potential cost to oneself. We used generalized linear mixed models (GLMM) that included 10 predictor variables (demographic, contextual, and psychological) for each of five outcome measures (a WHO cooperative health behavior score, plus responses to each of our four moral, helping dilemmas). Identification with all humanity was the most consistent and consequential predictor of individuals’ cooperative health behavior and helpful responding. Analyses showed that the identification with all humanity significantly predicted each of the five outcomes while controlling for the other variables (Prange < 10−22 to < 0.009). The mean effect size of the identification with all humanity predictor on these outcomes was more than twice as large as the effect sizes of other predictors. Identification with all humanity is a psychological construct that, through targeted interventions, may help scientists and policymakers to better understand and promote cooperative health behavior and help-oriented concern for others during the current pandemic as well as in future humanitarian crises.
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Thompson, James P., J. Alexander Boeringa, John Thornby, and Fred Lewis. "Some Outcome Predictors for Use in Vocational Rehabilitation Planning." Psychological Reports 76, no. 2 (April 1995): 423–26. http://dx.doi.org/10.2466/pr0.1995.76.2.423.
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This study examined some predictor variables for positive outcome with a state vocational rehabilitation agency. Referrals to the agency from VA Counseling Psychology were analyzed to develop guidelines for identifying clients with good probability for positive outcome in their vocational rehabilitation planning. All subjects had a history of substance-abuse treatment at the Houston VA Medical Center. Analyses suggested a number of factors are relevant when assessing potential for successful vocational rehabilitation. Provision of drug-free housing, recent work history, and discharge status from a substance-abuse program were identified as relevant factors.
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Zaiets, Andrii. "Predictor Variables for Elevated Mood and Activity in Hypomania and Self-Actualization." Bulletin of Taras Shevchenko National University of Kyiv. Series “Psychology” 1, no. 13 (2021): 28–32. http://dx.doi.org/10.17721/bsp.2021.1(13).5.
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The issue of overdiagnosis of hypomanic episodes is relevant due to their borderline nature. Diagnostic criteria describing elevated mood and activity, high self-esteem and productivity, is partly resembling a healthy process of self-actualization, which on the one hand leads to the already mentioned overdiagnosis, and on the other – to the social stigma of patients with bipolar disorder and medical and social prejudice against the elevated mood and activity they display. As part of this work, using correlation and regression, differentiation of high mood and high activity predictors is made in groups of people with a high level of self-actualization (n = 25) and patients diagnosed with the bipolar affective disorder without comorbidity (n = 24). A sample of healthy individuals with a normal level of self-actualization (n = 110) was also involved to control side variables and compare means. The following assessment inventories are used to collect data: Personal Orientation Inventory (Shostrom, 1963 – in the adaptation of SAT (Aleshina, Gozman, Zaika & Kroz, 1984)), shortened version of MMPI Mini-Mult (Zaitsev, Kozyula, 1981), FPI-B (Hampel & Selg, 1963 – adapted by Krylov, Ronginsky, 1989). The regression of the "hypomania" scale, Mini-Mult, as a dependent variable, showed different groups of predictors for the two studied samples. The statistics are summarized as a model of the relationship between elevated mood and activity predictors for the two groups. Significant differences in the configuration and elements of the linkages indicate the possibility of further development of this problem to create assessment tools and methodological guidelines for psychosocial care for groups at risk of developing or recurrence of bipolar disorder. Mainly, the difference in predicting variables consists of highly negative relation of cognitive needs to hypomania in the BAD group with highly positive direct affection of "schizoid" variable of Mini-Mult, whereas hypomanic trait in the self-actualizing group is affected by "schizoid" variable indirectly and is mainly subject of effect for "existential flexibility" and "time competence" variables.
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Pandey, Bikram, Nirdesh Nepal, Salina Tripathi, Kaiwen Pan, Mohammed A. Dakhil, Arbindra Timilsina, Meta F. Justine, Saroj Koirala, and Kamal B. Nepali. "Distribution Pattern of Gymnosperms’ Richness in Nepal: Effect of Environmental Constrains along Elevational Gradients." Plants 9, no. 5 (May 14, 2020): 625. http://dx.doi.org/10.3390/plants9050625.
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Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms’ richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern.
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Stemberger, Joseph Paul. "Vowel dominance in overregularizations." Journal of Child Language 20, no. 3 (October 1993): 503–21. http://dx.doi.org/10.1017/s030500090000845x.
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ABSTRACTWhen children produce regularizations likecomed, not all verbs are equally likely to be regularized. Several variables (e.g. lexical frequency) have been shown to be relevant, but not all the variability between verbs is understood. It is argued here that one predictor is which vowels are present in the base form vs. the past tense form. Using a notion of recessive vs. dominant vowel (where recessive vowels are more likely to be replaced by dominant vowels than vice versa) based on adult phonological processing, it is predicted that regularizations should be likely when the base vowel is dominant and unlikely when the past tense vowel is dominant. Data from 17 children reported in the literature, aged 1;6–5;6, show that this prediction is correct. Implications for the role of phonological variables in the processing of irregular past tense forms are discussed.
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Hafermann, Lorena, Nadja Klein, Geraldine Rauch, Michael Kammer, and Georg Heinze. "Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study." Entropy 24, no. 6 (June 20, 2022): 847. http://dx.doi.org/10.3390/e24060847.
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There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.
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Onsay, Emmanuel. "Unraveling the Nexus of Science & Technology Input and Economic Growth through Research & Development (R&D) Indicators in Asia-pacific Region: A Panel Data and Causality Analysis." Journal of Education, Management and Development Studies 1, no. 3 (December 30, 2021): 1–18. http://dx.doi.org/10.52631/jemds.v1i3.38.
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This paper unravels the critical aspect of science and technology through research and development indicators as sources, drivers, and predictors of economic growth from the perspective of two developing countries, namely: Philippines and Thailand (ASEAN), and two developed economies, namely: Japan and Australia (ASEAN-X) in Asia-Pacific Region. The data set ranges from 1980 to 2019 and is collected from World Development Indicators of the World Bank, Institute for Statistics of United Nations Educational, Scientific and Cultural Organization (UNESCO), and World Intellectual Property Organization (WIPO). Research and Development (R&D) is a tool for generating new knowledge and serves as input for technological advancement. In the long run, it has been proven that technology can sustain permanent economic development in the economy. In developed economies, the nexus between the aforementioned variables is robust and significant. Thus, the R&D indicators can be used as a predictor of economic growth. However, in developing economies, the nexus of variables involved is negligible and insignificant. Hence, the R&D indicators cannot be effectively utilized as a predictor of economic growth. Furthermore, the study combined the two sets of panel data and a relevant conclusion was drawn. A country-panel regression and causality analysis were performed based on the empirics of macroeconomics.
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Studerus, Erich, Patrick Vizeli, Samuel Harder, Laura Ley, and Matthias E. Liechti. "Prediction of MDMA response in healthy humans: a pooled analysis of placebo-controlled studies." Journal of Psychopharmacology 35, no. 5 (March 30, 2021): 556–65. http://dx.doi.org/10.1177/0269881121998322.
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Background: 3,4-methylenedioxymethamphetamine (MDMA, “ecstasy”) is used both recreationally and therapeutically. Little is known about the factors influencing inter- and intra-individual differences in the acute response to MDMA. Effects of other psychoactive substances have been shown to be critically influenced by personality traits and mood state before intake. Methods: We pooled data from 10 randomized, double-blind, placebo-controlled, cross-over studies performed in the same laboratory in 194 healthy subjects receiving doses of 75 or 125mg of MDMA. We investigated the influence of drug dose, body weight, sex, age, drug pre-experience, genetics, personality and mental state before drug intake on the acute physiological and psychological response to MDMA. Results: In univariable analyses, the MDMA plasma concentration was the strongest predictor for most outcome variables. When adjusting for dose per body weight, we found that (a) a higher activity of the enzyme CYP2D6 predicted lower MDMA plasma concentration, (b) a higher score in the personality trait “openness to experience” predicted more perceived “closeness”, a stronger decrease in “general inactivation”, and higher scores in the 5D-ASC (5 Dimensions of Altered States of Consciousness Questionnaire) scales “oceanic boundlessness” and “visionary restructuralization”, and (c) subjects with high “neuroticism” or trait anxiety were more likely to have unpleasant and/or anxious reactions. Conclusions: Although MDMA plasma concentration was the strongest predictor, several personality traits and mood state variables additionally explained variance in the response to MDMA. The results confirm that both pharmacological and non-pharmacological variables influence the response to MDMA. These findings may be relevant for the therapeutic use of MDMA.
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Barrio, Irantzu, Inmaculada Arostegui, María-Xosé Rodríguez-Álvarez, and José-María Quintana. "A new approach to categorising continuous variables in prediction models: Proposal and validation." Statistical Methods in Medical Research 26, no. 6 (September 18, 2015): 2586–602. http://dx.doi.org/10.1177/0962280215601873.
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When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points’ location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.
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Juarez-Orozco, Luis Eduardo, Andrea G. Monroy-Gonzalez, Friso M. van der Zant, Nick Hoogvorst, Riemer H. J. A. Slart, and Remco J. J. Knol. "Ventricular synchrony is not significantly determined by absolute myocardial perfusion in patients with chronic heart failure: A 13N-ammonia PET study." Journal of Nuclear Cardiology 27, no. 6 (November 15, 2018): 2234–42. http://dx.doi.org/10.1007/s12350-018-01507-9.
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Abstract Background It is thought that heart failure (HF) patients may benefit from the evaluation of mechanical (dys)synchrony, and an independent inverse relationship between myocardial perfusion and ventricular synchrony has been suggested. We explore the relationship between quantitative myocardial perfusion and synchrony parameters when accounting for the presence and extent of fixed perfusion defects in patients with chronic HF. Methods We studied 98 patients with chronic HF who underwent rest and stress Nitrogen-13 ammonia PET. Multivariate analyses of covariance were performed to determine relevant predictors of synchrony (measured as bandwidth, standard deviation, and entropy). Results In our population, there were 43 (44%) women and 55 men with a mean age of 71 ± 9.6 years. The SRS was the strongest independent predictor of mechanical synchrony variables (p < .01), among other considered predictors including: age, sex, body mass index, smoking, diabetes mellitus, dyslipidemia, hypertension, rest myocardial blood flow (MBF), and myocardial perfusion reserve (MPR). Results were similar when considering stress MBF instead of MPR. Conclusions The existence and extent of fixed perfusion defects, but not the quantitative PET myocardial perfusion parameters (sMBF and MPR), constitute a significant independent predictor of ventricular mechanical synchrony in patients with chronic HF.
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Garshelis, David Lance. "Understanding Species–Habitat Associations: A Case Study with the World’s Bears." Land 11, no. 2 (January 23, 2022): 180. http://dx.doi.org/10.3390/land11020180.
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Habitat modeling is one of the most common practices in ecology today, aimed at understanding complex associations between species and an array of environmental, bioclimatic, and anthropogenic factors. This review of studies of seven species of terrestrial bears (Ursidae) occupying four continents examines how habitat models have been employed, and the functionality of their predictions for management and conservation. Bear occurrence data have been obtained at the population level, as presence points (e.g., sign surveys or camera trapping), or as locations of individual radio-collared animals. Radio-collars provide greater insights into how bears interact with their environment and variability within populations; they are more commonly used in North America and Europe than in South America and Asia. Salient problematic issues apparent from this review included: biases in presence data; predictor variables being poor surrogates of actual behavioral drivers; predictor variables applied at a biologically inappropriate scale; and over-use of data repositories that tend to detach investigators from the species. In several cases, multiple models in the same area yielded different predictions; new presence data occurred outside the range of predicted suitable habitat; and future range projections, based on where bears presently exist, underestimated their adaptability. Findings here are likely relevant to other taxa.
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Neiman, Nicole, Kavya Anjur, Ann Ming Yeh, Rachel Bensen, and Anava Wren. "FEAR OF THE COVID-19 PANDEMIC IN ADOLESCENTS AND YOUNG ADULTS WITH INFLAMMATORY BOWEL DISEASE: RELATIONSHIP TO PSYCHOLOGICAL AND PHYSICAL OUTCOMES." Inflammatory Bowel Diseases 28, Supplement_1 (January 22, 2022): S89—S90. http://dx.doi.org/10.1093/ibd/izac015.144.
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Abstract BACKGROUND The fear of contracting COVID-19 has taken a significant toll on the psychological and physical health of adolescents and young adults (AYA), especially those with chronic health conditions. AYA with Inflammatory Bowel Disease (IBD) are at increased risk for poor psychological and physical well-being. However, to date, there is no published research examining the Fear of COVID-19 Scale (FCV-19S) in AYA with IBD. AIMS To assess: 1) the reliability of the Fear of COVID-19 Scale in AYA with IBD, and 2) how the fear of COVID-19 relates to key psychological (i.e., anxiety, depression) and physical (i.e., fatigue, pain, disease activity) outcomes in AYA with IBD. METHODS This ongoing study is a collaboration between Stanford University and ImproveCareNow. Participants included 64 AYA with IBD (M=18.7 years old; 52% Ulcerative Colitis; 60% female; 60% White). Participants completed a one-time online survey. Correlation analyses examined the association between fear of COVID-19, outcome variables (i.e., anxiety, depression, fatigue, pain, disease activity), and demographic variables. Multiple linear regression (MLR) analyses (using the F test) further examined the association between fear of COVID-19, outcome variables, and significant demographic variables (i.e., age, sex). RESULTS The internal reliability of FCV-19S was measured using Crohnbach’s alpha (α=0.88), indicating high internal consistency. Correlation analyses demonstrated that fear of COVID-19 was significantly associated with anxiety, depression, and fatigue (Table 1). The overall MLR models were significant for anxiety (P<0.001), depression (P=0.002), and fatigue (P=0.031), but not for pain and disease activity. Fear of COVID-19, age, and sex accounted for 29% of the variance in anxiety, 27% of the variance in depression, and 17% of the variance in fatigue. Fear of COVID-19 was not a significant independent predictor of outcomes (Table 2). Female sex was a significant independent predictor for greater levels of anxiety (b=-0.31, P=0.016) and depression (b=-0.35, P=0.008). Older age was a significant independent predictor for greater levels of depression (b=0.29, P=0.031). CONCLUSION Preliminary results suggest that FCV-19S can be reliably assessed in AYA with IBD. Analyses indicate that fear of COVID-19 is associated with anxiety, depression, and fatigue in AYA with IBD. While COVID-19 fear may be important in explaining these outcomes, female sex independently predicted increased anxiety and depression, and older age independently predicted depression. This highlights the importance of assessing relevant demographic variables (e.g., female sex, young adults) when considering predictors of adjustment in AYA with IBD. Research should further investigate fear of COVID-19 in larger, more diverse IBD populations to better understand its relationship to key IBD outcomes.
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Barbot, Baptiste. "“Generic” Creativity as a Predictor or Outcome of Identity Development?" Creativity. Theories – Research - Applications 5, no. 2 (December 1, 2018): 159–64. http://dx.doi.org/10.1515/ctra-2018-0013.
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AbstractIn this brief commentary to Kaufman’s call for a “new agenda for positive outcomes” of creativity research, I emphasize how the broad construct of “identity” qualifies as such an outcome. While doing so, I challenge the issue of directionality (predictor vs. outcome) of creativity in relation to relevant correlates by outlining the influence of epistemological position and publication bias in directional interpretations of correlational findings. Through illustrations of various levels of relationships between creativity and identity, I also urge creativity researchers to be more explicit regarding how “generic” creativity is being operationalized in their study, so that more targeted hypotheses regarding the relationship between distinct aspects of creativity and such positive out-come variables may be formulated.
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Gonçalves, Gabriela, Marta Reis, Cátia Sousa, Joana Santos, Alejandro Orgambídez-Ramos, and Peter Scott. "Cultural intelligence and conflict management styles." International Journal of Organizational Analysis 24, no. 4 (September 5, 2016): 725–42. http://dx.doi.org/10.1108/ijoa-10-2015-0923.
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Purpose Negotiating effectively in multicultural contexts or others is not only a very important skill for all organizational elements but also crucial to inter-organizational relations (Adler, 2008). If defined as a process that occurs when one party feels adversely affected by another (De Dreu, 1997). Conflict management styles can be analyzed as a function of personality variables. In this respect, cultural intelligence and self-monitoring appear to be relevant variables, as they are characterized by the demonstration of flexibility and interest in elements that are present in conflict management styles. This study aimed to evaluate the extent to which variables such as cultural intelligence and self-monitoring can positively influence the ability to solve interpersonal conflicts more effectively. Design/methodology/approach This study, with a sample of 399 individuals, aimed to test a model that explores how cultural intelligence and self-monitoring are related as predictor variables in the styles of conflict resolution. Findings It was observed that cultural intelligence presents itself as a reasonable predictor of conflict management styles, whereas self-monitoring appeared as a dispositional and controversial measure in relation to those styles. Self-monitoring exhibited itself as an important predictor of conflict management, but on the other hand, it had an influence on the choice of the dominating style in conflict situations. Practical implications Understanding the predictors of conflict management style and, in particular, realizing the extent to which cultural intelligence promotes a more effective conflict management style can help in the development of selection processes and skill training programs. The development of these multicultural skills will contribute to individual, social and organizational well-being. Originality/value This study contributes to the literature of individual differences and conflict management, demonstrating that some individual differences that predict the styles of conflict management can lead to a certain ambiguity in understanding the behaviour that an individual may adopt in situations of conflict.
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Bennington, S., W. Rayment, and S. Dawson. "Putting prey into the picture: improvements to species distribution models for bottlenose dolphins in Doubtful Sound, New Zealand." Marine Ecology Progress Series 653 (October 29, 2020): 191–204. http://dx.doi.org/10.3354/meps13492.
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Species distribution models (SDMs) often rely on abiotic variables as proxies for biotic relationships. This means that important biotic relationships may be missed, creating ambiguity in our understanding of the drivers of habitat use. These problems are especially relevant for populations of predators, as their habitat use is likely to be strongly influenced by the distribution of their prey. We investigated habitat use of a population of a top predator, bottlenose dolphins Tursiops truncatus, in Doubtful Sound, New Zealand, using generalised additive models, and compared the results of models with and without biotic predictor variables. We found that although habitat use by bottlenose dolphins was significantly correlated with abiotic variables that likely describe foraging areas, introduction of biotic variables describing potential prey almost doubled the deviance explained, from 19.8 to 39.1%. Biotic variables were the most important of the predictors used, and indicated that the dolphins showed a preference for areas with a high abundance of a reef fish, girdled wrasse Notolabrus cinctus. For the dolphins of Doubtful Sound, these results show the importance of prey distribution in driving habitat use. On a broader scale, our results indicate that making an effort to include true biotic descriptors in SDMs can improve model performance, resulting in better understanding of the drivers of distribution of marine predators.
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Franco-Peláez, Juan Antonio, Roberto Martín-Reyes, Ana María Pello-Lázaro, Álvaro Aceña, Óscar Lorenzo, José Luis Martín-Ventura, Luis Blanco-Colio, et al. "Monocyte Chemoattractant Protein-1 Is an Independent Predictor of Coronary Artery Ectasia in Patients with Acute Coronary Syndrome." Journal of Clinical Medicine 9, no. 9 (September 21, 2020): 3037. http://dx.doi.org/10.3390/jcm9093037.
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Our purpose was to assess a possible association of inflammatory, lipid and mineral metabolism biomarkers with coronary artery ectasia (CAE) and to determine a possible association of this with acute atherotrombotic events (AAT). We studied 270 patients who underwent coronary angiography during an acute coronary syndrome 6 months before. Plasma levels of several biomarkers were assessed, and patients were followed during a median of 5.35 (3.88–6.65) years. Two interventional cardiologists reviewed the coronary angiograms, diagnosing CAE according to previously published criteria in 23 patients (8.5%). Multivariate binary logistic regression analysis was used to search for independent predictors of CAE. Multivariate analysis revealed that, aside from gender and a diagnosis of dyslipidemia, only monocyte chemoattractant protein-1 (MCP-1) (OR = 2.25, 95%CI = (1.35–3.76) for each increase of 100 pg/mL, p = 0.001) was independent predictor of CAE, whereas mineral metabolism markers or proprotein convertase subtilisin/kexin type 9 were not. Moreover, CAE was a strong predictor of AAT during follow-up after adjustment for other clinically relevant variables (HR = 2.67, 95%CI = (1.22–5.82), p = 0.013). This is the first report showing that MCP-1 is an independent predictor of CAE, suggesting that CAE and coronary artery disease may share pathogenic mechanisms. Furthermore, CAE was associated with an increased incidence of AAT.
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Yang, Cheng-Hong, Sin-Hua Moi, Li-Yeh Chuang, Shyng-Shiou F. Yuan, Ming-Feng Hou, Yi-Chen Lee, and Hsueh-Wei Chang. "Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses." BioMed Research International 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2563910.
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The interaction between the meiotic recombination 11 homolog A (MRE11) oncoprotein and breast cancer recurrence status remains unclear. The aim of this study was to assess the interaction between MRE11 and clinicopathologic variables in breast cancer. A dataset for 254 subjects with breast cancer (220 nonrecurrent and 34 recurrent) was used in individual and cumulated receiver operating characteristic (ROC) analyses of MRE11 and 12 clinicopathologic variables for predicting breast cancer recurrence. In individual ROC analysis, the area under curve (AUC) for each predictor of breast cancer recurrence was smaller than 0.7. In cumulated ROC analysis, however, the AUC value for each predictor improved. Ten relevant variables in breast cancer recurrence were used to find the optimal prognostic indicators. The presence of any six of the following ten variables had a high (79%) sensitivity and a high (70%) specificity for predicting breast cancer recurrence: tumor size ≥ 2.4 cm, tumor stage II/III, therapy other than hormone therapy, age ≥ 52 years, MRE11 positive cells > 50%, body mass index ≥ 24, lymph node metastasis, positivity for progesterone receptor, positivity for epidermal growth factor receptor, and negativity for estrogen receptor. In conclusion, this study revealed that these 10 clinicopathologic variables are the minimum discriminators needed for optimal discriminant effectiveness in predicting breast cancer recurrence.
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Grobler, Anneke C., Adelene A. Grobler, and Karel G. F. Esterhuyse. "Some Predictors of Mathematics Achievement among Black Secondary School Learners." South African Journal of Psychology 31, no. 4 (December 2001): 48–54. http://dx.doi.org/10.1177/008124630103100406.
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This study was conducted to identify predictors of mathematics achievement among grade 9 learners of a random sample of five township schools. A series of regression analyses were performed for boys and girls separately to obtain Cohen's (1992) effect size estimate (uniquely explained criterion variance expressed as a proportion of unexplained criterion variance) for various predictor variables. Cognitive predictors were verbal and non-verbal General Scholastic Aptitude Test scores. Non-cognitive variables included the hierarchical levels of self-concept: Global (Rosenberg Self-Esteem Scale), and academic and mathematics self-concept (relevant scales of Brookover, Erickson and Joiner). Socio-economic predictors included home-related variables (parental education, parental occupation, family size) and school-related factors (class size, teacher's qualification, teacher's experience). Gender differences favouring boys were found. Non-verbal and verbal scholastic aptitude and teacher's general training correlated significantly with mathematics achievement for boys and girls, with nonverbal scholastic aptitude showing the highest correlation and effect size estimate for girls and teacher's general training occupying this position for boys. Teacher's mathematics training and class size showed correlations in excess of 0.35 for boys but not for girls. The negative corrrelation obtained for teacher's general training suggested that learners whose teachers held a three-year teaching diploma performed better in mathematics than did learners whose teachers held a degree and a teacher's diploma.
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Zador, Zsolt, Wendy Huang, Matthew Sperrin, and Michael T. Lawton. "Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era." Operative Neurosurgery 14, no. 6 (July 31, 2017): 603–10. http://dx.doi.org/10.1093/ons/opx163.
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AbstractBACKGROUNDFollowing the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping.OBJECTIVETo update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning.METHODSWe reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning.RESULTSImportance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters.CONCLUSIONOur study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.
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Pandor, Abdullah, Gordon Fuller, Munira Essat, Lisa Sabir, Chris Holt, Helen Buckley Woods, and Hridesh Chatha. "Individual risk factors predictive of major trauma in pre-hospital injured older patients: a systematic review." British Paramedic Journal 6, no. 4 (March 1, 2022): 26–40. http://dx.doi.org/10.29045/14784726.2022.03.6.4.26.
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Background: Older adults with major trauma are frequently under-triaged, increasing the risk of preventable morbidity and mortality. The aim of this systematic review was to identify which individual risk factors and predictors are likely to increase the risk of major trauma in elderly patients presenting to emergency medical services (EMS) following injury, to inform future elderly triage tool development.Methods: Several electronic databases (including Medline, EMBASE, CINAHL and the Cochrane Library) were searched from inception to February 2021. Prospective or retrospective diagnostic studies were eligible if they examined a prognostic factor (often termed predictor or risk factor) for, or diagnostic test to identify, major trauma. Selection of studies, data extraction and risk of bias assessments using the Quality in Prognostic Studies (QUIPS) tool were undertaken independently by at least two reviewers. Narrative synthesis was used to summarise the findings.Results: Nine studies, all performed in US trauma networks, met review inclusion criteria. Vital signs (Glasgow Coma Scale (GCS) score, systolic blood pressure, respiratory rate and shock index with specific elderly cut-off points), EMS provider judgement, comorbidities and certain crash scene variables (other occupants injured, occupant not independently mobile and head-on collision) were identified as significant pre-hospital variables associated with major trauma in the elderly in multi-variable analyses. Heart rate and anticoagulant were not significant predictors. Included studies were at moderate or high risk of bias, with applicability concerns secondary to selected study populations.Conclusions: Existing pre-hospital major trauma triage tools could be optimised for elderly patients by including elderly-specific physiology thresholds. Future work should focus on more relevant reference standards and further evaluation of novel elderly relevant triage tool variables and thresholds.
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Steinfath, Matthias, Dirk Repsilber, Manuela Hische, Nicolas Schauer, Alisdair R. Fernie, and Joachim Selbig. "Metabolite profiles as a reflection of physiological status – a methodological validation." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 61–76. http://dx.doi.org/10.1515/jib-2006-28.
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Summary Biological ”omics” data comprise numerous variables (metabolites, gene expression, physiological quantities) and comparatively few samples. These samples represent either measurements for slightly different genotypes in identical environments, or for different environmental conditions affecting the same genotype. Given this kind of data, it is intriguing to ask for possible measurable associations between molecular variables and the phenotypical or physiological status.To evaluate such correlations we need a model for the functional dependency of the physiological state on given molecular variables. Supervised machine learning methods such as neural networks, decision trees, or support vector machines may be used to reveal such correlations. The simplest model is certainly a linear approach. To investigate the association between molecular and phenotypical variables, we ask if the correlation between predictor and response is statistically significant, and how much of the phenotypical variance of the response can be explained by a given set of predictors. When confronted with a set of molecular data not all of them are generally relevant for each physiological trait. Given this fact the problem of feature selection arises.Different regression methods have been developed to answer this question: Ordinary Least Squares (OLS) yields an unbiased solution, but normally has a high mean square error. In particular, there is no dimension reduction included in this method and, hence, overfitting is a critical problem. In contrast, Principle Component Regression (PCR) offers such a dimension reduction, however, the principle components are found without considering the response. Partial Least Squares Regression (PLSR) is utilised as an alternative method since it considers the variance within the predictors as well as between predictors and response, whilst Ridge Regression is a further alternative worthy of consideration.In our study we applied these methods to data resulting from a tomato metabolite experimental series. Comparison of the results for this dataset with experimentally relevant correlation structure between variables and samples allows us to test the relative merits of the regression methods with respect to the questions raised above. Given certain prerequisite knowledge it also allows us to conjecture the true biological correlation. Our results show that under most circumstances OLS is worst with respect to prediction. However, the ranking of methods seems to change considerably if the question of feature selection is considered. Understanding and discussing these differences is a relevant contribution to the task of choice of suitable approach of correlation analysis for “omics” datasets with respect to the biological interpretation in question.
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Fueyo-Díaz, Ricardo, Miguel Montoro, Rosa Magallón-Botaya, Santiago Gascón-Santos, Ángela Asensio-Martínez, Guillermo Palacios-Navarro, and Juan J. Sebastián-Domingo. "Influence of Compliance to Diet and Self-Efficacy Expectation on Quality of Life in Patients with Celiac Disease in Spain." Nutrients 12, no. 9 (September 2, 2020): 2672. http://dx.doi.org/10.3390/nu12092672.
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The purpose of this study is to understand the health-related quality of life (HRQoL) in patients with celiac disease (CD) and analyze its main determinants. A transversal descriptive study of 738 patients with celiac disease was carried out. A series of questionnaires were answered related to their HRQoL, adherence to a gluten-free diet (GFD), and self-efficacy beliefs among other relevant variables. Regression analyses were carried out in order to explore the predictive variables in adherence to the GFD and HRQoL. A total of 61.2% showed a good HRQoL, and the main predictors of HRQoL were specific self-efficacy, adherence to the diet, risk perception, time since diagnosis, and age. While 68.7% of participants showed good or excellent adherence to the GFD, and the main predictors of adherence were specific self-efficacy, perceived adoption of recommended behaviors, HRQoL and gender. The HRQoL of patients with CD, and adherence to the GFD in Spain, are good. It is the self-efficacy expectation, measured specifically and not generally, which is the best predictor of both adherence and HRQoL. It is necessary to develop programs to improve the HRQoL of patients with CD that focus on improving specific self-efficacy.
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Annesi, James. "Moderating Effects of Depression, Food Cravings, and Weight-Related Quality-of-Life on Associations of Treatment-Targeted Psychosocial Changes and Physical Activity in Adolescent Candidates for Bariatric Surgery." Journal of Physical Activity and Health 15, no. 12 (December 1, 2018): 946–53. http://dx.doi.org/10.1123/jpah.2018-0099.
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Background: Physical activity is a strong predictor of sustaining weight loss. Yet physical activity has been challenging to maintain. Adolescent bariatric surgery is increasing, and there is typically an initial 6-month period when improving health behaviors such as physical activity are addressed by a clinic-based team. However, there is minimal understanding of how to target psychosocial factors relevant for behavioral changes. Methods: A group of 15 adolescent candidates for bariatric surgery (mean age = 15.1 y; mean body mass index = 55.9 kg/m2) were assessed on changes in 3 theory-based predictors of physical activity from baseline–month 3 and baseline–month 6. Results: Changes in physical activity-related self-regulation and self-efficacy over 3 months significantly predicted change in physical activity over 6 months. Reciprocal relationships were also significant, including the prediction of physical activity change by change in negative mood. The clinical psychology-based factor of weight-related quality-of-life significantly moderated the prediction of self-regulation via physical activity, and degree of depressive symptoms significantly moderated the prediction of changes in physical activity through self-efficacy changes. Conclusions: Because improvements in several theory-based psychosocial variables related to physical activity have demonstrated a carry-over to controlling eating, the improved understanding of those variables for treating adolescents with severe obesity was useful.
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Mata, Luciana Regina Ferreira da, Emilia Campos de Carvalho, Cássia Regina Gontijo Gomes, Ana Cristina da Silva, and Maria da Graça Pereira. "Postoperative self-efficacy and psychological morbidity in radical prostatectomy." Revista Latino-Americana de Enfermagem 23, no. 5 (October 2015): 806–13. http://dx.doi.org/10.1590/0104-1169.0456.2618.
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Objective: evaluate the general and perceived self-efficacy, psychological morbidity, and knowledge about postoperative care of patients submitted to radical prostatectomy. Identify the relationships between the variables and know the predictors of self-efficacy.Method: descriptive, cross-sectional study, conducted with 76 hospitalized men. The scales used were the General and Perceived Self-efficacy Scale and the Hospital Anxiety and Depression Scale, in addition to sociodemographic, clinical and knowledge questionnaires.Results: a negative relationship was found for self-efficacy in relation to anxiety and depression. Psychological morbidity was a significant predictor variable for self-efficacy. An active professional situation and the waiting time for surgery also proved to be relevant variables for anxiety and knowledge, respectively.Conclusion: participants had a good level of general and perceived self-efficacy and small percentage of depression. With these findings, it is possible to produce the profile of patients about their psychological needs after radical prostatectomy and, thus, allow the nursing professionals to act holistically, considering not only the need for care of physical nature, but also of psychosocial nature.
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Coenders, Germà, Josep A. Martín-Fernández, and Berta Ferrer-Rosell. "When relative and absolute information matter: Compositional predictor with a total in generalized linear models." Statistical Modelling 17, no. 6 (June 23, 2017): 494–512. http://dx.doi.org/10.1177/1471082x17710398.
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The analysis of compositional data (CoDa) consists in the study of the relative importance of parts of a whole rather than the size of the whole because absolute information is either unavailable or not of interest. On the other hand, when absolute and relative information are both relevant, research hypotheses concern both. This article introduces a model including both the logratios used in CoDa and a total variable carrying absolute information as predictors in an otherwise standard statistical model. It shows how logratios can be tailored to the researchers’ hypotheses and alternative ways of computing the total. The interpretational advantages with respect to traditional approaches are presented and the equivalence and invariance properties are proven. A sequence of nested models is presented to test the relevance of relative and absolute information. The approach can be applied to dependent metric, binary, ordinal or count variables. Two illustrations are provided, the first on tourist expenditure and satisfaction and the second on solid waste management and floating population.
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Martos-Méndez, María José, Luis Gómez-Jacinto, Isabel Hombrados-Mendieta, Anabel Melguizo-Garín, and Iván Ruiz-Rodríguez. "Psychosocial and Sociodemographic Determinants Related to Chronic Diseases in Immigrants Residing in Spain." International Journal of Environmental Research and Public Health 19, no. 7 (March 25, 2022): 3900. http://dx.doi.org/10.3390/ijerph19073900.
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The aim of the study is to analyze the effect of the psychosocial determinants of satisfaction with social support, resilience and satisfaction with life, and the sociodemographic determinants of age, gender and length of residence on chronic diseases in immigrants living in Spain. The sample was composed of 1131 immigrants from Africa, Eastern Europe, Latin America and Asia. 47.1% were men and 52.9% were women. Most relevant results point to age as the sociodemographic variable with the highest predictive effect in the six chronic diseases analyzed. Gender, in this case female, predicts arthrosis, chronic back pain and migraine, whereas length of residence was only significant in the case of chronic allergies. Regarding psychosocial variables, resilience is a good predictor of hypertension, chronic allergies and arthrosis. However, satisfaction with social support appears to be the best predictor for chronic back pain in the regression equation, satisfaction with life being a significant variable in migraine, arthrosis, allergies and high cholesterol. Results are notably relevant for the design of preventive health programs in immigrants, as well as in ensuring their appropriate access to the health system so that their chronic diseases can be diagnosed. Given the relevance and incidence of the chronic diseases analyzed in immigrants, preventive strategies should be improved to tackle chronic diseases that can have a serious impact on immigrants’ health.
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Bresnihan, Nina, Aibhín Bray, Lorraine Fisher, Glenn Strong, Richard Millwood, and Brendan Tangney. "Parental Involvement in Computer Science Education and Computing Attitudes and Behaviours in the Home: Model and Scale Development." ACM Transactions on Computing Education 21, no. 3 (May 10, 2021): 1–24. http://dx.doi.org/10.1145/3440890.
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This work is situated in research on Parental Involvement (PI) in Computer Science (CS) Education. While the importance of PI in children's education is well established, most parents have little experience in CS and struggle to facilitate the learning of a child in the area. If PI in CS Education is to happen, then we argue that parents need support and that understanding the current behaviours and attitudes toward CS in the family context is important to discerning the form that support should take. This article therefore describes the development of an instrument to identify factors relating to parental attitudes toward and motivation for PI in CS education. Relevant variables situated in the context of parental computing behaviours and attitudes in the home were identified using a literature review and expert focus group. These include computing usage, availability, confidence, and experience. To measure these variables, a survey instrument was developed and administered to a large sample of parents ( n = 1228). Results of exploratory and confirmatory factor analysis confirm that the instrument measures five constructs, namely “Confidence,” measuring parental confidence levels with computing; “Attitude to PI”; “Motivation for PI”; and two types of “Usage”: Creation and Consumption. Results of Pearson correlation revealed significant positive relationships between confidence and both positive attitudes toward, and motivation for, PI, with linear regressions confirming that confidence was a significant predictor of both. Regression analysis also identified that creative usage was a predictor of positive attitudes to PI, and that programming experience was a predictor of attitude to, and motivation for, PI. These findings were further validated through triangulation with qualitative data from focus groups with the target population. We conclude that this understanding of the predictors of PI attitudes and motivation should inform the design of initiatives to address parental engagement in CS Education.
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Holz, Elena, Johanna Lass-Hennemann, and Tanja Michael. "Analogue PTSD Symptoms are Best Predicted by State Rumination." Journal of Experimental Psychopathology 8, no. 2 (September 25, 2016): 192–213. http://dx.doi.org/10.5127/jep.050915.
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Posttraumatic Stress Disorder (PTSD) is a severe mental disorder characterized by distressing intrusions. Since not all traumatized individuals develop PTSD, it is important to understand its underlying risk factors. So far, several psychological and physiological risk factors have been identified. However, these factors have rarely been examined together. An excellent tool to assess analogue PTSD in a prospective manner is the trauma film paradigm. This study examined relevant psychological and physiological factors in 60 healthy participants before, during and after the presentation of a “traumatic” film clip, including rumination, dissociation, anxiety, mood, cortisol and psychophysiology measures. Moreover, we assessed intrusions and administered the Impact of Event Scale – Revised (IES-R) for one week following the “trauma”. Surprisingly, the only significant predictor for both intrusion frequency and IES-R was rumination about the film (state rumination). Furthermore, intrusion distress was predicted by both state rumination and an increase in anxiety after the film clip. Our study highlights the relevance of rumination in PTSD. Further well designed clinical studies with PTSD patients should investigate these key variables prospectively to confirm our findings.
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Lin, Bingqing, Zhen Pang, and Qihua Wang. "Cluster feature selection in high-dimensional linear models." Random Matrices: Theory and Applications 07, no. 01 (January 2018): 1750015. http://dx.doi.org/10.1142/s2010326317500150.
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This paper concerns with variable screening when highly correlated variables exist in high-dimensional linear models. We propose a novel cluster feature selection (CFS) procedure based on the elastic net and linear correlation variable screening to enjoy the benefits of the two methods. When calculating the correlation between the predictor and the response, we consider highly correlated groups of predictors instead of the individual ones. This is in contrast to the usual linear correlation variable screening. Within each correlated group, we apply the elastic net to select variables and estimate their parameters. This avoids the drawback of mistakenly eliminating true relevant variables when they are highly correlated like LASSO [R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B 58 (1996) 268–288] does. After applying the CFS procedure, the maximum absolute correlation coefficient between clusters becomes smaller and any common model selection methods like sure independence screening (SIS) [J. Fan and J. Lv, Sure independence screening for ultrahigh dimensional feature space, J. R. Stat. Soc. Ser. B 70 (2008) 849–911] or LASSO can be applied to improve the results. Extensive numerical examples including pure simulation examples and semi-real examples are conducted to show the good performances of our procedure.
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Stelmańska, Karolina, Zbigniew Paluch, Marta Twardokęs, Katarzyna Ura-Sabat, Hanna Frelich, Michał Szlęzak, and Maciej Misiołek. "Evaluation of morphological relations between lower craniofacial skeletal structures and dimensions of upper respiratory tract in skeletal Classes I and III." Journal of Stomatology 69, no. 6 (December 31, 2016): 647–58. http://dx.doi.org/10.5604/00114553.1230585.
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Introduction. Review of the literature indicates the relationship between upper airways patency and lower jaw morphology. Aim of the study. To design multidimensional models to describe morphological relations of the linear and angular dimensions of hard tissues of the lower facial skeleton and the cervical spine (independent variables) with the linear dimensions of the upper respiratory tract (dependent variables). The obtained models took into consideration the age, gender and the skeletal Class I and III of the evaluated individuals. Material and method. The patients represented two skeletal classes: I (n = 97; 42.4%) and III (n = 53; 23.1%). Prior to orthodontic treatment, lateral cephalometric radiography (LCR) was performed in all patients. The obtained cephalometric measurements were evaluated statistically. Results. The statistical analysis pointed to significant differences between variables of the URT and dimensions of the facial skeleton and the cervical spine. It also revealed significant differences between variables of the URT and dimensions of the facial skeleton and the cervical spine. The independent variable Co-Gn emerged as an important predictor in regression of the nasopharynx. The anteroinferior height of the face and mandibular inclination were the factors relevant for oropharynx. Variables of the cervical spine were significant predictors in laryngopharyngeal models. Conclusions. The results illustrating morphological interrelations between the lower face and dimensions of the URT air space may prove helpful in planning orthodontic treatment, with or without teeth extraction, as well as orthognathic surgeries.
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Mohamad, Dadang, Kumares C. Sinha, Thomas Kuczek, and Charles F. Scholer. "Annual Average Daily Traffic Prediction Model for County Roads." Transportation Research Record: Journal of the Transportation Research Board 1617, no. 1 (January 1998): 69–77. http://dx.doi.org/10.3141/1617-10.
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A traffic prediction model that incorporates relevant demographic variables for county roads was developed. Field traffic data were collected from 40 out of 92 counties in Indiana. The selection of a county was based on population, state highway mileage, per capita income, and the presence of interstate highways. Three to four automatic traffic counters were installed in each selected county. Most counters installed on the selected road sections were based on the standard 48-hour traffic counts. Then, the obtained average daily traffic was converted to annual average daily traffic by means of adjustment factors. Multiple regression analysis was conducted to develop the model. There were quantitative and qualitative predictor variables used in the model development. To validate the developed model, additional field traffic data were collected from eight randomly selected counties. The accuracy measures of the validation showed the high accuracy of the model. The statistical analyses also found that the independent variables employed in the model were statistically significant. The number of independent variables included in the model was kept to a minimum.